*703*
*49*
*83MB*

*English*
*Pages [860]*
*Year 2015*

*Table of contents : Vehicle, Mechanical and Electrical Engineering Preface, Committees and Sponsors Table of Contents Chapter 1: Vehicle Engineering and Design Reverse Optimization Algorithm of CVT‘s Energy Economy Hybrid Mobile Power Supply Using PEMFC and Lithium Battery The Research and Design on the Electric Vehicles’ Centrifugal Automatic Transmission Design on a Collision Warning and Vehicle-Headlights Intelligent Switching System Study on the Closed-Loop Control Strategy of Voltage for Pure Electric Vehicle in Limp Mode Performance Analysis of a Pure Electric Light Off-Road Vehicle Based on the Cruise FSAE Race Car Engine Options on the Impact of Vehicle Design Based on CATIA Car Side Welding Fixture Design and Research Chapter 2: Traffic and Transport Engineering, Vechicle and Road Safety Effects of Traffic Calming Measures on Vehicle Speed Control and Road Safety Violation Behavior Analysis of Pedestrian and Non-Motorized Vehicle The Study of Traffic Rules Based on Cellular Automata Traffic Wave Model The Overall Structure of IOV and the Research of the Unsolved Key Technology of IOV in Intelligent Transportation System Vehicle Routing Problem Based on Heuristic Artificial Fish School Algorithm An Improved Cellular Automaton Model of Traffic Regulations and the Effect of Deceleration Probability Smoke Management System Design for Airport Terminal Chapter 3: Mechanical and Dynamical Principles and Design, Machinery and Manufacturing Engineering Influence of Guide Vane Wrap Angle Key Design Parameters on Hydraulic Performance of Nuclear Reactor Coolant Pump Numerical Investigation on the Flow in Tip Clearance of the Last Three Stages of Industrial Steam Turbine A Simplified Model of Semi-Open Impeller Stage and Analysis of its Effects on the Transient Flow Dynamic Analysis and Optimization of Pivot Points of Telescopic Jib Based on Genetic Algorithm Development of Testing Machine for Measuring Unsteady State EHL Film under Heavy Load Finite Element Analysis of Z-Shaped Pipe in the Directly Buried Heat-Supply Pipeline with Large Diameter Multiple Weibull Statistical Model of Random Censored Data of NC Machine Tools and Optimal Estimation of Parameters Controllable of Underwater Towing Tension Passive Compensation Device Design Research Engineered-Based Machining Parameters Analysis for Aircraft Structural Parts Parametric Design of Deep Groove Ball Bearing Based on Pro/Program and Family Table Parameter Study of Opposite Tape-Spring Flexure Hinge Research on the Methods for Common-Rail Pipe Holes Abrasive Flow Machining Intermediate Slabs and Optimization in Forging Process of Balance Elbow by FEM Analysis of Hydroelectric Unit’s Upper Bracket Based on Test and FEM The Optimization Method of Tailor Welded Blank Forming Process Based on Numerical Simulation Research on Gear Mesh Misalignments under the System Deformation Applying Multi-Objective Particle Swarm Optimization to Maintenance Scheduling for CNC Machine Tools On the Axisymmetrical Dissemination of Glycerine Driven by Shock Wave Static Stiffness Analysis of High Stiffness Rotary Hinge Locking Mechanism for SAR Antenna Research on the Variable Ratio Gear Modeling and Transmission Ratio Test Method of Rack and Pinion Variable Ratio Steering Gear Calculative Method for Critical Condition of Fire Liftoff Caused by Man-Made Interfering Jet Flow Trajectory Reconstruction Using Paper Target Experiment Based on UKF Performance Analysis of a Novel Compression Refrigeration System Numerical Study of Flow and Heat Transfer of Heat Exchanger with Louver Baffles Control and Analysis on Shot Peening Process to Aeropropeller Pitch Change Knob A New PANS Model for Unsteady Separated Flow Simulations FSAE Racing Gear Selection and Shifting Agencies Choose to Discuss the Impact of Car Drivers Modeling and Simulation of a New Type Intelligent Gas Pressure Regulator Calculation on Fracture Parameter of Model I Crack Tip Based on ANSYS Three-Dimensional Numerical Simulation of the Flow around Two Side-by-Side Cylinders of Different Diameters Chapter 4: System Modeling and Algorithms for Intelligent Automation and Control Systems Particle Swarm Optimization of PIλDμ Control of Heating Furnace Temperature Control System Self-Scheduled and Robust Control for Tilt Rotor Aircraft Mathematical Model of Obstacle Avoidance Shortest Time Path for Robot Synchronization Control of Chaotic Systems with Different Orders Research on Carrier-Attacker Control before Launching Air-Ground Guidance Weapons Control of Channel Power Compensation Based on Genetic Algorithm to Impairment Aware in Optical Network Study on Parametric Reverse Modeling Chaos Synchronization of Systems via Accelerated Factors Cooperative Output Regulation for a Class of Nonlinear Uncertain Multi-Agent Systems Using the Backstepping Method Simulation of Valve Position Feedback Mechanism Based on Pro/E and ADAMS Study on Single-Phase Grid-Connected Inverter Based on PI Control Intelligent Control of Time-Delayed System Applying LESO for Disturbance Rejection of Space Station Developing Fuzzy Control Irrigate System Based on PLC Greenhouse Moisture and Temperature Fuzzy Control System and PLC Program Design Anti-Synchronization Control of the Complex Lu System The Design of the Tiny-Skin Transplanter’s Pneumatic Control System An Improved Purging Control Algorithm for Small Power PEMFC The Research of Stamping Springback Compensation Algorithm Based on the Improved Fourier Transform Design and Simulation about Linkage of Caterpillar Beach Cleaner’s Graps Research on Big Data Consistency Algorithm of Multi-Sensor Fusion The Research of Loyalty Model Based on the PN (Petri Net) Algorithm The Research of Rapid Construction Method of Kinematics Coordinate System from a Kind of Mobile Robot Study on the Energy Management System of Electric Vehicle Charging Station Based on Multi-Agent System Research on Drive Control and Safety Management Strategy for Electric Vehicle Study on Control Strategy for Electric Starting up of Pure Electric Vehicle Study on the Control Strategy of Slope Starting for Pure Electric Vehicle Research on the Design and Manufacture of Steam Cleaning Machine Based on PLC The Design Method for Sequence Function Chart in Drilling Machine Control System The Control Design of the Chain Magazine Test Bench Design of Foil-Making Machine Based on Motion Controller Optimization Research on the Circuit Breaker Instantaneous Characteristic Experiment Based on Neural Network Analysis on Composite Action Control Strategy of Separate Meter-In Separate Meter-Out Control System A Design and Implementation for the Auto-Booting of Baseband Board with ARM, DSP and FPGA Chapter 5: System Test, Diagnosis, Detection and Monitoring, Instrumentation and Measurement, Optimization and Algorithms, Numerical Methods and Simulation Reliability Evaluation for Distribution System Considering the Access of Distributed Generations Transformer Fault Diagnosis Based on Online Sequential Extreme Learning Machine Analysis of a Nonlinear System with a Random Parameter The Research on Flow Calculation Method of the Ball Solenoid Valve Reflection Plane Precision Measuring Methods Research of Shipborne Measurement and Control Antenna Echo Doppler Fidelity’s Verification of Radar Simulator About Several Kinds of Probe on the Impact of Different Failure Modes on Inspection Speed Analysis Design of Light Moisture Tester and its Application in Tiefa Coal Industry Group Numerical Analysis of Buried Heating Branch Junctions Fault Prediction of Pitch Actuator for Wind Turbines Vivid Algorithm of Scene Simulation for URAV A New INS/VNS Integrated Navigation Model for Planetary Rovers Optimum Sensor Array for Passive Localization from Time Differences of Arrival Smoke Detection Alarm System Based on NRF401 and FPGA Road Flatness Detection Using Permutation Entropy (PE) Analysis of Natural Pollution Deposit on Optic Sensor for Monitoring of Insulator Contamination Comprehensive Evaluation of the Designing Scheme of AUV Platform Performance by AHP Method Data Prediction Algorithm in Wireless Sensor Networks for Oil and Gas Pipeline Monitoring The Research on Central Monitoring System for Wind Farm Ultrasonic Transceiver-Based Structural Deformation Monitoring with Location Ability Lunar Soft - Landing Trajectory of Mechanics Optimization Based on the Improved Ant Colony Algorithm Chaos Research of Asymmetric System Based on Melnikov Method The Design and Experiment of Levelness-Adjustable Clamping Device for Cable Gland Large-Scale Virtual Simulation of the Solar System Based on Java3D The Effect of Different Optimization Methods on Robustness for Robust Optimization Design Modeling and Simulation Research of Lidar Tomography Imaging Researching on Characteristics of Rayleigh Waves Evaluation Framework and Method of the Intelligent Behaviors of Unmanned Ground Vehicles Based on AHP Scheme Fracture Research on Preheating Installation of Directly Buried Heating Return Water Pipe with Large Diameter The Electric Monitoring System Application Based on ZigBee Wireless Technology Research on Magnetometer Positioning Technology Based on a Variable Magnetic Source A Method of Order Determination for ARX and ARMA Models Based on Nonnegative Garrote Research on Jamming Resource Scheduling Based on Multi-Target and Fuzzy Multi-Stage The Design and Implementation of a Certain Type of Information Machine Simulator Based on ARM PSO Based Feature Extraction Method for Analog Circuits Fault Information A Precision Resistance Generator for the Calibration of RTD-Based Temperature Instruments Research on Identification of Induction Motor Parameters Based on Nameplate Data Research on Wireless Temperature Measurement System for Electrical Equipment The Design of Mobile Phone Multimeter Based on the Android Platform Application of the Bat Algorithm to Optimize the BP Neural Network An Algorithm to Find the First K Spanning Trees with Minimum Weights The Multi-Site Joint Operation Method Based on Discharge Gate Ant Colony Algorithm A Novel and Improved Apriori Algorithm Algorithm for Computing Attribute Reduction Based on Radix Sort of Optimized Linked List Structure Chapter 6: Electrical and Electronic Technology, Power System Engineering Impact of DLC on Distribution System Reliability Doherty Power Amplifier with Dynamic Power Dividing Network for Enhanced Efficiency The Design of the Minimum CPU System of TMS320VC54x Research on Simulation of Electronic-Protection Circuit of Chopper Cascade Speed Control System A Kind of Electric-Corona-Prevent Paint Research on High-Power, High-Speed Laser Modulation and Enlarge Experiment PV Grid-Connected Inverter Islanding Detection Algorithm Improvements Summary of Research Status of Active Power Filter Based on Class D Amplifier Magnetic Resonance Wireless Transmission Technology Application Research of Electrical Automation Technology for Power Plant Research on the Key Factors of Mis-Operations and Correct Operations of the Residual Current Operated Protective Device Research and Application of UPS-CAN Protocol in Parallel System Selective Harmonic Elimination PWM Technique with Walsh Functions for Nine-Level Cascade Inverter An Active Equalization Technique Based on Flyback Converter Topology Development and Research of MEMS Capacitive Touch Sensing Principle and Application in the Car Microcomputer Interface Experimental System Design The Model of Estimating MTL Crosstalk in Inhomogeneous Medium Research on Four Water-Filling Power Distribution Schemes in MU-MIMO System Design of Electronic Control System for HCCI Optical Engine and Optical Testing Chapter 7: Communication for Vehicles and Transportation, Signal Processing Discussion on the Technology Application of OFDM in the Ad-Hoc Network of Unmanned Aerial Vehicle (UAV) A Novel Anti-Collision Algorithm of Tags in RFID System Design Method of an Intermediate Transcoding Structure Based on XML An Effective Multiple-Receiver SAS Range Doppler Algorithm Based on the Modified Range History Approximation The Study on DFS Algorithm of Unmanned Aerial Vehicle Platform Communications Relay System Method of Common View Based on Digital Satellite Television Modulation Type Recognition of OFDM Signals Based on EMD Network-Coding-Based Mutually Cooperative Transmission in Deep Space Communications Research on Near Space Optical Communication Transmitting and Receiving Technology Chapter 8: Information Technology and Networks Applications Software Defined Networking: A New Trend of Networking Integrated Avionics Network Dynamics Analysis High Performance Start Node of Edge Ordering for Infrastructure Networks The Design of the Power Energy Management System Based on Wireless Sensor Network The Applications of Information Visualization in Power Systems The Application of Communication Network in Distribution Automation The Application of Distribution GIS in Regional Distribution Network Multi-Objective Programming Model for Joint Scheduling of Ships in the Complex Water Network Research on Energy Saving in Cellular Networks Based on Dynamic Load Balancing Application of Process Migration in Intelligent Router Based on BLCR Channel Assignment Algorithm in Centralized WLAN Integrity and Privacy Preserving Data Aggregation Algorithm for WSNs A Study of the Model which Can Improve LEACH Protocol Power Control Optimization for Position Tracking in Vehicle Network An Optimized Design of Safety Messages’ Communication Sub-Layer in Vehicle Ad-Hoc Network Visualizing Interrupts and Replication with Timer Chapter 9: Recognition, Video and Image Processing An Effective Program Clustering Algorithm for TV Recommendation System The Application of Gaussian Mixture Modeling in Traffic Flow Videos Research on Locality Preserving Discriminant Projection Algorithm Based on Gabor for Face Expression Recognition Design and Algorithm Optimization of P2P Mobile Monitoring Network-Based Facial Recognition System Rotation Moment Invariant Feature Extraction Techniques for Image Matching The Application of Improved PSO Algorithm in PMMW Image OSTU Threshold Segmentation A Robust Line Filter for Automatic X-Ray/CT Image Segmentation The Research Based on H.264 Embedded Video Systems of Optimized Compression Algorithm Chapter 10: Materials for Vehicles and Transportation, Civil Constructions, Fuel Cells and Energy Materials Effect of Ce on Secondary Extrusion Deformation Microstructure of ZM21 Alloy Effects of Zn on Microstructure and Mechanical Properties of Mg-6.0Zn-0.6Zr-1.0Y Magnesium Alloys Influence of Laminate Scheme on Design of Continuous Fiber Reinforced Composite Turbofan Engine Shaft Analysis of Tunnel Running through Bridge Pile Foundation Based on the MIDAS/GTS Influences of Ground Reflection on the Elevated Box Bridge Structure Noise Acoustic Properties of Magnetorheological Fluids under Magnetic Fields Synthesis and Photoelectric Properties of Donor-Acceptor-Donor Molecule Containing Perylene Diimide Preparation and Properties of SiO2-P2O5-TiO2 Membrane for Fuel Cell Electrolytes Analysis of Resistance of Current Collector in Packing-Type Microbial Fuel Cell Vibrating Rechargeable Battery Design Research Based on the Two Kinds of Charging Ways Collision Simulation and Analysis of Energy-Absorbing Box for Vehicle Keywords Index Authors Index*

Vehicle, Mechanical and Electrical Engineering

Edited by Zhigang Fang Jianjun Xu Pin Wang

Vehicle, Mechanical and Electrical Engineering

Selected, peer reviewed papers from the 2014 International Conference on Vehicle, Mechanical and Electrical Engineering (ICVMEE 2014), November 29-30, 2014, Wuhan, China

Edited by

Zhigang Fang, Jianjun Xu and Pin Wang

Copyright 2015 Trans Tech Publications Ltd, Switzerland All rights reserved. No part of the contents of this publication may be reproduced or transmitted in any form or by any means without the written permission of the publisher. Trans Tech Publications Ltd Churerstrasse 20 CH-8808 Pfaffikon Switzerland http://www.ttp.net

Volume 721 of Applied Mechanics and Materials ISSN print 1660-9336 ISSN cd 1660-9336 ISSN web 1662-7482

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Preface Dear Distinguished Delegates and Guests, The organizing Committee of ICVMEE 2014 warmly welcomes you to join the International Conference on Vehicle, Mechanical and Electrical Engineering (ICVMEE 2014: www.icvmee.org/), held on November 29-30, 2014. The aim of ICVMEE 2014 is to provide a platform for researchers, engineers, and academicians, as well as industrial professionals, to present their research results and development activities in vehicle, mechanical, and electrical engineering, and its applied technology. There are six themes is this proceeding: Vehicle Engineering, Electric and Hybrid Vehicle, Transportation Engineering, Mechanical Principles and Design, Machining, Manufacturing and Molding, System Modeling, Algorithms, Intelligent Automation and Control Systems System Test and Diagnosis, Signal and Image Processing, Computational Mathematics, Information Technology and Networks Applications, Internet and Communications Technologies, Electrical and Electronic Technology, Power System and Energy Engineering. It provides opportunities for the delegates to exchange new ideas and application experiences, to establish business or research relations and to find global partners for future collaboration. Hopefully, all participants and other interested readers benefit scientifically from the proceedings and also find it stimulating in the process. With our warmest regards, Zhigang Fang Conference Organizing Chair Wuhan, China

Committees and Sponsors Committee Chair Dr. Zhigang Fang, Wuhan University of Technology, China

Co-Chairmen Prof. Jianjun Xu, Northeast Petroleum University, China Prof. Pin Wang, Guangxi college of Education, China Dr. Bin Wang, Wuhan University of Technology, China Dr. Can Yang, Wuhan University of Technology, China

Committee Prof. Lu Yu, Wuhan University, China Dr. Xue Ding, YunNan Normal University, China Dr. Mingming Li, Kunming University of Science and Technology, China Dr. Huanhuan You, Zhicheng Conference Services Ltd., China Prof. Ya Ma, Zhicheng Conference Services Ltd., China Dr. Jun Shi, Zhicheng Times Culture Development Co., Ltd., China Dr. Li Xu, Zhicheng Times Culture Development Co., Ltd., China Prof. Hao Chen, Shanghai University of Engineering Science, China Dr. Haifeng Fang, Jiangsu University of Science and Technology, China Dr. Jun Wang, Nanjing Forestry University, China Dr. lan Song, East China Jiaotong University, China Prof. Limin Shao, Agricultural University of Hebei, China Dr. Dongmin Li, Shandong University of Science and Technology, China Prof. Tao Cui, Guizhou Institute of Technology, China

Sponsors Wuhan University of Technology Zhicheng Conference Services Ltd. Zhicheng Times Culture Development Co., Ltd. Research Center of Engineering and Science (RCES) Communication Center for Science and Technology Researchers (CCSTR)

Table of Contents Preface, Committees and Sponsors

Chapter 1: Vehicle Engineering and Design Reverse Optimization Algorithm of CVT‘s Energy Economy M. Zhou Hybrid Mobile Power Supply Using PEMFC and Lithium Battery N.J. Song, W. Qi, J. Fang, W. Liu and D. Xiao The Research and Design on the Electric Vehicles’ Centrifugal Automatic Transmission Y. Fan Design on a Collision Warning and Vehicle-Headlights Intelligent Switching System Y. Fan and R.K. Deng Study on the Closed-Loop Control Strategy of Voltage for Pure Electric Vehicle in Limp Mode L. Chu, C. Guo, Y. Yang, Z.C. Fu and Y.J. Zhang Performance Analysis of a Pure Electric Light Off-Road Vehicle Based on the Cruise J.J. Yang FSAE Race Car Engine Options on the Impact of Vehicle Design H. Gong, Y.P. Li and J. Chao Based on CATIA Car Side Welding Fixture Design and Research Z.H. Zhu and H. Cao

3 7 12 16 20 24 28 32

Chapter 2: Traffic and Transport Engineering, Vechicle and Road Safety Effects of Traffic Calming Measures on Vehicle Speed Control and Road Safety Y.B. Jiang, L.H. Jiang and Y.Q. Qin Violation Behavior Analysis of Pedestrian and Non-Motorized Vehicle P. Hao and Q.Y. Hao The Study of Traffic Rules Based on Cellular Automata Traffic Wave Model W.B. Liu and Y.Q. Yang The Overall Structure of IOV and the Research of the Unsolved Key Technology of IOV in Intelligent Transportation System F. Li, Y. Jian and R. Zhao Vehicle Routing Problem Based on Heuristic Artificial Fish School Algorithm L. Qin, Y.Q. Li and K. Zhou An Improved Cellular Automaton Model of Traffic Regulations and the Effect of Deceleration Probability L.C. Han Smoke Management System Design for Airport Terminal L.B. Wang and S.P. Zhang

39 43 47 52 56 62 66

Chapter 3: Mechanical and Dynamical Principles and Design, Machinery and Manufacturing Engineering Influence of Guide Vane Wrap Angle Key Design Parameters on Hydraulic Performance of Nuclear Reactor Coolant Pump W.N. Jin, R. Xie, M.T. Hao and X.F. Wang Numerical Investigation on the Flow in Tip Clearance of the Last Three Stages of Industrial Steam Turbine Z.J. Sha, R. Xie, X.F. Wang, X.D. Ding and Y.F. Sui

73 78

b

Vehicle, Mechanical and Electrical Engineering

A Simplified Model of Semi-Open Impeller Stage and Analysis of its Effects on the Transient Flow M.T. Hao, R. Xie, L. Guan and Z.F. Lu Dynamic Analysis and Optimization of Pivot Points of Telescopic Jib Based on Genetic Algorithm T. Liu, W.H. Wang, R.W. Yuan and F. Lu Development of Testing Machine for Measuring Unsteady State EHL Film under Heavy Load N.M. Miao, J.N. Ding and J.C. Yang Finite Element Analysis of Z-Shaped Pipe in the Directly Buried Heat-Supply Pipeline with Large Diameter M.Q. Li, F. Wang, G.W. Wang and Y.G. Lei Multiple Weibull Statistical Model of Random Censored Data of NC Machine Tools and Optimal Estimation of Parameters T. Fu, D.Z. Wang and Q.Z. Gong Controllable of Underwater Towing Tension Passive Compensation Device Design Research J. Meng, Z.X. Chen, T. Jiang and Z.Q. Xu Engineered-Based Machining Parameters Analysis for Aircraft Structural Parts F. Zhu, X.F. Huang and L.J. Meng Parametric Design of Deep Groove Ball Bearing Based on Pro/Program and Family Table N.M. Miao Parameter Study of Opposite Tape-Spring Flexure Hinge H. Yang, R.Q. Liu, Y. Wang and J.G. Tao Research on the Methods for Common-Rail Pipe Holes Abrasive Flow Machining L.F. Zhu, K. Wang, H. Wu, D. Xiu and L.Z. Sun Intermediate Slabs and Optimization in Forging Process of Balance Elbow by FEM B.J. Xiong, K.L. Wang, J. Fang and Y. Huang Analysis of Hydroelectric Unit’s Upper Bracket Based on Test and FEM M.M. Xia and Y.G. Li The Optimization Method of Tailor Welded Blank Forming Process Based on Numerical Simulation Y. Gan, J.X. Li, Y. Chen and H.Z. Li Research on Gear Mesh Misalignments under the System Deformation X.H. Deng Applying Multi-Objective Particle Swarm Optimization to Maintenance Scheduling for CNC Machine Tools Z.H. Yao and M. Zhou On the Axisymmetrical Dissemination of Glycerine Driven by Shock Wave L. Yang, X.L. Yang and Z.W. Huang Static Stiffness Analysis of High Stiffness Rotary Hinge Locking Mechanism for SAR Antenna Q. Cong, Y. Wang, C.F. Zhang, R.Q. Liu and J. Wang Research on the Variable Ratio Gear Modeling and Transmission Ratio Test Method of Rack and Pinion Variable Ratio Steering Gear Z.R. Niu, G.Y. Li, S. Zeng, T.L. Yan, S.Y. Zhang and W. Wang Calculative Method for Critical Condition of Fire Liftoff Caused by Man-Made Interfering Jet Flow X.G. Jiang, W. Chi and L.G. Jin Trajectory Reconstruction Using Paper Target Experiment Based on UKF L.L. An and L.M. Wang Performance Analysis of a Novel Compression Refrigeration System Z.W. Cheng Numerical Study of Flow and Heat Transfer of Heat Exchanger with Louver Baffles H. Lai Control and Analysis on Shot Peening Process to Aeropropeller Pitch Change Knob Z.P. Fan and C. Zhang A New PANS Model for Unsteady Separated Flow Simulations D.H. Luo, C. Yan, W.L. Zheng and W. Yuan

82 87 91 96 100 104 109 113 118 122 127 131 135 140 144 149 153 157 162 166 170 174 178 182

Applied Mechanics and Materials Vol. 721

FSAE Racing Gear Selection and Shifting Agencies Choose to Discuss the Impact of Car Drivers J. Chao and H. Gong Modeling and Simulation of a New Type Intelligent Gas Pressure Regulator Y.T. Wang and J.G. Ma Calculation on Fracture Parameter of Model I Crack Tip Based on ANSYS K. Song and B.C. Yang Three-Dimensional Numerical Simulation of the Flow around Two Side-by-Side Cylinders of Different Diameters Z.X. Bi and Z.H. Zhu

c

187 191 195 199

Chapter 4: System Modeling and Algorithms for Intelligent Automation and Control Systems Particle Swarm Optimization of PIλDμ Control of Heating Furnace Temperature Control System P.G. Wang, L. Zhang and X.P. Zong Self-Scheduled and Robust Control for Tilt Rotor Aircraft Y.Y. Cao, X.M. Wang and C. Peng Mathematical Model of Obstacle Avoidance Shortest Time Path for Robot L.L. Duan, P. Wang and N. Ruan Synchronization Control of Chaotic Systems with Different Orders Y.F. Yang Research on Carrier-Attacker Control before Launching Air-Ground Guidance Weapons H.Y. Zhang, L. Zhang, Z.Q. Liu, Y.G. Ji and W.D. Zhang Control of Channel Power Compensation Based on Genetic Algorithm to Impairment Aware in Optical Network D.Y. Zhao, K.P. Long, Y.C. Zheng, Z.Y. Du, J.F. Shi and S. Cheng Study on Parametric Reverse Modeling S.X. Hu Chaos Synchronization of Systems via Accelerated Factors J.P. Jia Cooperative Output Regulation for a Class of Nonlinear Uncertain Multi-Agent Systems Using the Backstepping Method D.C. Huang, X.K. Wang and Y.F. Niu Simulation of Valve Position Feedback Mechanism Based on Pro/E and ADAMS Y.J. Fan, F. Li and H.T. Zhao Study on Single-Phase Grid-Connected Inverter Based on PI Control L.J. Hou, C.Q. Zhu, Y.F. Zhao and X.F. Yan Intelligent Control of Time-Delayed System C.J. Zhang, H.P. Hu and Y.Q. Zhang Applying LESO for Disturbance Rejection of Space Station P. Liu and W.J. Sun Developing Fuzzy Control Irrigate System Based on PLC L. Xiao, L. Li and X.L. Wu Greenhouse Moisture and Temperature Fuzzy Control System and PLC Program Design M.Q. Huang, J.H. Ke and X.L. Wu Anti-Synchronization Control of the Complex Lu System F.D. Zhang The Design of the Tiny-Skin Transplanter’s Pneumatic Control System T.T. Deng An Improved Purging Control Algorithm for Small Power PEMFC J. Fang, W. Qi and D. Xiao The Research of Stamping Springback Compensation Algorithm Based on the Improved Fourier Transform W.J. Liu, Z.Y. Liang, X.Y. Zhu and X.Y. Zhang

205 210 214 218 222 226 230 234 238 244 249 253 257 261 265 269 273 277 281

d

Vehicle, Mechanical and Electrical Engineering

Design and Simulation about Linkage of Caterpillar Beach Cleaner’s Graps Y.K. Zheng, X.H. Ge and L.G. Ouyang Research on Big Data Consistency Algorithm of Multi-Sensor Fusion J.X. Duan and H.S. Wang The Research of Loyalty Model Based on the PN (Petri Net) Algorithm R. Xu The Research of Rapid Construction Method of Kinematics Coordinate System from a Kind of Mobile Robot C.H. Lu and M.J. Song Study on the Energy Management System of Electric Vehicle Charging Station Based on Multi-Agent System Y.H. Tao, S.J. Wang and X.S. Zhang Research on Drive Control and Safety Management Strategy for Electric Vehicle H.W. Liu, L.J. Wang and J.H. Tian Study on Control Strategy for Electric Starting up of Pure Electric Vehicle L. Chu, C. Guo, P.Z. Zhang, Z.C. Fu and Y.J. Zhang Study on the Control Strategy of Slope Starting for Pure Electric Vehicle L. Chu, C. Guo, P.Z. Zhang, Z.C. Fu and Y.J. Zhang Research on the Design and Manufacture of Steam Cleaning Machine Based on PLC D.M. Zhang and S.B. Li The Design Method for Sequence Function Chart in Drilling Machine Control System S. Jin The Control Design of the Chain Magazine Test Bench M.Z. Rao Design of Foil-Making Machine Based on Motion Controller D.H. Zhang and X.Q. Wu Optimization Research on the Circuit Breaker Instantaneous Characteristic Experiment Based on Neural Network X.J. Jia Analysis on Composite Action Control Strategy of Separate Meter-In Separate Meter-Out Control System W.R. Wu and J.C. Yao A Design and Implementation for the Auto-Booting of Baseband Board with ARM, DSP and FPGA L.W. Hu

285 291 295 299 303 308 313 317 322 326 330 334 338 342 349

Chapter 5: System Test, Diagnosis, Detection and Monitoring, Instrumentation and Measurement, Optimization and Algorithms, Numerical Methods and Simulation Reliability Evaluation for Distribution System Considering the Access of Distributed Generations H.S. Zhao, S. Chen, Y.Y. Wang and Y. Wang Transformer Fault Diagnosis Based on Online Sequential Extreme Learning Machine L.L. Wang, F. Pei and Y.L. Zhu Analysis of a Nonlinear System with a Random Parameter H.G. Dang, X.Y. Yang and W.S. He The Research on Flow Calculation Method of the Ball Solenoid Valve Y. Yang, L. Chu, D. Fan and Y.T. Huang Reflection Plane Precision Measuring Methods Research of Shipborne Measurement and Control Antenna Y.J. Sun, X.L. Lu, H.S. Jin and J.C. Yu Echo Doppler Fidelity’s Verification of Radar Simulator Y.H. Xu, F.Z. Wang, C.C. Chen and D.Z. Zeng About Several Kinds of Probe on the Impact of Different Failure Modes on Inspection Speed Analysis Y.K. Ji and F.N. Liu

355 360 366 370 374 378 382

Applied Mechanics and Materials Vol. 721

Design of Light Moisture Tester and its Application in Tiefa Coal Industry Group H.L. Su Numerical Analysis of Buried Heating Branch Junctions Y.L. Liu, F. Wang, L.P. Li and G.W. Wang Fault Prediction of Pitch Actuator for Wind Turbines H.S. Zhao, S.S. Lian and L. Shao Vivid Algorithm of Scene Simulation for URAV G.D. Jin, L.B. Lu and X.F. Zhu A New INS/VNS Integrated Navigation Model for Planetary Rovers Y.Z. Xu and X.L. Ning Optimum Sensor Array for Passive Localization from Time Differences of Arrival C. Zhou, G.M. Huang and J. Gao Smoke Detection Alarm System Based on NRF401 and FPGA T. Wang and W.B. Liu Road Flatness Detection Using Permutation Entropy (PE) Q.C. Wang, W.Q. Song and J.K. Liang Analysis of Natural Pollution Deposit on Optic Sensor for Monitoring of Insulator Contamination J. Xu and Q. Wang Comprehensive Evaluation of the Designing Scheme of AUV Platform Performance by AHP Method T.T. Wang, S.L. Yang, Y. Chen, J. Li and Q. Yu Data Prediction Algorithm in Wireless Sensor Networks for Oil and Gas Pipeline Monitoring H.P. Yu and M. Guo The Research on Central Monitoring System for Wind Farm Y. Tang, Z. Yang, C.C. Wang and Y.Q. Zhang Ultrasonic Transceiver-Based Structural Deformation Monitoring with Location Ability W. Zheng, C.X. Wu and R.R. Cui Lunar Soft - Landing Trajectory of Mechanics Optimization Based on the Improved Ant Colony Algorithm M.F. Qu Chaos Research of Asymmetric System Based on Melnikov Method A.K. Hu, Y.C. Liu, F.L. Han and C.H. Wang The Design and Experiment of Levelness-Adjustable Clamping Device for Cable Gland Y. Ke, Z.H. Jiang and S.G. Li Large-Scale Virtual Simulation of the Solar System Based on Java3D X.L. Chen, J.X. Chen and H. Liu The Effect of Different Optimization Methods on Robustness for Robust Optimization Design T. Fu, Q.Z. Gong and D.Z. Wang Modeling and Simulation Research of Lidar Tomography Imaging F.Q. Qu, C. Shu and J.H. Tu Researching on Characteristics of Rayleigh Waves X.F. Zhu and B. Yan Evaluation Framework and Method of the Intelligent Behaviors of Unmanned Ground Vehicles Based on AHP Scheme X. Zhang, Y.A. Zhao, L. Gao and D.H. Hao Fracture Research on Preheating Installation of Directly Buried Heating Return Water Pipe with Large Diameter Y. Fu, F. Wang, G.W. Wang and Y.G. Lei The Electric Monitoring System Application Based on ZigBee Wireless Technology Y. Li, X.D. Ding and Y.T. Li Research on Magnetometer Positioning Technology Based on a Variable Magnetic Source S. Zhang, D.M. Liu, H. Zhang and S.N. Zhou A Method of Order Determination for ARX and ARMA Models Based on Nonnegative Garrote G.D. Jin, L.B. Lu and X.F. Zhu

e

386 393 397 402 406 411 416 420 424 428 434 438 442 446 450 455 459 464 468 472 476 481 486 490 496

f

Vehicle, Mechanical and Electrical Engineering

Research on Jamming Resource Scheduling Based on Multi-Target and Fuzzy Multi-Stage F.L. Wang, S.J. Rao, N. Jiang and D. Wang The Design and Implementation of a Certain Type of Information Machine Simulator Based on ARM J.H. Tu, Z.C. Shao, X.L. Tan and F.Q. Qu PSO Based Feature Extraction Method for Analog Circuits Fault Information H. Liu and S.Z. Sun A Precision Resistance Generator for the Calibration of RTD-Based Temperature Instruments P.H. Chen, L.F. Guo, G.B. Lu and C.X. Jin Research on Identification of Induction Motor Parameters Based on Nameplate Data X.W. Wang, Z.Y. Zhang and Y.J. Lin Research on Wireless Temperature Measurement System for Electrical Equipment X.L. Wang, Y.G. Nian and D.D. Cai The Design of Mobile Phone Multimeter Based on the Android Platform Z.T. Cheng, M. Yang and W.P. Wang Application of the Bat Algorithm to Optimize the BP Neural Network H.H. Xiao and Y.M. Duan An Algorithm to Find the First K Spanning Trees with Minimum Weights G.M. Sun, H. Li and P. Wang The Multi-Site Joint Operation Method Based on Discharge Gate Ant Colony Algorithm C.C. Liu, Y.C. He and C.P. An A Novel and Improved Apriori Algorithm D.J. Gu and L. Xia Algorithm for Computing Attribute Reduction Based on Radix Sort of Optimized Linked List Structure L. Zhang and G.J. Ding

500 505 509 513 517 523 527 531 535 539 543 547

Chapter 6: Electrical and Electronic Technology, Power System Engineering Impact of DLC on Distribution System Reliability H.S. Zhao, Y.Y. Wang, S. Chen and Y. Wang Doherty Power Amplifier with Dynamic Power Dividing Network for Enhanced Efficiency W. Xi, Y. Shi, S.L. Yang and J. Li The Design of the Minimum CPU System of TMS320VC54x A.L. Qu, C.L. Ma and B.B. Qu Research on Simulation of Electronic-Protection Circuit of Chopper Cascade Speed Control System Z.Y. Zhang, X.W. Wang and Y.G. Ma A Kind of Electric-Corona-Prevent Paint X.Y. Zhao Research on High-Power, High-Speed Laser Modulation and Enlarge Experiment H. Liu and W.D. Zhan PV Grid-Connected Inverter Islanding Detection Algorithm Improvements C. Lu, X.M. Sun and Y. Jin Summary of Research Status of Active Power Filter H.Y. Wang Based on Class D Amplifier Magnetic Resonance Wireless Transmission Technology X.P. Yu, G. Yang and W.H. Song Application Research of Electrical Automation Technology for Power Plant H.J. Xie and W.L. Li Research on the Key Factors of Mis-Operations and Correct Operations of the Residual Current Operated Protective Device L.M. Shao, M. Zhang and S.H. Du Research and Application of UPS-CAN Protocol in Parallel System H.Y. Zhang, K.D. Zhu and J.Z. Wan

555 560 564 569 575 579 583 587 591 595 599 603

Applied Mechanics and Materials Vol. 721

Selective Harmonic Elimination PWM Technique with Walsh Functions for Nine-Level Cascade Inverter C.F. Zheng, X.M. Xu, B. Zhang and D.Y. Qiu An Active Equalization Technique Based on Flyback Converter Topology H.W. Liu, H. He, Y. Gui and X.W. Hao Development and Research of MEMS S.C. Wang, Y.P. Hao and S.J. Liu Capacitive Touch Sensing Principle and Application in the Car Q.X. Tao Microcomputer Interface Experimental System Design W.J. Cai and H.S. Peng The Model of Estimating MTL Crosstalk in Inhomogeneous Medium Y.H. Gao, Z.Y. An, T.H. Wang, K.Y. Yang and J.X. Wang Research on Four Water-Filling Power Distribution Schemes in MU-MIMO System Y.B. Zhang, J.X. Li and W.M. Wen Design of Electronic Control System for HCCI Optical Engine and Optical Testing Y.H. Gao, M. Si, P. Cheng and J.C. Chi

g

607 612 618 622 626 631 635 639

Chapter 7: Communication for Vehicles and Transportation, Signal Processing Discussion on the Technology Application of OFDM in the Ad-Hoc Network of Unmanned Aerial Vehicle (UAV) Z. Yuan, S.D. Yang and H.W. Liu A Novel Anti-Collision Algorithm of Tags in RFID System X.P. Zong, R.K. Lu, P.G. Wang, T. Zhang and S. Liu Design Method of an Intermediate Transcoding Structure Based on XML A.M. Pu An Effective Multiple-Receiver SAS Range Doppler Algorithm Based on the Modified Range History Approximation Z. Tian, J.S. Tang and H.P. Zhong The Study on DFS Algorithm of Unmanned Aerial Vehicle Platform Communications Relay System H.W. Liu and W.B. Luo Method of Common View Based on Digital Satellite Television X.L. Liu, Y. Hua, Y. Xiang and H.Q. Liu Modulation Type Recognition of OFDM Signals Based on EMD A.M. Gong, B.H. Wang, Y. Qu and Y.R. Zheng Network-Coding-Based Mutually Cooperative Transmission in Deep Space Communications X.L. Yao, X.R. Zhu and P. Yu Research on Near Space Optical Communication Transmitting and Receiving Technology W.D. Zhan, D.Y. Xiao, Z.Q. Hao and H.Z. Li

645 649 653 657 662 666 670 674 678

Chapter 8: Information Technology and Networks Applications Software Defined Networking: A New Trend of Networking Y.R. Zheng, G.W. Shi, W.B. Luo and A.M. Gong Integrated Avionics Network Dynamics Analysis D. Li, J.D. Zhang, Y. Wu and G.Q. Shi High Performance Start Node of Edge Ordering for Infrastructure Networks B. Xie, X. Liu and Y.C. Mo The Design of the Power Energy Management System Based on Wireless Sensor Network W.L. Li and H.J. Xie The Applications of Information Visualization in Power Systems X.L. Wang, M.X. Lu, D.D. Cai and Y.G. Nian

685 689 693 699 703

h

Vehicle, Mechanical and Electrical Engineering

The Application of Communication Network in Distribution Automation Y.W. Ding, D.D. Cai, Y.G. Nian and Y.C. Wang The Application of Distribution GIS in Regional Distribution Network Z. Yang, Y. Tang, Y.Q. Zhang and C.C. Wang Multi-Objective Programming Model for Joint Scheduling of Ships in the Complex Water Network Y.C. He, C.C. Liu and C.P. An Research on Energy Saving in Cellular Networks Based on Dynamic Load Balancing J.B. Xue, F.J. He and B. Liang Application of Process Migration in Intelligent Router Based on BLCR T.X. Wang, M.H. Jiang, J. Liang, C.L. Zhou, F. Yu and X. Wu Channel Assignment Algorithm in Centralized WLAN D.W. Dong, X.G. Liu and T. Jing Integrity and Privacy Preserving Data Aggregation Algorithm for WSNs H. Zhang A Study of the Model which Can Improve LEACH Protocol C.G. Wang, X. Zhang and K. Han Power Control Optimization for Position Tracking in Vehicle Network Z. Yang, G.Z. Tan and F.X. Zhang An Optimized Design of Safety Messages’ Communication Sub-Layer in Vehicle Ad-Hoc Network X.Y. Song, G.Z. Tan, M.J. Liu and F.X. Zhang Visualizing Interrupts and Replication with Timer J.S. Pan and S. Cheng

707 711 715 720 724 728 732 736 740 744 750

Chapter 9: Recognition, Video and Image Processing An Effective Program Clustering Algorithm for TV Recommendation System C. Cheng, X.Y. Liu and X.J. Wang The Application of Gaussian Mixture Modeling in Traffic Flow Videos W.Y. Lv, H.Y. Yang, R. Wang and J.Y. Li Research on Locality Preserving Discriminant Projection Algorithm Based on Gabor for Face Expression Recognition W.Y. Gong and F.X. Lu Design and Algorithm Optimization of P2P Mobile Monitoring Network-Based Facial Recognition System X. Li, P. Wang and H. Li Rotation Moment Invariant Feature Extraction Techniques for Image Matching Y.Q. Lai The Application of Improved PSO Algorithm in PMMW Image OSTU Threshold Segmentation P. Chen, T. Zou, J.Y. Chen, Z. Gao and J. Xiong A Robust Line Filter for Automatic X-Ray/CT Image Segmentation S.H. Peng, H.D. Nam, Y.F. Gan and X. Hu The Research Based on H.264 Embedded Video Systems of Optimized Compression Algorithm Y. Liu, Z.J. Cen and J. Liu

757 762 766 771 775 779 783 788

Chapter 10: Materials for Vehicles and Transportation, Civil Constructions, Fuel Cells and Energy Materials Effect of Ce on Secondary Extrusion Deformation Microstructure of ZM21 Alloy Q. Li, B. Zeng, W.B. Zhu, S.Y. Wang, X.Q. Jiang and F.S. Pan Effects of Zn on Microstructure and Mechanical Properties of Mg-6.0Zn-0.6Zr-1.0Y Magnesium Alloys Q. Li, B. Zeng, W.B. Zhu, S.Y. Wang, X.Q. Jiang and F.S. Pan

795 799

Applied Mechanics and Materials Vol. 721

Influence of Laminate Scheme on Design of Continuous Fiber Reinforced Composite Turbofan Engine Shaft Y.D. Sha, Q.Y. Jia, L. Luo, F.T. Zhao and X.C. Luan Analysis of Tunnel Running through Bridge Pile Foundation Based on the MIDAS/GTS J.K. Li and D.J. Tang Influences of Ground Reflection on the Elevated Box Bridge Structure Noise X.Y. Zhang, Q. Song and X.A. Zhang Acoustic Properties of Magnetorheological Fluids under Magnetic Fields M. Shen and Q.B. Huang Synthesis and Photoelectric Properties of Donor-Acceptor-Donor Molecule Containing Perylene Diimide C.M. Jiao, S.L. Yi, G.M. Zhou and Q. Xu Preparation and Properties of SiO2-P2O5-TiO2 Membrane for Fuel Cell Electrolytes L. Zheng, F. Wang, T. Li, Y.S. Li and J.H. Li Analysis of Resistance of Current Collector in Packing-Type Microbial Fuel Cell H. Li, L.G. Shao and Y.Y. Jiao Vibrating Rechargeable Battery Design Research Based on the Two Kinds of Charging Ways K.L. Shi Collision Simulation and Analysis of Energy-Absorbing Box for Vehicle K. Song and B.C. Yang

i

803 809 813 818 824 829 834 838 842

CHAPTER 1: Vehicle Engineering and Design

Applied Mechanics and Materials Vol. 721 (2015) pp 3-6 © (2015) Trans Tech Publications, Switzerland doi:10.4028/www.scientific.net/AMM.721.3

Submitted: 22.10.2014 Accepted: 28.10.2014

Reverse Optimization Algorithm of CVT‘s Energy Economy Min Zhoua Chongqing College of Electronic Engineering, Chongqing 401331, China a

[email protected]

Keywords: CVT; energy economy; reverse optimization algorithm.

Abstract. Taking CVT plug-in hybrid electric vehicle as the study subject, the objective function of the energy economy is created. In view of engine, motor efficiency and CVT efficiency coupled relationship; proposed reverse optimization algorithm to solve function. The target speed ratio of optimal energy economy is obtained in different SOC (State Of Charge), velocity, acceleration and slope. Introduction If plug-in hybrid electric vehicle (PHEV) adopts CVT as the transmission, its energy consumption is not only related to the engine efficiency, but also affected by CVT efficiency, motor efficiency and battery efficiency. In this paper, the objective function of the energy consumption is set up, which is calculated by reverse calculation method. Then the best target speed ratio of CVT MAP is get under different state of charge (SOC), velocity, acceleration and grade. The definition of the optimization problem The optimization of energy consumption economy about PHEV, it comes down to the following optimization problem that seeks the best target speed ratio and the speed and torque of engine and motor under the given SOC, velocity and acceleration. In this way, the system optimization objective function can be expressed as indicated in max Q . At this point, it is necessary to state the problem: in each instant the controller should minimize consumption cost considering some global boundaries, as the battery SOC variation, and some local boundaries, as the maximum or minimum torque and speed of each traction system, and it should define how to split the power request between the engine and the motor. The problem boundaries are analytically defined in (1). Tm min ≤ Tm ≤ Tm max n m min ≤ nm ≤ nm max 0 ≤ Te ≤ Te max (1) ne min ≤ ne ≤ ne max icvt min ≤ icvt ≤ icvt max SOCmin ≤ SOC ≤ SOCmax where Tm and Te are the output torque of motor and engine respectively; nm and ne are the speed of motor and engine respectively; icvt is the speed ratio of CVT.

Reverse optimization algorithm In view of engine, motor efficiency and CVT efficiency coupled relationship; proposed reverse optimization algorithm to solve function.

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Vehicle, Mechanical and Electrical Engineering

Start SO C=[0.95:-0.01:0.35] I=1:length(SO C) v=[0:160] J=1:length(v) a=[0:5] K=1:length(a) Calculate Pv icvt = [0.422 : 0.01 : 2.432] M =1:length( i cvt )

The target speed ratio calculation

N = vicvt i0 / 0.377 r

0 < N < 6000

No

Y es Look-up table to get the CVT efficiency

Ts = 3600 Pv r / ( vi0 )

Tin = Ts /( icvtη cvt )

0 < Tin < Tm max

W ork out Q

Yes EV mode Tm = Tin , Te = 0

Q < Q (i , j ) t

No

H ybrid m ode N o Tm否= Tm max , Te = Tin − Tm max

Y es Target assignm ent

N ext M

M loop end

No

Y es N ext K No Y es

Next I

K loop end

J loop end

No

Yes N ext I No I loop end Y es End

Fig.1. Target speed ratio optimization calculation flow chart In the Fig.1, Ts is the output torque of CVT; Tin is the input torque of CVT. Optimization algorithm and its results In view of the efficiency of CVT has coupling relationship between the efficiency of engine and motor, this paper puts forward the reverse calculation of optimization method to solve the objective function. The optimization calculation results as shown in figure 2 to 6. Figure 2 and figure 3 presents the optimization results of CVT speed ratio in CD mode under different velocity, acceleration and the slope gradient (0, 0.05, 0.15, and 0.25, respectively). It can be seen that the velocity is greater, the CVT speed ratio is smaller; the acceleration is greater, the CVT speed ratio is bigger; the slope gradient is greater, the CVT speed ratio is greater.

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Fig.2. Target speed ratio of EV mode in CD

Fig.3. Target speed ratio of hybrid mode in CD Figure 4 to 6 presents the optimization results of CVT speed ratio in CS mode under different velocity, acceleration and the slope gradient. It can be seen that the change trend is the same as with CD mode.

Fig.4. Target speed ratio of EV mode in CS

Fig.5. Target speed ratio of engine mode in CS

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Vehicle, Mechanical and Electrical Engineering

Fig.6. Target speed ratio of hybrid mode in CS Conclusion In view of the efficiency of CVT has coupling relationship between the efficiency of engine and motor, the reverse calculation of optimization method is designed to solve best CVT speed ratio under different SOC, velocity, acceleration and slope grade. References [1]

Jiang Qiang, Liu Hong-yi, Hao Jian-jun, et al. Ratio control strategy for an electromechanical continuously variable transmission based on engine models [J]. Advanced Materials Research, 2011,291(294), 2861-2865.

[2]

Xia Jing-jing, Wang Dong. CVT speed ratio control study on the dynamic performance compensation[C]//. IEEE. International on Consumer Electronics, Communications and Networks: Yichang, IEEE, 2012: 2480-2482.

[3]

Wang Yuan-zhi. Speed ratio control study on CVT with electrical pulley actuation system [J].Advanced Materials Research, 2010, 39(342), 342-346.

[4]

Montazeri M, Asadi M. Genetic-fuzzy shifting strategy for continuously variable transmission in parallel HEV[C]//. IEEE. International Symposium on Mechatronics and its Applications: Jordan, IEEE, 2008: 1-6.

[5]

Huang Wei, Zhou Yun-shan, Xue Dian-lun, et al. A study on driveline control strategy of the fou-wheel drive hybrid electric vehicle based on CVT [J]. Automotive Engineering. 2008, 6(30):501-505.

Applied Mechanics and Materials Vol. 721 (2015) pp 7-11 © (2015) Trans Tech Publications, Switzerland doi:10.4028/www.scientific.net/AMM.721.7

Submitted: 28.10.2014 Accepted: 06.11.2014

Hybrid mobile power supply using PEMFC and Lithium Battery Nijun Song, Wei Qi a,*, Jian Fang, Wei Liu, Duo Xiao School of information and electrical engineering, Zhejiang University City College, Hangzhou 310015, China a,

*[email protected]

Keywords: power supply, power switching, PEMFC, Lithium Battery

Abstract. A hybrid mobile power supply is developed based on air-cooled Proton Exchange Membrane Fuel Cell (PEMFC) and lithium battery. Pure fuel cell provides a rated output of 25W. With the assistance of lithium battery, the system provides instantaneous power of 50W. The system consists of fuel cell and its controller, lithium battery and its management circuit, power switching circuit, system control circuit and DC/DC convert circuit. The system is designed and realized, the measurement results show that the system works steadily below 50W. Introduction With the development of communication technology and computer technology, human society has entered the information age from the industrial era. Increasing demands are put forward for obtaining information by cell phones, laptop and other equipment anywhere, any time. For outdoor workers or outdoor sports enthusiasts, the power supply issue has always been a problem. Battery type mobile power supplies effectively alleviate the problem of cell phone. But the battery cannot provide basic mobile power supply for a long time laptop. With the supply of hydrogen, the PEMFC can generate continues power, so it is suitable for mobile power supply. The reaction product of PEMFC is only water, there is no any pollution to the environment, and the quiet reaction process does not produce any noise pollution [1]. In this paper, a hybrid power supply is proposed and realized using the PEMFC and lithium Battery. The two types of power work at switching mode, the PEMFC provide continuous long-term power generating, and the lithium battery provides instantaneous over loading. With the assistant DC/DC convert circuit, the system provides long-term power supply for the laptop and can replace the conventional AC power adapter. System design The reaction of the PEMFC is not burning, but slow chemical reaction process. As an independent power supply, the PEMFC has a long starting time, a slow power response and a poor overload characteristic. A lithium battery is introduced as assistant power for reducing the power requirement of the PEMFC and enhancing the overload capacity of the system. By investigating the battery capacity and standby time of laptops in the market, the calculated average operating power is 20W or less. The rated output power of the system is designed to be 25W and meets command of most laptops. Taking into account the conversion efficiency, a 50W PEMFC is introduced to the system design. In order to improve short-term overload of the system, a 12V/5AH lithium battery is used. According to the 1C discharge rate for a lithium battery, the maximum output may exceed 50W. The entire system consists of hydrogen supply system, PEMFC, fuel cell controller, lithium battery and DC/DC conversion circuit, as shown in figure 1. Owe to the lower power of the PEMFC, the requirements for the hydrogen supply system is lowered. The sodium borohydride-site hydrogen production [2, 3] or solid hydrides [4, 5] can meet the supply requirements. In this paper, a mature solid metal hydrogen storage tank with a hydrogen storage capacity of 350L. When the load power is less than 25W, only PEMFC works. When the power exceeds 25W, the lithium battery provides all

8

Vehicle, Mechanical and Electrical Engineering

the power. The charging circuit is responsible for the management of lithium battery. System controller takes charge of real-time voltage and current detection of the PEMFC and lithium battery, as well as the dual power switching. The DC/DC conversion circuit meets the command of output voltage. Fuel supply Power switching

PEMFC PEMFC Controller

Charging Cirtuit System Controller

DC/DC conversion circuit

Lithium Battery Power flow Signal flow

Fig. 1 System block diagram of the hybrid power supply Circuits design Charging circuit of the Lithium Battery. The output voltage of the PEMFC is 14-20V, and the maximum charging voltage of the 3-series lithium cells is 12.6V. Therefore a step-down charging scheme is necessary. The maximum output power of the PEMFC is only 50W, so the charting current of lithium battery is about 2-3A. To reduce the system complexity of, the MAX1873 is selected for the charging circuit. The low-cost MAX1873 provides all functions needed to simply and efficiently charge 2-, 3-, or 4- series lithium-ion cells at up to 4A or more. It provides a regulated charging current and voltage with less than ±0.75% total voltage error at the battery terminals. An external P-channel MOSFET operates in a step-down DC-DC configuration to efficiently charge batteries in low-cost designs. The MAX1873 regulates the battery voltage and charging current using two control loops that work together to transition smoothly between voltage and current regulation. An additional control loop limits current drawn from the input source so that adapter size and cost can be minimized [6]. For 3-series lithium cell in our design, MAX1873SEEE is used, as illustrated in figure 2.

Fig. 2 Max1873 based charging circuit of the lithium battery The set of total input current limit. MAX1873 can coordinate system and the total current lithium battery charging current. When the load power is larger, the chip can automatically reduce the charging current and the input current of the entire system is maintained in a certain range. The input current limit is set by the resistance R20 connected between CSSP and CSSN. In our design, the PEMFC provide the charging current, the total input current limit is set to 3A. A reasonable set of source current from the fuel cell has protection function as also.

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The regulating of charging current. The Charging current ICHG is set by the current-sense resistor RCSB connected between BATT and CSB. A resistance of 0.1Ω is used as RCSB, and the maximum charging current for the lithium battery is set to 2A. The control of charging start-stop The MAX1873 stops charging when ICHG/EN is pulled low (below 0.5V) and shuts down when the voltage at DCIN falls below the voltage at BATT. A start-stop control circuit is designed based on a PNP transistor. The charging start-stop is controlled by the BatChargeCtrl, which could be adjusted by the program in the system controller. Power switching circuit. In our design, only one power between the PEMFC and lithium battery provides the all output power for the system. The dual power switching circuit takes charge of the output switching between the PEMFC and the lithium battery. The circuit consists of a relay and two parallel connected diodes, as shown in figure 3. The system controller detects the real-time system power, and the relay works at ON state under normal condition. As mentioned above, the output voltage of the PEMFC is always higher than that of the lithium battery, thus the PEMFC provides all the output power. Under certain condition, when the output power exceeds 25W, which is over the rated power of the system, the system controller wills cutoff the output of PEMFC by the relay. When the output voltage of PEMFC falls below that of the lithium battery, the lithium provides all the output power. Taking into account that the system works in normal state at the most time, this design is a trade off between the performance and cost.

Fig. 3 Dual power switching circuit

Fig. 4 Voltage and current detection circuit

Voltage and current detection circuit. A real-time monitoring of the PEMFC and lithium battery is necessary when the system is running, in order to provide dual power switching or system protection. The maximum output voltage of the PEMFC and the lithium battery are 20V and 12.6 respectively, far more than the AD input voltage. A resistance-based voltage divider and a voltage follower based on integrated operation amplifier are added between the output and AD input pin, which is shown in figure 4. A current sensor ACS712-05B from the Allegro Company is used in the current detection. The device contains a precise, low-offset, linear Hall sensor circuit that can detect output current linearly proportional to the voltage, bidirectional current can be detected, and simple peripheral circuit, very easy to use [7]. ACS712-05B used in the system has a detection range of ± 5A, and the sensitivity is 185mV/A. Figure 4 shows the voltage and current detection circuit of lithium battery, the detection circuit of PEMFC is similar. DC/DC conversion circuit. The output voltage of PEMFC is 14-20V, while that of lithium battery is 9-12.6V, the output voltage after the dual power switching circuit is 9-20, so the DC/DC conversion circuit should cover this voltage range. The voltage of laptop power supply is 16-22V, differ from one another. The 19V output is chosen in the design. The LTC3780 from Linear Company is used in the design of DC/DC conversion circuit [8], which is shown in figure 5. LTC3780 is a high performance buck-boost switching regulator controller that operates from input voltages above, below or equal to the output voltage. It has 4-36V input voltage range, and the output

10

Vehicle, Mechanical and Electrical Engineering

voltage can be adjusted between 0.8V-30V voltages with accuracy of ± 1%. The output voltage is calculated as:

R VOUT = 0.8 × 1 + 19 R25

(1)

The target system output voltage is 19V, and the voltage is set by resistance of 100K and 4.4K, wherein a precision potentiometer is implemented.

Fig. 5 DC/DC conversion circuit Conclusion A hybrid mobile power supply is proposed and realized based on PEMFC and lithium battery. The system provides 25W rated output power, and a 50W maximum output power. The measurement results show that the overall efficiency of the system is more than 70%. The built-in hydrogen tank can provide continuous power supply of more than 12 hours for the laptop. The system design takes into account the cost and performance, and provides a long continuous power supply solution for outdoor workers or outdoor sports enthusiasts. With the wider use of hydrogen energy systems, the cost of the system will be less and less, the system has a good industrial prospects and huge potential market. Acknowledgments This work was supported by the Science and Technology Department of Zhejiang Province (No.2014C33026), Zhejiang University City College Scientific Research Foundation (No.J-14022, No.J-14021), the construct program of the top priority discipline in Hangzhou and the construct program of the top priority laboratory information processing and intelligent system in Hangzhou.

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References [1] M. Hou, B. L. Yi: Journal of electro chemistry, Vol. 18(2012) No. 1, p. 1-13. (In Chinese) [2] Y. X. Wang: Chemical industry and engineering progress, Vol. 28(2009) No. 12, p. 2122-2128. (In Chinese). [3] X. Zhang, K. B. Sun, J .B. Zhou: Inorganic Chemicals Industry, Vol. 42(2010) No. 1, p. 9-12. (In Chinese). [4] S. F. Fan: Chemical Propellants & Polymeric Materials, Vol. 8(2010) No. 2, p. 15-19. (In Chinese). [5] F. L. Shang, H. T. Yang, H. T. Han: Rare Metals Letters, Vol. 25(2006) No. 2, p. 11-16. (In Chinese). [6] Information on http://datasheets.maximintegrated.com/en/ds/MAX1873.pdf [7] W. H. Hu, P. N. Zhang: Electronic Component & Device Applications, Vol. 11(2009) No. 7, p. 1-6. (In Chinese). [8]Information on http://www.linear.com/docs/7197

Applied Mechanics and Materials Vol. 721 (2015) pp 12-15 © (2015) Trans Tech Publications, Switzerland doi:10.4028/www.scientific.net/AMM.721.12

Submitted: 03.11.2014 Accepted: 10.11.2014

The Research and Design on the Electric Vehicles’ Centrifugal Automatic Transmission Yi Fana Transportation College, Nanning University, Nanning 530200, China a

[email protected]

Keywords: Electric vehicle, automatic transmission, centrifugal type, planetary gear trains.

Abstract. Aiming at the deficiencies of the commonly used AMT and DSG structure in the electric vehicles’ transmission, a kind of three-speed automatic transmission structured by the planetary gear trains is designed. It uses the centrifugal components to realize the gear shifting, while using the electromagnetic brake and the motor reversal to realize the reversing. Based on the design concepts proposed, we did some matching calculations on the transmission system of a three-wheel pure electric vehicle, and finally made the optimization design on the driving motor’s selection and transmission parameters. The designed electric vehicle’s centrifugal automatic transmission has the characteristics of simple structure, small size and shifting smoothness, which can not only meet the requirements of the automobile power, but also improve the efficiency of the driving motor. Introduction With the continuous increasing in the automobiles’ sales, the development of the automobiles is becoming faster and faster. Traditional automobiles that are powered by the internal combustion engine pump out thousands of tons of poisonous fumes every year, which does great harm to the atmospheric environment and the human bodies [1, 2]. The pure electric vehicles become the hot issues of research all over the word for the electric power, power sources in various forms and the high energy utilization [3]. However, the present battery performance of the electric vehicles cannot fully satisfy the actual needs of the automobiles. Low energy density, low safety performance, but high cost, little mileage range and inconvenient charging make it as its big handicap [4]. In order to meet the requirements of large torques when vehicle starting and high speeds when smooth running, the transmission system is widely used in present electric vehicles. It is common for them to use two-speed automatic transmissions, and the most popular ones are AMT and DSG [5]. When using the AMT, the shifting smoothness is poor because of the synchronizer. While using the DSG, as it has only two gears, the dual-clutch structure is complicated and the cost is also a lot. Different from the above electric vehicles’ automatic transmission, a kind of centrifugal three-speed automatic transmission that is suitable for the electric vehicles is proposed. The paper introduces the working principle of this three-speed automatic transmission and plans its structure parameters. The designed three-speed automatic transmission has planetary gear trains, and uses centrifugal components to realize the gear shifting. It can not only meet the requirements of the automobile power, but also improve the efficiency of the driving motor due to the reasons of simple structure, small size and shifting smoothness. The overall structure of the electric vehicles’ centrifugal automatic transmission According to the common planetary-gear structure features, we designed a structure of three-speed centrifugal automatic transmission composed by double-row planetary gear sets, shown in Fig.1. Let’s suppose K is the characteristic parameter of the planetary gear trains, ZR is the number of the ring gear and ZS is the number of the sun gear. Then, it can be expressed as K=ZR /ZS.

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The transmission is mainly structured by the planetary gear. The input shaft is connected with the front sun gear. The back ring is the power output. And in the double-row planetary gear set, the front planetary carrier joins with the back planetary carrier, the front ring with the back sun gear. The centrifugal clutch, the centrifugal brake and the one-way clutch are shown in Fig.1, too. The power transferring characteristics of all gears can be seen as follows: First gear (low speed). The one-way clutch C1 makes the front and back planetary carriers locked up. The front sun gear drives the front ring to reverse. And the driving power is transmitted to the back sun gear. Due to the lock-up back planetary carrier, the back sun gear drives the back ring to output positively. This finally realizes the first forward gear, also called as the low-speed gear, which is mainly used in vehicles’ starting and climbing. Following the power transferring route, the transmission ratio I1 is K2 under the first gear. Second gear (medium speed). As the speed of the driving motor boosts, the speed of the back sun gear increases. When it reaches a certain speed, the centrifugal brake B2 on the back sun gear starts to work, and the one-way clutch C2 locks up. This leads to the braking of the front ring and the back sun gear. At the same time, the one-way clutch C1 separates automatically. The driving power is transmitted to the front sun gear via the driving motor, then to the front planetary carrier via the front sun gear, and finally to the back ring output via the front planetary carrier. This finally realizes the second forward gear. Following the power transferring route, the transmission ratio I2 is K under the second gear. Third gear (high speed). If the speed of the driving motor increases further, the speed of the front and back planetary carriers improve. When the speed of the back planetary carrier reaches the preset level, the centrifugal clutch C3 begins to work, which makes the back planetary carrier and the back ring engaged, and makes the one-way clutch C2 unlocked automatically. Then the front and back planetary gear trains are considered as a whole, due to the connection of the front and back planetary carriers. Finally, we realize the direct transmission. Its transmission ratio I3 is 1. Reverse gear. To realize the reverse gear, the electromagnetic brake B1 is engaged. The front and back planetary carriers are locked up, and the power transferring route in the reverse gear is the same as that in the first gear. However, the driving motor drives reversely. Its transmission ratio IR is -K2. B1

C1

7

B C3

1

4 3

B2 6 C2

A 2

5

1 driving motor 2 front sun gear 3 front planetary carrier 4 front ring 5 back sun gear 6 back planetary carrier 7 back ring B1 electromagnetic brake B2 centrifugal brake C1 one-way clutch C2 one-way clutch C3 centrifugal clutch A input axle B output axle Fig.1 Structure diagram of the centrifugal automatic transmission Designing requirement of the electric vehicle The electric vehicle proposed in this paper is three-wheel structured. Two wheels on the front axle are responsible for the steering, the monowheel in the back axle for the driving. The driving motor and the battery are fixed up between the front seats and the back axle. Table 1 shows the vehicle parameters and the performance requirements.

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Vehicle, Mechanical and Electrical Engineering

Table 1 Vehicle parameters and performance requirements Parameter

Index

Parameter

Index

Parameter

Empty mass /Kg

600

rolling resistance coefficient

0.018

Full mass/Kg

750

Frontal area /m2

2.05

Driving mode

Driving by the monowheel on the back axle

wheel radius /m

0.289

0-50Km/h acceleration time/S

≤9

air resistance coefficient

0.31

transmission efficiency

0.92

Rotating-mass conversion factors

1.02

maximum speed /(Km.h-1) maximum climbable gradient /%

Index 80 20

Matching of the driving motor It is of great importance to consider the rated power, the peak power, the maximum speed and the maximum torque when choosing the driving motor [6], so that the requirements of the automobiles’ dynamic property can be satisfied. The rated power and the peak power of the driving motor. To ensure the driving motor has an enough backup power to meet the performance requirements in Table 1, the maximum power Pmax of the driving motor should satisfy the need of three powers, they are: the maximum-speed power Pa , the maximum-climbable-gradient power pb , and the power according to the acceleration time Pc . It can be expressed as: pmax ≥ max [ pa , pb , pc ] (1) 2 CD Aumax mgf + 21.15 ui CD Aui2 Pb = mgf cos α max + mg sin α max + 3600ηT 21.15

Pa =

Pc =

umax 3600ηT

1 3600t aη T

(2) (3)

2 3 δm u a + mgf u a t a + C D Au a t a 2 t 1 .5 21.15 × 2.5 a

(4)

In equation (2), (3) and (4), u max is the maximum speed; η T is the transmission mechanical efficiency; m is the electric vehicle’s outfit mass; f is the rolling resistance coefficient; C D is the air resistance coefficient; A is the frontal area; α max is the maximum climbable gradient; u i is the climbing speed; u a is the terminal accelerated speed; t a is the acceleration time; δ is the rotating-mass conversion factors. According to equation (2), (3) and (4), the three different power values are Pa =7.9Kw, pb =20.4Kw, Pc =12.5Kw. The maximum torque of the driving motor. The maximum torque of the driving motor is determined by the maximum climbable gradient. It can be expressed as: r C A Tmax = (mg cos α max + mg sin α max + D v 2 ) (5) 21.15 ηT i In equation (5), r is the wheel radius; i is the total transmission ratio; α max is the maximum climbable gradient; v is the minimum speed when climbing. Considering the calculation results and the motor types in the present market, we finally choose the motor parameters in table 2. Table 2 Motor parameters Parameter

Value

Parameter

Value

Rated power (kw)

8

Maximum speed (r/min)

6000

Peak power (kw)

20

Rated torque (N·m)

180

Rated speed (r/min)

3000

Maximum torque (N·m)

200

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The parameter optimization of the transmission system. The transmission ratio of the electric vehicle when in the first speed should meet the requirement of the maximum climbable gradient. It can be expressed as: mg ( f cos α max + sin α max )r i≥ = i1 * i0 = K 2 * i0 (6) Tmax The transmission ratio when in the third speed needs to meet the requirement of the maximum speed. It can be expressed as: i ≤ 0.377 * r * nmax / vmax = i3 * i0 = i0 (7) In equation (6) and (7), i0 is the main reducer’s reduction ratio; Tmax is the maximum steady rotational speed of the driving motor. From the electric vehicle’s performance parameters, we can get the values, those are i0 =8.2 and K =1.9. Conclusion In this paper, we propose a design for the three-speed automatic transmission in the electric vehicles. We can realize the gear shifting via the centrifugal components, and reversing via the electromagnetic brake and the motor reversal. Through the matching calculations on the transmission system of the three-wheel pure electric vehicle, we can make the optimization design on the driving motor’s selection and transmission parameters. The advantages of the designed electric vehicle’s centrifugal automatic transmission are simple structure, small size and shifting smoothness. While meeting the need of the automobile power, the design proposed can also improve the efficiency of the driving motor. Acknowledgments This paper was financially supported by the Scientific Research and Technology Development Project of Nanning Yongning District (20140216A). References [1] F. Yan, E. Winijkul, T.C. Bond and D.G. Streetsb: Atmospheric Environment, Vol. 87 (2014), p.189-199. [2] S.J. Zhang, Y. Wu, X.M. Wu, M.L. Li, Y.S. Ge, B. Liang, Y.Y. Xu, Y. Zhou, H. Liu, L.X. Fu and J.M. Hao: Atmospheric Environment, Vol. 89 (2014), p.216-229. [3] R. Bohnsack, J. Pinkse, A. Kolk: Research Policy, Vol. 43 (2014) No.2, p.284-300. [4] B.M. Al-Alawi, T.H. Bradley: Renewable and Sustainable Energy Reviews, Vol. 21 (2013), p.190-203. [5] I. Husain: Electric and hybrid vehicles: design fundamentals (The Chemical Rubber Company, U.S.A. 2011). [6] Z. Yu: Theory of automobile (China Machine Press, China 2000).

Applied Mechanics and Materials Vol. 721 (2015) pp 16-19 © (2015) Trans Tech Publications, Switzerland doi:10.4028/www.scientific.net/AMM.721.16

Submitted: 03.11.2014 Accepted: 09.11.2014

Design on a Collision Warning and Vehicle-headlights Intelligent Switching System Yi Fana, Rukui Dengb Transportation College, Nanning University, Nanning 530200, China a

[email protected], [email protected]

Keywords: STC12C5A60S2, collision warning, headlights intelligent switching.

Abstract. The purpose of this paper is to propose a design of an automobile driver-assistance system. It uses the STC12C5A60S2 as its main controller, and the HC-SR04 as its ultrasonic ranging. Then, the sensor DHT11 is responsible for the external temperature and humidity measurement as well as the velocity correction. These help to improve the precision of the ultrasonic measurement. Through the combination of the vehicle distance detecting and the photo resistance monitoring the external illuminance, we can realize the vehicle-headlights intelligent switching. LCD12864 is an information display combining all the information such as temperature, humidity, vehicle distance and so on, and finally conduct the collision warning via the buzzer. The experimental results show that the system designed has advantages of reliable operation, simple structure, low price and double functions as the collision warning and the vehicle-headlights intelligent switching, which ensures it a good application prospect. Introduction The automobiles’ collision warning and vehicle-headlights intelligent switching have been becoming the hot issues of research all over the word for their impacts on the automobiles’ safety and energy saving [1, 2]. Considering the influence of temperature on the ultrasonic ranging, a kind of automobile collision warning system was designed by Qu X [3]. Before that, a vehicle-headlight switching system working at the ramp driving and automobiles meeting was proposed by Z. J. Zou, which didn’t take the interaction of the same direction’s vehicle-headlights into account [4]. Earlier than that, R. J. Chang used the infrared sensor to do the vehicle recognition and vehicle-headlights intelligent switching, which was of high precision [5]. However, it was of great difficulty for all the automobiles equipped with that system. The above three literatures have done the research on either the automobiles’ collision warning or the vehicle-headlights intelligent switching. So far, no joint study has been reported. Based on the ultrasonic ranging principle, the paper puts forward a system of collision warning and vehicle-headlights intelligent switching to warn the vehicle distance. By using the combination of the vehicle distance detecting and the photo resistance monitoring the external illuminance, we can realize the vehicle-headlights intelligent switching. And the LCD buzzer will do the voice warning, light warning, and information displaying. The overall scheme and working principle The overall structure of the collision warning or the vehicle-headlights intelligent switching system is shown in Fig.1. It is composed by a main controlling circuit, a HC-SR04 ultrasonic ranging circuit, a DHT11 temperature and humidity measuring circuit, an illuminance monitoring circuit, a collision warning system, a vehicle-headlights controlling circuit and a LCD12864 display circuit. The main controlling circuit, as the core part of the system, is responsible for all modules’ power supplying, signal transmitting and operation processing. Via the ultrasonic transmitter, the receiver and the controlling circuit, the ultrasonic ranging circuit gets the signal duration. Then combining the temperature and humidity values measured by the DHT11 circuit, it modifies the precision of the

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ultrasonic ranging. The illuminance monitoring circuit feels the light intensity of external environment. Finally, the vehicle-headlights intelligently switch according to all the above measured results. And the LCD12864 will display the above vehicle distance, temperature and humidity. The buzzer will alarm. Input

Output Main controlling circuit

HC-SR04 ultrasonic ranging circuit DHT11 temperature and humidity measuring circuit Illuminance monitoring circuit

LCD12864 display circuit

Collision warning system

Vehicle-headlights controlling circuit

Fig.1 System working principle chart Hardware designing

GND VCC V0 RS(CS) R/W (SID) E(CLK) DB0 DB1 DB2 DB3 DB4 DB5 DB6 DB7 PSB NC /RST VOUT LED_A LED_K

The main controlling circuit Choosing. The main controlling circuit, using STC12C5A60S2 as its chip, is an eight-bit microcontroller with high performance, low power consumption and enhancement type. It has a flash program memory of 60KB, a SRAM space of 1280B and double-serial ports. The internal integration is a MAX810 specific reset circuit. Its working frequency is 0~35MHz. Ultrasonic ranging and modifying. The system applies HC-SR04 as its ultrasonic ranging module. It measures the distance via the time detection method [6]. The basic working principle is: TRIG I/O interface triggers the ranging, and then the modules automatically send eight square waves with 40 kHz. If there is a signal returning back, a high level will be exported via the ECHO I/O interface. And the duration time of the high level is the acoustic roundtrip time of the ultrasonic. The metered distance =〔high level duration time × acoustic velocity(340M/S)〕/2. In order to improve the measurement accuracy, we plan to modify the acoustic velocity. The acoustic velocity modified formula is: c = 331.4 + 0.607t . c is the propagation velocity of the ultrasonic in the air, and the unit is m / s ; t is the Celsius temperature, and the unit is o C . Vehicle-headlights controlling. Through the circuit built by the photoresistance, the external illuminance is monitored. Considering the distance the ultrasonic sensor measured out, the vehicle-headlights automatically switch according to a set value.

VCC

VCC

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

VCC

12V

VCC

VCC

P2.0

R5

12V

10k

S1

P2.1

Q2

R2

R1

NPN

1k

1k

Q1 NPN

RL2

RL1

12V

D2

P2.2

D1

12V

R3 330 22pF

X1 11.0592Mhz 22pF

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

P1.0/ADC0 P1.1/ADC1 P1.2/ADC2 P1.3/ADC3 P1.4/ADC4 P1.5/ADC5 P1.6/ADC6 P1.7/ADC7 RST/P4.7 P3.0/RXD P3.1/TXD P3.2/INT0 P3.3/INT1 P3.4/T0 P3.5/T1 P3.6/WR P3.7/RD XTAL2 XTAL1 GND

VCC P0.0 P0.1 P0.2 P0.3 P0.4 P0.5 P0.6 P0.7 EA/RSET2/P4.6 ALE/P.5 NA/P4.4 P2.7/A15 P2.6/A14 P2.5/A13 P2.4/A12 P2.3/A11 P2.2/A10 P2.1/A9 P2.0/A8

STC12C5A60S2

Fig.2 Overall system circuit working principle diagram for vehicle-headlights intelligent switching

40 39 38 37 36 35 34 33 32 31 30 29 28 27 26 25 24 23 22 21

1 2 3 4

1 2 3 4

VCC DATA NC GND

VCC Trig Echo GND

the collision warning and the

18

Vehicle, Mechanical and Electrical Engineering

Collision warning and information displaying circuit designing. Through the ultrasonic sensor, the acoustic roundtrip time is metered, and the acoustic velocity values are corrected by the temperature and humidity values. When the vehicle distance is smaller than 150, the buzzer will alarm. The system data display is through a LDC12864 screen with Chinese font, and takes the serial communication mode, which occupies less I/O interfaces. The screen shows the information such as the vehicle distance, the temperature, humidity and so on. Figure 2 shows the overall system circuit working principle. Software design The STC12C5A60S2 single chip microcomputer collects the acoustic roundtrip time, and corrects the acoustic velocity value using the data gathered by the temperature and humidity sensor. When the distance reaches the preset value, the LED screen and the buzzer will warn the driver on the vehicle distance information. Through the combination of the information of the vehicle distance detecting and the photoresistance monitoring the external illuminance, we can realize the vehicle-headlights intelligent switching. The software flowchart of the collision warning and vehicle-headlights intelligent switching is shown in Fig.3. Main program begins System initialization

Call the collision warning program

DHT11 module measures the temperature and the humidity

Call the illuminance monitoring program

HC-SR04 ultrasonic module meters the roundtrip time

Call the vehicle-headlights intelligent switching program

Calculate the corrected vehicle distance

Call the LED display program

Fig.3 Software flowchart System testing and analyzing Tests are done according to the design scheme under different temperature, humidity and illumination condition. The testing results show that: when the distant is less than the preset value, the collision warning module starts to work, and the buzzer alarms; when the external illuminance is low, considering the vehicle distance, the vehicle-headlights are intelligent switched; Conversely, the vehicle-headlights won’t turn on. Under different temperature and humidity, the ultrasonic ranging data is shown in Table 1. The results suggests that: considering the impact of the temperature and humidity on the velocity, when the temperature is 15 oC or 25 oC and the relative humidity is 50%, 70% or 90%, the relative error of the metered distance is less than 0.5%. What’s more, the measure precision is higher than that of considering only the temperature. Table 1 Vehicle distance under different temperature and humidity Temperature distance (cm) Humidity (%) Actual distance 50 100 150 200 250 300

15 oC 50 70 Measured distance 50.1 50.0 100.5 100.3 150.6 150.4 200.8 200.7 250.8 251.1 301.2 301.4

90

25 oC 50

70

90

50.1 100.4 150.5 200.9 250.9 301.1

50.1 100.2 150.6 200.9 250.7 301.5

50.2 100.1 150.2 200.7 251.2 301.1

50.2 100.5 150.4 200.5 251.2 301.3

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Conclusion In this paper, we propose a design of a collision warning and vehicle-headlights intelligent switching system, using the STC12C5A60S2 as its main controller. Under different illuminance, we can realize the vehicle-headlights intelligent switching according to the preset conditions. When the vehicle distance is less than the present value, the buzzer alarms. Using the temperature and humidity sensor to correct the acoustic velocity can achieve the effect of collision warning. The relative error of the metered distance is less than 0.5%. Using the LCD12864 screen is convenient for the drivers to know well the information of temperature, humidity and vehicle distance. The software of the designed system is of high integration, and can reliably implement the functions such as the collision warning, vehicle-headlights intelligent switching, message displaying like the temperature and humidity, and so on, which is with great practical value. Acknowledgments This paper was financially supported by Science and Technology Research Project of Guangxi University (YB2014451). References [1] K.D. Kusano, H.C. Gabler: IEEE Intelligent Transportation Systems Society, Vol. 13 (2012) No.4, p.1546-1555. [2] J.C. Rubio, J. Serrat, A.M. Lopez, D. Ponsa: IEEE Intelligent Transportation Systems Society, Vol. 13 (2012) No.2, p.594-605. [3] X. Qu, W.G. Zheng: Applied Mechanics and Materials, Vol. 457 (2014), p.1631-1634. [4] Z.J. Zou, J.L. Feng, Y.C. Guo, M.D. Ge: Electronic Instrumentation Customers, Vol. 18 (2011) No.3, p.20-22. [5] R.J. Chang: Journal of Hubei Automotive Industries Institute, Vol. 24 (2010) No.2, p.77-78. [6] J.F. Wang: Modern sensor technology (China Machine Press, China 2006). [7] Y. Zhang, A.G. Chen, R.G. Gao: Piezoelectrics & Acoustooptics, Vol. 33 (2011) No.1, p.26-29.

Applied Mechanics and Materials Vol. 721 (2015) pp 20-23 © (2015) Trans Tech Publications, Switzerland doi:10.4028/www.scientific.net/AMM.721.20

Submitted: 04.11.2014 Accepted: 10.11.2014

Study on the Closed-loop Control Strategy of Voltage for Pure Electric Vehicle in Limp Mode Liang Chu a, Chong Guo b, Yi Yang c, Zicheng Fu d and Yuanjian Zhang e State Key Laboratory of Automobile Simulation and Control, Jilin University, Changchun 130025, China. a

[email protected], [email protected], [email protected] d e [email protected], [email protected]

Keywords: pure electric vehicle, limp mode, closed-loop control of voltage.

Abstract. In order to avoid the nonreversible damage to the batteries because of over discharge when the pure electric vehicle is moving, this article proposes a driving control strategy in limp mode due to under voltage. Firstly, research the discharge characteristics of the lithium battery pack. Secondly research and develop the control strategy that limit the motor torque by the closed-loop control of voltage. Finally develop the strategy model using MATLAB/Simulink, and finish the verification test and prove the control strategy effectiveness by offline simulation. Introduction The limp mode is a fault status of electric vehicles. It may be result in some cautions, such as under voltage of the batteries, over-temperature of the motor etc. The limp mode is used for reducing energy consumption, keeping a longer driving range, and protecting the main parts of power system. The demand torque would be limited and restrained to remain the power system parts running reasonably [1]. The mainly consideration is the control strategy in the limp mode caused by under voltage of the batteries. The way to remain the battery voltage is using the PID closed-loop control strategy. And for the target vehicle whose structure is center drive and no transmission, the strategy is achieved by adjust the torque command to the motor. Analysis Discharging Character of Lithium Battery The lithium battery has a reasonable voltage area [2-3]. The over discharge is the discharge operation after the battery reaches discharge cut-off voltage. Such as a kind of lithium battery, the voltage range is from 2.5V to 4.2V at 30 degrees Celsius, shown as figure 1. In other words, the discharge cut-off voltage is nearly 2.5V.

Fig.1 Discharge curve of lithium battery at 30 degrees Celsius

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Generally, it need to a certain concentration of Lithium-ion to remain the structure stable in cathode. However, the over discharge can make Lithium-ion excessive out from cathode and cause the nonreversible damage [4]. Especially when the battery is at low temperature or in low state of capacity (SOC), the voltage drop is faster because of the bigger resistance. So the discharge cut-off voltage has significance for protection. Control Strategy of Closed-loop Control of Voltage According to the discharging character above, there are several voltage areas of lithium battery in the actual use, such as charge limited, normal use, discharge limited and discharge disabled, shown as figure 2. VBat Vchr _ high Vdis _ limit Vdis _ disable

Fig.2 Schematic diagram of threshold voltage In the figure 2, the Vdis _ disable is provided by supplier, and the vehicle will break off when the voltage reaches there. And Vdis _ limit is a voltage which is higher then Vdis _ disable , it is defined to limit driving torque. When the batteries pack is running in the discharge limited area, the discharge power is limited. And the maximum discharge power can be expressed as follow: E −U Pmax = U ocv (1) R Where, Pmax is the maximum discharge power, Eocv is the open circuit voltage, R is the internal resistance, U is the current voltage. Ignoring the power loss from battery to motor, the maximum output torque can be expressed as follow: 9550 ⋅ Pmax Tmax _ ref = (2) n Where, Tmax _ ref is the maximum output torque in current rotate speed, n is rotate speed. And then a feedback regulation is designed by PID control to adjust the current voltage close to Vdis _ limit . The input information of PID control is difference between the current voltage and Vdis _ limit . Finally get the output torque by calculation to ensure the current voltage close to Vdis _ limit as possible. The mathematical expression is shown as equation (3-4), and the control process shown as figure 3. d (U dis _ limit − U ) (3) ∆T = K p ⋅ (U dis _ limit − U ) + K i ∫ (U dis _ limit − U ) + K d ⋅ dt Tmax = Tmax _ ref + ∆T (4)

22

Vehicle, Mechanical and Electrical Engineering

Fig.3 Process of PID control strategy of voltage Offline Simulation According to the voltage PID control process, the simulation model has built using MATLAB /Simulink to verify control strategy by offline simulation. Firstly, the parameters of the target vehicle and main parts of power system show in table 1. Table 1 The parameters of the electric vehicle and power system Motor Rated Power (kW) 42 Maximum Power(kW) 90 Rated Torque(Nm) 100 Battery Pack Capacity(Ah) 60 Rated Voltage(V) 326 Main Reduction Ratio Ratio 8.28 Vehicle Drag Coefficient 0.28 Radius of Wheels(mm) 307 Curb Weight (kg) 1552 For the simulation, the SOC sets to 10%, the Vdis _ limit sets to 300V. And there is an operating condition selected, which is the process of 100% accelerator opening. The simulation results of the process of 100% accelerator opening shows as figure 4. As the curve shown, the current is reduced to stability, and the voltage is adjusted back to nearly 300V after dropping below 300V, prove the control strategy effectiveness.

Fig.4 Simulation results of process of 100% accelerator opening in limp mode

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Conclusions This closed-loop control strategy of voltage using PID control can keep voltage close to a protect threshold by limiting the current and the torque, so that the damage because of over discharge can be avoided. In sum, the strategy benefits performance improvement and lifetime extension for the batteries. References [1] C. Lin, G.Q. Chen, X. Meng, F.C. Sun: Beijing Ligong Daxue Xuebao, Vol. 27 (2007) No.1, p.25.Strategies of control for electric bus transmission [J], 2007, 27(01) p.25-28. [2] B. Zhang, G.T. Lin, Q.S. Chen: Chinese Journal of Power Sources, Vol. 32 (2008) No.2, p.95. Performance of LiFePO4/C Li-ion battery for electric vehicle [J], 2008, 32(2) p.95-98. [3] S. Gasworth, T. Tankala, A. Kancharla: SAE Technical Paper, Vol. 1 (2011), p.1341. [4] F.K. Zhou: Research on Powertrain Parameters Design and Vehicle Control Strategy for Pure Electric Vehicle (Ph.D., Jilin University, China 2012), p137.

Applied Mechanics and Materials Vol. 721 (2015) pp 24-27 © (2015) Trans Tech Publications, Switzerland doi:10.4028/www.scientific.net/AMM.721.24

Submitted: 10.11.2014 Accepted: 11.11.2014

Performance Analysis of a Pure Electric Light Off-road Vehicle based on the Cruise Jianjun Yang Scholl of Automotive Engineering, Wuhan University of technology, Wuhan 430070, China [email protected] Keywords: Theoretical calculation; Dynamic performance; Economy; Cruise; Modeling; Simulation

Abstract. According to the approximate design requirements of pure electric light off-road vehicle in the initial stage of development, fundamental parameter values of the main parts of the vehicle driving system are selected after relevant theoretical calculation. With the vehicle modeling and its performance simulation on the Cruise software, indicators of its dynamics and economic performance are chalked up and the results have been compared to the design requirements, which have verified the feasibility of Cruise software application in the electric vehicle driving system production development. Introduction The lack of oil resources has been much more obvious and vehicle tremendous damage pressure to the environment has attributed to the discovery of the alternative energy sources and the application of the renewable energy. The national development planning guidelines on automobile industry lead the entire automotive industry to make progress on the low oil-energy consumption and low-pollutant emission [1]. The driving conditions of urban road is much complex and congested, so the production development design of a pure electric light off-road vehicle has great significance to improve the urban environment and has huge market potential for the automobile enterprises. Driving system of the pure electric light off-road vehicle Layout of driving system. The pure electric light off-road vehicle was designed to just adopt the original internal combustion engine model to realize the elementary renovation in the early period of the whole production development process. Therefore, the internal combustion engine driving system arrangement and the main essential parameters of the traditional vehicle are still applied in the new pure electric light off-road vehicle's driving system. The batteries package, driving motor, final drive, differential, half shaft and wheels have constituted the new vehicle driving system to achieve the power delivery. The vehicle's primary parameters and the expected performance index are shown in table 1. Table 1 Primary vehicle parameters and expected performance index Curb weight

1350kg

Wheelbase

2420

Cross weight

1650kg

Tires

205/70R15

Front/rear wheelspan

1305/1310

Length/width/height

3970/1570/1730

25

Minimum ground clearance

150mm

100 km/h

Electricity consumption per hundred kilometers

15 degrees

Expected maximum gradability Expected maximum speed

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Main components selection of driving system. The driving motor is the core-block component of the pure electric vehicle driving system, whose function performance directly determined its basic driving performance, especially the vehicle maximum speed, acceleration time and climbing performance, etc. Refer to the functional performance comparison of all kinds of automotive driving motors existing in the current automobile industry [2], combining with the new electric light off-road vehicle's driving requirements, it gives the preference for the permanent magnet brushless DC motor as the driving motor. The main driving conditions of the new light off-road vehicle are urban road. Considering the chosen driving motor type and after the theoretical calculations, the transmission ratio of final drive is determined as 7.5. In terms of the specific parameters of the driving motor and existing widely-used power battery in the automobile market, a type of lithium iron phosphate batteries is selected as the power source of the electric light off-road vehicles [3]. The rated capacity of the battery is 100Ah and the nominal voltage is 320V. Modeling and simulation in Cruise Vehicle dynamic model. The Cruise software has predigested the devising process for engineers, which already contained all the main subsystems and components of the vehicle in the module. When establishing the vehicle dynamic simulation model in the software, the only need to drag the corresponding parts models of the vehicle into the platform window. According to the power transmission route from the power battery in turn connected to the driving tires, and the driver module should be added to realize the control of the vehicle. That is the whole procedure for building the vehicle dynamics simulation model [4]. The vehicle dynamics simulation model of the new light off-road vehicles is shown in figure 1.

Figure 1 Vehicle simulation model of the new light off-road vehicles Then the specific parameters of those chosen modules ought to be confirmed and calculated, such as the vehicle module parameters, the final drive parameters, the differential parameters, the wheel parameters, the brake system parameters and the driving motor parameters. Then it needs to establish the physical connections and signal connections between the modules [5]. Setting the simulation tasks. The maximum speed, acceleration time, maximum tradability, mileage and energy consumption should be obtained by kinds of simulation environments. Very simulation setting has the specific purpose to obtain the vehicle performance assessment criteria.

26

Vehicle, Mechanical and Electrical Engineering

Although the simulation tasks are really tedious, the operation process of the software setting is easier for the engineers. With the help of the model checking function, the premier operation steps can be checked whether they are set up correctly. Then we can choose a single calculation computing task to do the simulation. Analysis of simulation results. During the calculation process, the speed and torque of the driving motor, vehicle speed can be shown within the scope of the time domain. The voltage and current of the battery can be dragged out to the scope in real time through the monitor module. The engineers can adjust the simulation parameters during the computing process just to interrupt and stop the simulation, which realizes the real-time control of the simulation. The specific simulation results from the Cruise software are as follows: 1) NEDC cycle run simulation results. The NEDC cycle run condition of the pure electric light off-road vehicle is shown as figure 2. Through the cycle run condition simulation result, it can be easily obtained that the total mileage in this condition was 10.93km; the driving time was 1180s; the top speed was 110.5km/h; the average velocity was 33.3km/h and the parking times during the driving were 13. When the torque of the driving motor transforms from positive to negative, the pure electric light off-road vehicle can recover energy during the braking operation, which extends the mileage and reduces the electricity consumption per hundred kilometers.

Figure 2 NEDC cycle run condition 2) Full load acceleration performance simulation results. Through the simulation results, it indicated that the vehicle 0 ~ 100 km/h acceleration time was 17s and the top speed was 105 km/h. The results have met our initial design goals. 3) Climbing performance simulation results. The simulation results indicated that the maximum grad ability of the pure electric light off-road vehicle was 27%, which completely satisfied the requirement on the city light off-road vehicle climbing performance. 4) Mileage simulation result. In the Cruise V2000 software, there is no estimation of the pure electric vehicle mileage, therefore the electricity consumption per hundred kilometers index is adopted to measure the vehicle fuel economy performance [6]. From the analysis of the NEDC driving cycles, the pure electric light off-road vehicle's electricity consumption per hundred kilometers was 20 degrees, which has not achieved the design target on the economy performance. 5) Results comparison. The comparison between the simulation results and the expected design requirements is shown in table2.

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27

Table 2 Comparison between simulation results and expected design requirements expected design simulation performance improvement requirements results Top speed(km/h) 100 105 5% Maximum gradability 25 27 8% Electricity consumption per unrealized 15 20 hundred kilometers Conclusions Through the dynamics modeling and cycling run simulation of the pure electric light off-road vehicle during the development design stage based on the Cruise software, the vehicle power performance can be estimated by the several simulation tasks results. The top speed has been improved by the 5% from the original target and the maximum tradability has extended 8% than the premier setting, while the electricity consumption in hundred kilometers has not met the economy requirements. The main parameters of the pure electric light off-road vehicle driving system can be obtained by the design requirements and the corresponding theoretical calculation during the early development. And through setting the theoretical calculation parameters in the Cruise software, the simulation results can directly verify whether the basic performances have met the standard, which also validate the feasible application of the Cruise software in the pure electric vehicle driving system design and provide further design reference for the parameters selection and the optimization of economic performance. Reference [1] S. M. Cui: The new energy automotive technology (Peking University Press, China, 2009) p.122. [2] Z. S. Yu: Automobile Theory (China Machine Press, China, 2009) p.75. [3] Q. Q. Chen: Modern electric car technology (Beijing Institute of Technology Press, China, 2002) p.36. [4] R. Wang, H. W. He: Vehicle dynamic performance simulation analysis base on the Cruise (Vehicle & Power Technology, China, 2009) p.46-49. [5] B. Wang. Z. Li: The Cruise application in the hybrid vehicle performance simulation (Automotive Engineering, China, 2007) p.32-35. [6] S. K. Wang: Modeling and simulation of pure electric city bus based on the Cruise software (Bus technology and research, China, 2011) p.54-58.

Applied Mechanics and Materials Vol. 721 (2015) pp 28-31 © (2015) Trans Tech Publications, Switzerland doi:10.4028/www.scientific.net/AMM.721.28

Submitted: 28.10.2014 Accepted: 06.11.2014

FSAE Race Car Engine Options on the Impact of Vehicle Design Hang Gonga, Yaoping Lib, Jie Chaoc Kunming University of Science and Technology, Kunming 650500, China a

[email protected], [email protected], [email protected]

Keywords: FSAE; racing; engine; options.

Abstract. FSC is a Formula One car contest car works by the universities or auto-related professional school students team up to participate in the design and manufacture of automotive competition. All participating teams in accordance with the rules and race car manufacturing standards, in a year's time to design and manufacture an accelerating, braking, handling, and so has excellent performance of small single-seater racing leisure, to successfully complete all or Part of the event part of the game. This article discusses the single-cylinder engine and four-cylinder engine of the car design different vehicle. Introduction FSC, which is the abbreviated form of Formula Student China, is a competition about design and manufacturing of racecar. The design of every single system requires professional knowledge, especially when it comes to the choice and match of the engine. Engine is the major part of the power system in a racecar and it will evidently influence the performance of a racecar. The selection of the engine is related to the total weight and power performance of a racecar. A 20mm flow-limiting valve must be installed to the engine according to the FSAE rules. The selection of the engine will influence the design of the whole car and it’s the basis of a car, so the design process begins with engine selection. This article will introduce the influences of engine selection on the design process of the whole car. Analysis of the optional engine in FSAE According to the FSAE rules, the optional engine must be a four-stroke piston type internal combustion petrolic engine whose displacement is under 610cc. If you use multiple engines, then the total displacement must be under 610cc. To limit the engine power, a flow-limiting valve whose inner section is rounded must be installed to the air intake system, just between the throttle valve and engine. And all the air that flow into the engine must flow through that flow-limiting valve. Any device between the flow-limiting valve and engine and can control air flow is forbidden. If you use multiple engines, then all the air that flow into the engine must flow through that flow-limiting valve. Self-design turbo and super charger are allowed. But the original turbo that is specially design for the engine is forbidden. The flow-limiting valve must be installed between supercharging equipment and carburetor or throttle valve. Therefore, the only valid installation order is: throttle valve, flow-limiting valve, supercharging equipment and engine. The intake air can be cooled by inter cooler using only ambient air. Air-air and air-water inter cooler are both valid. The maximum diameter of the flow-limiting valve’s inner section is as follow: If use gasoline – 20.0mm (0.7874 inches);If use E85 fuel – 19.0mm (0.7480 inches). The selection of the engine According to the rules, after research, analysis is as follow: Single cylinder engine. We make a general survey of the market and found that the optional single cylinder engines are mainly from high-emission trail bike and beach bike. Valid displacements are 450cc, 500cc, 600cc. Their characteristic is as follow:

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Fig. 1 Engine external characteristic Max rev is 7000rpm – 8000rpm; Max power is 30kw – 35kw;Max torque is 45 N·m – 55 N·m; Weight is 35kg – 45kg. The 20mm flow-limiting valve doesn’t influence the engine performance much. Let’s take Jialing F02 (most common in China) as example: Max power: 30 kw/6000rpm;Max torque: 51 N·m/4500rpm;Max rev: 7000rpm. After the installation of the 20mm flow-limiting valve: Max power: 28.9 kw/6000rpm;Max torque: 48.9 N·m/4500rpm. According to the requirement of dynamic competition, engine should be able to output enough torque at low rev. We can see from the engine external characteristic diagram that F02 engine can output 80% of the max torque at 2300rpm, 45 N·m at 3000rpm and 49 N·m between 4500rpm to 5000rpm at max. Its output at common rev is outstanding. Smooth and wide torque curve will make the car easier to drive and comfortable. Drivers will easily know the car’s dynamic, which will help them get a better lap time. Single cylinder engine is well match the design requirement in the aspect. After the installation of the flow-limiting valve, F02 engine’s max power and torque reduce to 96.33% and 95.88% of the original and the influence is very small. The main reason is that high-emission single cylinder engine has a relatively low max rev. So in the same working condition, its intake air is less than a 4-cylinder 600cc engine. That’s why it’s not influenced much. Need to explain, the results above is got in a condition that we just simply change the inner section of the original air inlet to 20mm and EFI is also original. If we use a more reasonable flow-limiting valve, the influence on the engine will reduce more. Double cylinder engine. We make a general survey of the market and found that the optional valid double cylinder engine is less, so this article will not talk about it. 4-cylinder engine We make a general survey of the market and found that the optional double cylinder engines’ displacements are 250cc, 400cc, 600cc, of which 600cc 4-cylinder engine is the most welcome one in FSAE. Honda CBR600 series, Yamaha YZF-R6 series, Suzuki GSX-R600 series, and Kawasaki ZX-6R series are the most common 4-cylinder engine in FSAE. Their characteristic is as follow: Max rev is 12000rpm - 15000rpm; Max power is 70kw - 75kw; Max torque is 60 N·m - 65 N·m; Weight is 55kg - 60kg.

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Fig. 2 4-cylinder engine power diagram

Fig. 3 4-cylinder engine torque diagram The diagram on the top is power, whose unit is horsepower (1BHP=0.746kw). The diagram on the bottom is torque, whose unit is pound per foot (1lb*ft=1.4 N·m). All the data is got after the installation of the 20mm flow-limiting valve. Let’s take the Honda CBR600 series (most common in China) from University of Applied Sciences – Munich as example: After the installation of the 20mm flow-limiting valve: Max power: 66 kw/12000rpm;Max torque: 60 N•m/8500rpm. According to the dynamic competition’s requirement for speed, engine should be able to output enough torque at low rev. But 600cc 4-cylinder engine acquires max power and torque by high rev, and the torque curve is not smooth. That makes the car not so easy to drive and it requires a higher level of skill from the driver. Need to explain, University of Applied Sciences – Munich had made a lot of changes to their CBR600RR engine. They use programmable MOTEC EFI instead of the original EFI and redesign the intake system. After collecting the data of the engine at different working conditions, they complete the EFI program on the engine bench. The influence of engine selection on car design The different weight and external characteristic curve of single cylinder engine and 4-cylinder engine has a great influence on car design.

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The influence of single cylinder engine selection on car design The influence of the 20mm flow-limiting valve on a single cylinder engine is small. And if you use a more reasonable flow-limiting valve, the influence will become even smaller. So reducing the total weight of car is the key to enhance its overall performance and key to car design. FSAE rules have very strict stipulate on the chassis’ material and specification. It’s not easy to reduce the weight of chassis. Many foreign teams are financially strong, they usually use carbon fiber to make monocoque to reduce weight. But in China, the cost of opening mound is too high. We can only make carbon fiber or fiberglass body to reduce weight. According to the rules, tyres and rims must be bigger than 8 inches, so valid sizes are 10 inches and 13 inches. The weight of 10 inches rim is only two thirds of the 13 inches version due to the reducing of size. The size of upright and braking system will be smaller with small-sized rims and tyres, that way we can reduce more weight. The reducing of unsprung weight well evidently bring the car’s acceleration performance to a higher level. What’s more, lightweight is also beneficial for fuel economy. The influence of 4-cylinder engine selection on car design 4-cylinder engine has a better performance in max power and max torque than single cylinder engine, so lightweight isn’t the very key point for design. The most important thing in design process is to reduce the influence of the flow-limiting valve on engine. If you use the original EFI, then you only need to redesign the intake system and use the data from bench testing to optimize it. If you use programmable EFI, then lots of works on engine bench test are waiting for you. You have to do large amount of experiments and collect data to optimize the intake system. The workload of which is very heavy. No matter what kind of EFI you use, the original or the programmable one, the main target is to reduce the influence of the 20mm flow-limiting valve on engine. Conclusion The engines involved in this article are all naturally aspirated and non-naturally-aspirated engines are not discussed. The key influence of single cylinder engine on car design is how to reduce the total weight and unsprung weight of the car. While the key influence of 4-cylinder engine on car design is how to reduce the influence of the 20mm flow-limiting valve on engine. References [1] Automotive engineering manual editing committee: Automotive Engineering Manual (China Communications Press, China 2001). [2] Xiaofan Ju: Design-Build-Test of a Formula SAE Racing Car (MS., Shanghai Jiao Tong University, China 2009). [3] Jiarui Chen. Automobile structure (China Communications Press, China 2009). [4] Tian Cai: Performance analysis and research of A Formula SAE Racing Car (Ph.D.Hunan University, China 2009). [5] 2010 Formula SAE Rules, SAE Inc, 2009.10.

Applied Mechanics and Materials Vol. 721 (2015) pp 32-35 © (2015) Trans Tech Publications, Switzerland doi:10.4028/www.scientific.net/AMM.721.32

Submitted: 27.10.2014 Accepted: 01.11.2014

Based on CATIA car side welding fixture design and research Zhihan Zhu a, Hang Cao School of Mechanical Engineering, Shanghai Institute of Technology, Shanghai 201418 China a

[email protected]

Keywords: CATIA, Welding jig, Design efficiency.

Abstract. Based on using CATIA tools for car side around the welding jig are studied, by means of CATIA platform welding jig design, according to the design flow chart, produce drawings, it is advantageous to the reasonable arrangement of assembly line production, facilitate balanced location time, reduce the non-production season with lower production cost, improve the quality of automobile body design efficiency and design purpose. Side of fixture design study In general, a complete set of fixture design should be carried out in accordance with the process, car side circumference welding fixture design flow chart shown in Fig.1.

Fig.1: Design flow chart A top-down design approach Through the design flow chart as you can see, the final fixture design is gradually from the whole to the detail perfect, and make sure each set of fixture design orientation, according to the position of the welding torch to determine each set of fixture in various fast and compact design, this design method is called the "top-down" design [1]. Top-down design makes sure the whole framework of product, reference framework to determine the various assembly position again, and then determine the number of each set of fixture in various parts and assembly relationship. That is to say, each set of the

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parts in the welded structure to adapt to the environment, the distribution of welding torch to produce and can not interference, avoid unnecessary constraints. This makes the coordination between components in the structure of each set of the can more intuitive and more accurate, this is also the advantage of using CATIA software design [2]. Determine the overall design Fig.2 is a car side model. First of all, according to the lateral confining model is needed to determine the workbench and base plate, mainly according to the model of the size of the corresponding table is determined by the size and shape, a good number of welding jig design, reasonable arrangement of the distribution according to the measuring point location.

Fig.2: A car side model Sure each set of fixture design and installation According to the welding jig design, the requirements of the relevant considering the fixture and welding torch to avoid interference, check each set of fixture assembly craft whether meet the design requirements, so want to make sure of the welding torch location. Installation location is confirmed, on the workbench environment for the design of relevant fittings[3]. Anchorage is the foundation of the whole fixture, support, positioning function; Function limit up and down limit block, prevent the fixture due to error when clamping the work piece destroyed; Compaction and supporting blocks used at the same time, the profile role positioning; Main used for locating pin hole positioning; Transition plate is the brackets and other parts of the transition, all general positioning elements and clamping elements are installed on the transition plate; Adjust pad general thickness between 3-5mm, used for precision adjustment in the direction of distance; Transition piece is used here to connect positioning pin, and the effect of adjusting pad, ensure the locating pin in at least two can adjust the distance precision positioning direction; Pressure arm and limit block and clamp connection, under the drive of cylinder, reciprocating motion. Not only intuitive Fig. 3 shows the position of the solder joint, and show the connection of each group fixture and the relative position. Due to the complexity of actual work piece surface modelling, so here is not the actual design of fixture structure, but the group fixture element structure and connection mode[4].

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Fig.3: Car side surround jig schematic diagram Intensity Here choose V2_50_A10_T00_105 piston cylinder, through V2_50 ultimate strength to determine the diameter of cylinder, because parts of the clamping force of purpose is in order to ensure the work piece positioning precision, so under the conditions of ensure the work piece clamping can be welding, according to the desires of clamping mechanism of motive power to determine the cylinder bore.as shown in Fig.4[5].

Fig.4: Piston cylinder By using CATIA three-dimensional software for this part in overall quality attributes analysis and inspection on the overall global interference, results show that the volume of the parts and the surface area of the main parameters of inertia moment, etc[6]. Can manufacture and process to develop a good reference to parameters[7]. After the above work, the entire preliminary design completed, the final position of the critical design error modulus can be converted into a Three-dimensional engineering drawing. The use of CATIA drawing module, you can easily generate various components related views, and dimensioning, choose a good location and processing benchmark of each module, and can be converted into a variety of forms for storage. Three-dimensional jig dimension is the only way to design into production. We want to design specific folder tagging, including assembly, the Department installed some typical parts marking, especially fast briquetting care there adapter blocks[8]. When labelling, to choose a good fit positioning reference and processing benchmarks. Only the size of each element with a clear mark on the fixture after allowing workers to actual production, while marked in strict accordance with the requirements of workers to facilitate processing. Through continuous improvement design, the product can be smoothly mass production.

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Summary CATIA software provides a software product quality can not be achieved in a two-dimensional analysis, kinematic simulation analysis, the product of force and thermal analysis capabilities for designing the virtual welding fixture design, without drawing management development provided the conditions. Through the use of CATIA design requirements contralateral enclosed fixture designer must use reasonable. Standardized modeling approach, to build a good parametric model, so as to facilitate modification and improve design efficiency. Reference [1] Gengyun Han, Design of cab welding jig. New Technology & New Process,2, pp. 33-34, August 2001. [2] Xiao-ping Xiong, Motion mechanism analysis for car body welding jig. Modern Manufacturing Engineering,2, pp. 80-81, January 2005. [3] Shen Xuanyu, CATIA command Detailed finite element analysis and examples, Beijing: Mechanical industry press, 2005. [4] Chen Huanming, Welding equipment design,Beijing: Aviation industry press,2006. [5] Liwei Lu & Kejing He, Domestic and foreign automobile welding line two design examples of comparative analysis. Automotive technology,3,pp.35-37,1999. [6] Zhao Xiangyang, White body welding jig design and spot welding robot welding simulation, Chengdu: University of electronic science and technology,2007,6. [7] Zhenhong Yu, Meijuan Zhang, Simply analyses of the structural design for automobile component welding fixture. Journal of Wuxi Institute of Technology,4, pp. 31-34, September 2006. [8] Liu Jiaguang, Automobile welding fixture positioning in the size of the computer aided calculation, Machinery manufacturing,2, pp. 20-21, march 2004.

CHAPTER 2: Traffic and Transport Engineering, Vechicle and Road Safety

Applied Mechanics and Materials Vol. 721 (2015) pp 39-42 © (2015) Trans Tech Publications, Switzerland doi:10.4028/www.scientific.net/AMM.721.39

Submitted: 20.10.2014 Revised: 11.11.2014 Accepted: 11.11.2014

Effects of Traffic Calming Measures on Vehicle Speed Control and Road Safety Yubo Jianga, Lianghua Jiangb,*, Yaqin Qinc Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650500, China a

[email protected], [email protected], [email protected]

Keywords: Traffic calming; engineering measures; speed control; road safety.

Abstract. This thesis introduced the origin and function of traffic calming measures in solving road safety problem and reducing vehicle speed to make the environment more secure, also introduced several different types of basic traffic calming measures. Facts showed that using traffic measures can improve the safety of traffic environment, reduce vehicle speed and decrease accidents and pollutions. Therefore, traffic calming measures should be highly valued by governments and policy makers in constructing a city with harmony and prosperity. Introduction With the development of modern city and improvement of civilian’ living standard, auto mobiles have gradually come to people’s daily lives, especially in developed regions and areas. However, the increasing numbers and speed of cars also bring hidden danger to inhabitant around. Hence providing safety and quiet environment for inhabitant while promoting the development of auto industry has become a major issue for many transportation engineers and designers. Countries like Netherland first came up with the ideas of traffic calming, aimed to seek win-win outcomes for city environment and transportation functions through management of transportation engineering, behavior of road users and the planning and construction of the whole city. It has proved that regions with traffic calming measures could reduce the negative effects of cars effectively, also improve the quality of life of residents by reconstructing the living environment of local people. Traffic calming measures Origin and concept. Traffic calming measures first originated in Germany in 1920s[1],the local residents came up with the idea of “calming” in order to minimize the negative effects of traffic on their daily life. In 1960s,the residents of Denmark had taken measures to restrict or calm the vehicles from entering their living environment. In the year 1999, the institute of transportation engineering in the US put forward the concept of traffic calming: Traffic calming is combination of several measures to reduce the negative effects of vehicles to change the behavior of drivers and improve the environment of non-vehicles users on the road[2]-[3]. For much of the twentieth century, streets were designed by engineers who were charged only with ensuring smooth traffic flow and not with fostering the other functions of streets. The basis for traffic calming is broadening traffic engineering to include designing for these functions. Traffic calming has been successfully used for decades in cities across Europe. For example, a living street towards the end of the 1960s, initially in Delft, is a street in which the needs of car drivers are secondary to the needs of users of the street as a whole; traffic calming principles are integrated into their design. From the Netherlands, application spread rapidly to Germany, starting in North-Rhine Westphalia in 1976, becoming very widespread by the early 1980s. More recently, in response to growing numbers of traffic accidents and speeding problems, cities across North America have begun creating traffic calming programs to improve safety and liveability on residential streets. Many municipalities create asphalt or concrete measures, although preformed rubber products that are easier to install and consistently meet standardized requirements are becoming increasingly popular.

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Engineering measures. Engineering measures involve physically altering the road layout or appearance to actively or passively retard traffic by increasing the cognitive load of driving. Measures include speed humps, chicanes, curb extensions, and Living Street and shared space type schemes. A number of visual changes to roads are being made to many streets to cause more attentive driving, reduced speeds, reduced crashes, and a greater tendency to yield to pedestrians. Visual traffic calming includes lane narrowing (9-10'), road diets (reduction in lanes), use of trees next to streets, on-street parking, and buildings placed in urban fashion close to streets. Physical devices include speed humps, speed cushions, and speed tables, sized for the desired speed. Such measures normally slow cars to between 10 and 25 miles per hour (16 and 40 km/h). Most devices are made of asphalt or concrete but rubber traffic calming products are emerging as an effective alternative with several advantages Quite often residents have used a variety of homemade devices ranging from faux enforcement camera signs and even faux speed cameras and including dummy police.

a) Vertical deflection

b) Narrowing

c) Traffic signs and markings d) Infrastructure to transfer volume Fig.1 Engineering measures

Vertical deflection Speed bumps

Table 1 Different types of traffic calming measures Speed control Volume control Traffic signs and Infrastructure to transfer Narrowing markings volume Narrower Speed limit Completely closed road traffic lanes

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Speed humps Speed cushions Speed tables Raised pedestrian crossings

Curb extensions chokers Road diets

One way traffic Reduction to yield Pedestrian crossing

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Half closed road Diagonal closed road Changed T intersection

Small island

Positive results Speed control. Different measures of traffic calming have different effects on controlling vehicle speed, especially vertical deflection. Setting road barriers like speed bump and hump can make 85% of the vehicle speed less than 30km per hour[4]. Other measures, such as decreasing the turning radius and the area of intersection can also play active roles in reducing vehicle speed. Table 2 The Result of Differences traffic calming measures Maximum speed limit 85% speed limit /(km/h) mean speed/(km/h) (km/h) Measures

Vertical deflection narrowing Reducing intersection Rotary island

without measures

with measures

without measures

with measures

without measures

with measures

100

40

75

30

45~65

18~25

100

65

75

45

45~65

25~35

100

95

75

70

45~65

40~55

100

95

75

70

45~65

40~55

Decrease traffic accidents. Apply traffic calming measures can lower the vehicle speed then reduce traffic accidents. According to statistics, traffic calming measures can reduce the accidents with casualties by 60%, and can also reduce accidents with serious casualties by 78%[5].In Netherland, two communities with traffic calming measures had reduced traffic accidents by 44%, and the whole region had reduced accidents by 20%.[6] In six cities in west Germany, after setting traffic calming measures, the accidents had been reduced by 50%, and the casualty had been reduced by 63%.[7]Due to the traffic calming measures, the vehicle speed in urban areas had been slowed down. From the year 1982 to 1991, the casualty number in every one hundred thousand people in west Germany had been reduced from 6.2 to 2.3. A Cochrane Review of studies found that there is evidence to demonstrate the efficacy of traffic calming measures in reducing traffic-related injuries and may even reduce deaths. However, the review found that more evidence is needed to demonstrate its efficacy in low income countries. From the above results, traffic calming measures plays a significant role in decreasing traffic accidents in communities and regions. Although different countries have different types of traffic calming measures, the number of casualties had been reduced in a certain degree.Engineers and designers should pay highly attenion to the traffic calming measures when building the city’s environment and enhance the security of the public. Conclusion Traffic calming consists of physical design and other measures, including narrowed roads and speed humps, put in place on roads for the intention of slowing down or reducing motor-vehicle traffic as well as to improve safety for pedestrians and cyclists. Urban planners and traffic engineers

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have many strategies for traffic calming. Traffic calming measures has brought great changes in urban transportation designing and constructing. Apply of traffic calming measures have achieved positive results in many aspects, also make the city more safe, attractive and more comfortable to live in. Although it has developed for more than 80 years in western countries, it still has a lot of space for China to give a widespread use of traffic calming. Traffic calming measures can reduce the vehicle speed, improve the safety of transportation environment and decrease the accident. Therefore, facing the common challenges in building the cities and regions with harmony and prosperity. Traffic calming measures must be highly valued by our government and policy makers. Acknowledgements This work was financially supported by the Open Research Subject of Key Laboratory (Research Base) of Control and Safety Key Laboratory of Sichuan Province (szjj2014-066). References [1] Dennis Daughters. Traffic Calming Manual[R].City of Sarasota Engineering Department, 2003. [2] Reid Ewing. Traffic Calming - State of the Practice [M]. Washington D.C.: Institute of Transportation Engineers, 1999. [3] Lockwood I M.ITE Traffic Calming Definition [J]. ITE Journal, 1997(67), p.22-24. [4] Pharoah, Tim. Traffic Calming in West Europe [J]. Planning Practice & Research, 1993,(1), p.23. [5] Webster, D. C., Mackie, A. M. Review of traffic calming schemes in 20mph zones[R]. Crowthorne, UK: Transport Research Laboratory (TRL), 1996. [6] Hass-Klauetal.1992 Civilised Streets: A Guide to Traffic Calming[J]. Environmental and Transport Planning 1992. [7] Brilon W., Blanke, H. Extensive traffic calming: results of the accident analyses in 6 model towns[C], ITE 1993 Compendium of technical papers. Washington, DC, USA: Institute of Transportation Engineers (ITE), 1993, p.119- 123.

Applied Mechanics and Materials Vol. 721 (2015) pp 43-46 © (2015) Trans Tech Publications, Switzerland doi:10.4028/www.scientific.net/AMM.721.43

Submitted: 20.10.2014 Accepted: 28.10.2014

Violation Behavior Analysis of Pedestrian and Non-Motorized Vehicle Pan Haoa,*, Qingyu Haob Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650500, China a

[email protected], [email protected]

Keywords: Violation behavior; survival analysis; nonparametric method; empirical research.

Abstract. Behavior-based analysis of the relationship between pedestrian and non-motorized vehicle’s violation was established through empirical study and survival analysis. The nonparametric method which belongs to survival analysis a statistical method combining the result of the event and the time of result complied with the SPSS the data statistical analysis software was used to set up hazard rate function and waiting time survival function and then the regularities of the pedestrians and non-motorized vehicles’ irregularities are obtained. The study is helpful to evaluate the crowd on the influence of the irregularities and provide the basis for urban planning. Introduction Green Travel Plan and Slow Traffic are the current popular concept of urban traffic planning. The theme for urban traffic is to choose healthy and environmentally friendly ways of traveling. However, crossing especially violation behavior of pedestrian and non-motorized vehicle has a certain influence on traffic flow. Using survival of the data statistical method from the perspective of pedestrians and non-motorized vehicle drivers, the article studied the factors of violation, identified important factors, analyzed peccant pedestrians and non-motorized vehicle drivers compared with the results of nonparametric method and resulted to guide planning, design and signal control of intersection in order to construct safe and convenient crossing environment as the fundamental goal. Nonparametric method According to the order statistics provided by the sample nonparametric method estimated on survival rate, the probability object still survived after a period of time t, namely the probability survival time is greater than t. Survival function is the function that survival rate relative to time t and survival function's value at some t is the survival rate. For this study, the survival function reflects probability of the individual survival time to t, and the waiting time survival function is remaining proportion of pedestrians and non-motorized vehicles to wait after t. Survival function can be written as [1]: (1) = > =1− ≤ Hazard rate function h(t), survival probability in conditions, is refers to the probability the research object of survival died after moment of t represented as: ∆ ∆ ℎ = = (2) ∆ ∆ × ∆ →

∆ →

Applying these theories to the problems of the study, the corresponding relationship to each other

is: Survival time: crossing moment minus the time of arrival. The event start point: reach during the red light. The event end point: began to cross the intersection. Waiting time survival function containing censored data that time information is incomplete when light turns green and then violation people cross the road normally uses product estimation method. Arrange observation value of samples in total of n (survival time t) from small to large orderly, a rank i = 1, 2, 3...n. List the number of initial observations , the number of violations and the number of censored data in each beginning stage(10s). Calculate death probability = and survival probability = 1 − every moment. To solve each survival probability that non-motor vehicles’ endurance time beyond the moment =∏ . Standard error of survival rate is

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=

−

Analysis of factor influence Due to survival time generally does not obey the normal distribution, and censored data is contained in the survival time, it is difficult to apply the traditional regression analysis method. The selected covariates of traffic conditions can determine the effects on waiting time and safety [2]. The practical effects on waiting behavior and the feasibility of data acquisition are considered in the covariate selection. The multivariate Cox regression model considered a covariate—pedestrian or not, age, herd or not, and the type of non-motorized vehicle.

Pedestrian or not 1 yes, 0 no

Table 1 Research factors and assignment Herd or Non-motor vehicle Violation or Age group not type not 1 if under 20, 2 if 20–40, 3 if 40–60, 4otherwise

1 yes, 0 no

1 bicycle, 2motorcycle

Violation time

1 yes, 0 no

Continuous variable

Using SPSS the research got pedestrian and non-motor vehicle’s waiting time survival function curve and relative hazard function curve shown in figures below:

Accumulation of survival function

Relative hazard function

Fig. 1 Pedestrians or non-motorized vehicles

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Accumulation of survival function

45

Relative hazard function

Fig. 2 Different age factors

Accumulation of survival function

Relative hazard function

Fig. 3 Conformity factors (herd or not) The figure of survival function shows the pedestrians’ curve is under the non-motor vehicles’ curve in general. The relative hazard function curve in the opposite further validates the indication that the probability of non-motor vehicles’ violation is higher.

Accumulation of survival function

Relative hazard function

Fig. 4 The type of non-motorized vehicle

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The elderly’s ratio in early waiting is more than other age groups, abiding by the rules well. As time passed, teenagers wait patiently and the ratio of matures is higher than primes in waiting. The figure illustrates the pedestrians or non-motorized vehicles in the process of running a red light, are easily influenced by behavior of the crowd outside, making their own perception, judgment accord with the behavior of most people, namely someone also will not consciously "follow" when crowd violate around . Bicycle's and motorcycle's curves are intertwined, and have the same trend basically, but the motorcycle risk is higher than the former. Overall, effect of the type is not obvious. Conclusion The research served pedestrians and non-motor vehicles which reach during the period of red light as the object, studied the violation phenomenon and observed samples through the survival analysis method. Multivariate Cox regression model considering special covariates which can be pedestrians’ gender, age, herd and non-motor vehicle type needs to do the impact analysis between them. From the result of analysis, the most significant factor on violations is the age, secondly, difference between pedestrians and non-motorized vehicles is also slightly larger. Further research can consider other traffic environment factors, such as traffic design and the pedestrian psychology. References [1] Xiaobao Yang, Yingxue Zhou, Nonparametric survival analysis of traffic congestion duration time, Journal of Beijing Jiao tong University, 2000, 135. [2] Hongwei Guo, Ziyou Gao, Xiaobao Yang, Xiaobei Jiang, Modeling Pedestrian Violation Behavior at Signalized Crosswalks in China: A Hazards-Based Duration Approach, Traffic Injury Prevention, 2011, 12, p.98–99.

Applied Mechanics and Materials Vol. 721 (2015) pp 47-51 © (2015) Trans Tech Publications, Switzerland doi:10.4028/www.scientific.net/AMM.721.47

Submitted: 30.10.2014 Accepted: 01.11.2014

The Study of Traffic Rules Based on Cellular Automata Traffic Wave Model Wenbin Liu a, Yueqiang Yang b Wuhan University of Technology, Hubei 430070, China a

[email protected], [email protected]

Keywords: Traffic wave; vehicle; Cellular automata; Traffic rules.

Abstract. The traffic control problem is a hot issue in recent years. We investigate the effectiveness of three traffic rules in assuring safety and improve traffic flow. We utilize a cellular automata method of traffic flow to investigate and simulate the vehicle performance and use a linear weighting approach to weigh safety and traffic flow comprehensively. We establish overtaking models of a two-lane freeway on the basis of the stochastic traffic cellular automaton (STCA) model. Using the cellular automata method, we simulate the relationships between the traffic flows, the average vehicle velocity, and the vehicle density. We propose two new traffic rules, which in the premise of ensuring safety and improve traffic flow. Introduction Safety and efficiency are two major concerns in vehicle transportation. To reduce traffic accidents and promote traffic flow, series of traffic regulations are formulated in accord with different countries and regions. In many countries, the keep-right-except-to-pass rule is often employed for multi-lane freeways, which means drivers must keep driving in the right-most lane unless passing another vehicle, in which case they move one lane to the left, passing, and return to the right-most lane. By establishing a mathematical model, we analyze the performance of keep-right-except-to-pass rule, we propose two new traffic rules, and further optimization rules to improve traffic flow. The cellular automata (CA) models were first introduced by Johann Louis von Neumann when dealing with biological systems and gained its popularity since John Horton Conway created “Game of Life” [4]. And in 1992, K. Nagel and M. Schreckenberg first applied the concept of CA to a freeway traffic model and successfully simulated some real traffic flow characters such as the “start-stop-waves” [2]. The traffic cellular automata (TCA) are dynamical systems that are discrete in both time and space, and the system state updates every single time step [1].The stochastic traffic cellular automaton (STCA) Model is a single lane model with open or periodic boundary conditions, and the model is defined on a one-dimensional array of cells, according to the work of Nagel and Schreckenberg [2,3]. Each site is either empty or occupied by a vehicle, and the states of all the cells update synchronously in discrete time step according to four irreducible rules. A probability was introduced to the rules to simulate the spontaneous traffic jam in a real vehicle transportation. We utilize the idea of discretization of time and space and of employing rules to depict the vehicle systems when constructing vehicle transportation models. We develop several models to simulate vehicle performance in two-lane freeways and investigate the relationship between traffic flow characters. Freeway Model As is stated, we find vehicle transportation a complex dynamic process when constructing a mathematical model. In reality, a driver may decide to accelerate or decelerate in respond to the distance variation of the around vehicles, and overtake a slow vehicle when conditions permit, or even accelerate or decelerate without any reason, which adds to the indeterminacy of the entire vehicle transportation on a multi-lane freeway. To analyze the traffic flow of the entire traffic lanes, we take

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advantage of the idea of discretization and lane-changing of the STCA Model and the RNSL Model, then we develop an overtaking model and derive the models followed. Vehicle Generation Model .We use Matlab to simulate the freeway traffic flow with a cellular automata method. According to literature, vehicles approximately subject to Poisson distribution when entering the freeway intersections, therefore we obtain the generation model of vehicles as P( x = k ) =

e−λ λ k k!

To assure the safety of vehicle drivers in case accidents happen forward in a freeway, we need to introduce a safe distance d 0 . The generally accepted estimate of safe distance is v02 + v0t 2a Where the index v0 denotes the constant driving velocity, t the average reaction time of drivers, and a the acceleration. The safe distance ensures that a vehicle can decelerate to zero within the reaction time, thus avoid collision with preceding vehicles. We first simulate the cell behavior of the vehicle system each at a vehicle density σ ∈ {0.1,0.3,0.5,0.7} . d0 =

Fig. 1 Several simulations for different vehicle density.

Fig. 2 The overtaking process The Overtaking Model. We develop an overtaking model to simulate the traffic flow based on the RNSL Model by modifying the lane-changing rules to overtaking rules and develop a series of speed updating rules, referring to the retrograde overtaking model in. Since the drivers usually accelerate when overtaking a vehicle, and sometimes they overtake several slow vehicles, we depict the overtaking rules as

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If a vehicle travels with a velocity less than its maximum speed vmax , then the vehicle accelerates when passing a slow vehicle, and it accelerates by one within each discrete time step until reaching vmax and drives uniformly. If a vehicle intends to overtake a queue of slow vehicles, then the vehicle flashes to prevent the vehicles in the queue from overtaking and assure that the first vehicle of the queue. Therefore we consider overtaking as a process within a certain time period (several time steps). And the general overtaking process is accelerating to the left lane, passing a queue of slow vehicles at a constant speed, and then moving back to the right-most lane. To take a close look, we examine the following vehicle system. Provided there is no obstacle ahead a vehicle, then its velocity is equivalent to the number of sites that it advances in one update. The index x(i ) denotes the position of the i th vehicle, gap (i ) = x (i + 1) − x (i ) − 1 the width of the gap to the preceding vehicle. Obviously the value of gap(i ) is equivalent to the number of empty sites ahead in the same lane. Assume that at a certain time step t the i th vehicle intends to overtaking a queue of slow vehicles at a length of n , and its maximum overtaking sight distance is s , when the width gap between the first vehicle of the slow vehicle queue and the preceding vehicle is denoted as gap f (i ) . And we use the index l0 to denote the number of empty sites forward the i th vehicle in an adjacent lane. As is shown in Fig. 2, we derive that the minimum time step T1 the i th vehicle takes to passing the slow vehicle queue and the maximum time step T2 the i th vehicle takes to moving in the left lane. Therefore we deduce the mathematical form of the overtaking rules as v(i ) > gap(i ) and v(i) + 1 < vhope (i) indicating that the i th vehicle is blocked by the slow vehicle queue forward and intends to accelerate to its desired speed vhope (i ) . gap f (i) ≥ v(i + m)T1 + 1 indicating that enough space is assured forward the first vehicle in the slow vehicle queue T2 ≥ T1 indicating that enough space is assured in the left lane . If all the above three rules are fulfilled, then the i th vehicle performs overtaking with a probability p , and its velocity and position update when entering the left lane and driving in the left lane. Thus the updating step are v(i ) = min(v(i) + 1, vmax (i )) for speed update and x(i ) = x(i ) + v(i) for position update at each time step throughout the lane-changing process and overtaking process. When the vehicle reach the time step t + T1 , it exactly passes the first vehicle of the slow vehicle queue. When the return conditions are fulfilled, the vehicle return to its former lane, namely the right-most lane. The performance of the k-p rule

We first simulate the traffic flow characters under the keep-right-except-to-pass rule. In this situation, a driver must drive on the right-most lane and the overtaking process is confined to moving one lane to the left, passing, and returning to the right-most lane. Analysis for traffic flow. Fig. 3(a) depicts that when the random slowdown probability remains the same and the vehicle density approximately reaches the value 0.1, the traffic flow substantially reached the peak. And then, as the vehicle density increase, the traffic flow decreases. So is the average vehicle speed. This is because vehicles continue to accumulate as the vehicle density increases, resulting in the increase of overtaking, which adds to the traffic congestion and decreases the vehicle density. We conclude from Fig. 3 (b) that since the deceleration reduces the average speed thus decreasing the traffic flow, in lanes with the same vehicle density, the greater the random slowdown probability is, the smaller the traffic flow as well as the average vehicle speed is. The simulations are performed under assumption that T1 = T2 = 1 , for we are still exploring to generating the computer program to occasions when T1 , T2 represents a few time steps.

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(a) (b) Fig. 3 Four traffic flow-vehicle density diagrams and four vehicle velocity-vehicle density diagrams, each for a different random slowdown probability p ∈ {0.3,0.5,0.7,0.9}. Proposing substitution rules

From the above analysis of the k-p rule, we conclude that for a two-lane freeway, this rule mainly avails to eliminating traffic accidents, but weak in increasing traffic flow, since the utilization rate of the overtaking lane is very low. Thus we propose two alternative rules to increase the lane utilization and decrease the vehicle overtaking probability. The loose rule (Fig. 4) Drivers mostly drive in the right-most lane unless passing another vehicle, in which case they move one lane to the left, passing, and they may either return to the right-most lane or stay in the overtaking lane.

Fig. 4 The loose rule.

Fig. 5 The stay-in-lane rule. We propose this new rule aiming at promoting better traffic flow. The stay-in-lane rule (Fig. 5) The two traffic lanes are distinguished as the fast lane and the slow lane where fast moving vehicles and slow moving vehicles driving respectively. Each vehicle must choose a lane according to the speed limit and the vehicle performance. And no lane change or overtaking is permitted unless a traffic accident happens forward, in which case the vehicle drivers change lane at a safe speed within safe distance. We take into account the different velocity performance of the vehicles in this rule and decrease the crash rate by minimizing lane change.

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Comparison for the rules We obtain the following traffic flow-vehicle density diagrams of the three rules in order to make a comparison.

Fig. 6 The traffic flow-vehicle density diagrams: rule 1, 2 and rule 3 represents the k-p rule, the loose rule and the stay-in-lane rule respectively. We see that when the vehicle density as well as the overtaking frequency is low, the traffic flow result of rule 2 is close to that of rule 1, and when vehicle density increases, the traffic flow result of rule 3 also decreases. From the view of safety, rule 2 produces a relatively low overtaking frequency since vehicles may not return to the former lane after overtaking, which increases the use ratio of the overtaking lane. Therefore we consider the stay-in-lane rule performing the best in alleviate traffic congestion and decreasing traffic accidents. Conclusions For traffic flow problems with traffic rules, we first proposed a two-lane overtaking model based on lane-changing conditions, and then using the overtaking conditions and a cellular automata method to simulate the vehicle performance. We also obtained the relationship between average vehicle speed, traffic flow and vehicle density. Then we weighed between safety factor and traffic flow and obtained the comprehensive factor with a linear weighting method. We derived that a high speed limit decreases the safety factor and increases the traffic flow, while a low speed limit performs the opposite. Additionally, we compared the vehicle performance under the k-p rule and under no rule and concluded that the k-p rule is not capable to promote traffic flow. Thus we proposed two new rules and compared them with the k-p rule, and we derive that the stay-in-lane rule performs the best among the three rules. Literature References [1] Maerivoet S, De Moor B. Cellular automata models of road traffic, Physics Reports. 419(2005): 1-64. [2] Nagel K, Schreckenberg M.A cellular automaton model for freeway traffic, Journal de Physique I, 1992, 2(12): p.2221-2229. [3] Rickert M, Nagel K, Schreckenberg M, et al. Two lane traffic simulations using cellular automata, Physica A: Statistical Mechanics and its Applications, 231(4): p.534-550. (1996) [4]Gardner M. Mathematical games: The fantastic combinations of John Conway’s new solitaire game ‘life’ Scientific American, 1970, 223(4): p.120-123.

Applied Mechanics and Materials Vol. 721 (2015) pp 52-55 © (2015) Trans Tech Publications, Switzerland doi:10.4028/www.scientific.net/AMM.721.52

Submitted: 04.11.2014 Accepted: 10.11.2014

The overall structure of IOV and the research of the unsolved key technology of IOV in intelligent transportation system Fan Li1, 2,a, Yang Jian2,b and Zhao Ran1, c 1

School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan 430070, China; 2

Air Force Airborne Academy, Guilin 541003, China.

a

b

c

[email protected], [email protected], [email protected]

Keywords: Intelligent transportation; Internet of Vehicles; Overall structural design; Key technology

Abstract. Internet of Vehicles is the basis of the application of intelligent transportation system .This article has carried on the analysis and discussion about the concept of IOV, overall structure and the key technologies which are desperately need to be solved to promote application and development of IOV system. Introductions While bringing convenient to people’s life, vehicles also brought some problems seriously impact the urban life, especially the traffic congestion. Using advanced information technology means to improve traffic conditions is one of the effective measures currently. On the one hand, using the network technology to connect all vehicles can Integrate and share the real-time traffic flow and traffic rate information, which can guide the vehicles based on the traffic condition with the purpose of fully excavate the potential of existing roads to improve the efficiency of traffic and road safety, reduce the high cost due to congestion or slow travel of people and the high automobile exhaust emissions; on the other hand, releasing the location of the parking lot and the current spot information to the vehicles terminal by Internet of Vehicles technology can reducing the congestion problems due the drivers’ slow cruising and disorderly parking in order to find parking spots. Concepts of IOV system At present, the traffic problem in urban is mainly the road congestion and road safety. It is mainly use the vehicle management and reasonable allocation that to solve the congestion problem, namely to establish information system based on car as node - intelligent transportation system based on Internet of Vehicles. The basis of intelligent transportation is the Internet of Vehicles. “Networking” is the synthesis of the existing electronic information technology. Each car is an information node and connect to the intelligent transportation network through wireless communication means, thus realizes the extraction and effective utilization of all attribute information of the vehicle and static and dynamic information on information network platform, and provides effective supervision and integrated services according to the different functional requirements of all the vehicles’ running condition, achieves the exchange of information between a car and another car, vehicles and road set. That will achieve the connectivity of people-car-road and a more intelligent, safer driving experience. System architecture of IOV system IOV system is the embodiment of the Internet of things technology’s application in the field of intelligent transportation. IOV system is an essentially part of the Internet of things. Based on the architecture of current Internet of things and the functions that IOV system needed to realize, the architecture of IOV system can be divided into the perception layer, network layer and application layer, which is shown in figure 1.

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Figure 1 IOV system architecture Perception layer. Perception layer is IOV’s nerve endings, the source of all kinds of information. These sensors can provide vehicles’ state information, the related information of the goods they are transporting, the state information of the traffic network they are driving in and the path environmental information based on geographic information system, etc. IOV perception layer's main functions are the following several aspects: one is using RFID technology for the identification of network nodes, the second one is using a variety of sensors to percept and collect all the data of the people - car - road (such as: the intention of driving, the vehicle location, speed, road traffic, weather conditions, etc.), the third one is using the on-board network technology to network all kinds of sensor, realize the data sharing and information fusion. Network layer. Network layer is the nerve center and brain of the IOV system, its main function is to transmit and process the information obtained from perception layer. Use wireless communication technology to realize the connectivity between nodes and docking with the Internet, complete the analysis and processing of the data and information and transport information over long distance. The bottom protocol used by the IOV system is different from TCP/IP network protocol, so it needs network protocol conversion. Application layer. The application layer mainly dock with the other subsystems, according to the needs of different users provide different applications. The Specific functions include data processing, defining the ways of user interactions and realizing the man-machine Interaction. Specific services the IOV system provided are also defined and implemented in this sub-layer. Such as: vehicle running status monitoring, rapid processing of road traffic accident, emergency rescue and dynamic traffic inducing, persuasions and guidance for traffic and parking congestion in the city road and the key vehicle security escort and dangerous goods transportation monitoring, etc. The application layer use equipment including the servers of network, traffic management and monitoring system, the on-board computer for drivers, car servicers’ network service system, etc. Network architecture of IOV system IOV’s network architecture is mainly composed of vehicle-vehicle communication, vehicle-road communication and vehicle-control center communication. The actual architecture is shown in figure 2. Vehicles use the on-board unit, units on adjacent vehicles and fixed installations of roadside unit for ad-hoc network communication. On-board unit mainly includes vehicle's running status information collection module, positioning and navigation module, network communication module and the input/output module, etc. Roadside unit is mainly set up near the road, on the one hand, it will send the network traffic manage and control center’s information and traffic control command to the nearby vehicle; on the other hand, it will also upload the nearby vehicles’ running status information to the traffic control center. On-board unit and intelligent traffic control center also can use the mobile cellular networks for communication, directly receive vehicles’ dynamic information and send traffic management instructions. Control center will also summary and processing the acquired vehicle information in its manage area and then selectively provide the data to other services unit, help the

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service units and other vehicle information services to work. The car drivers and passengers can also use smart phones or other terminals to connect with the vehicle and roadside units, acquire the required vehicles and traffic information.

Figure 2 IOV’s network architecture Key technologies to be solved of IOV system IOV system is network between people, vehicles and road; it is a very large technical system. There are many technical and developing problems in application of IOV need to be solved; these problems involve each layer of the IOV architecture. Radio frequency identification technology (RFID).Radio frequency identification (RFID) is a non-contact automatic identification technology, it use radio frequency signal automatically identify targeted objects and access to relevant data without human intervention. RFID applied in IOV system's main advantage is that the technique can identify the high-speed movement of multiple objects; thus can easily realize data transmission between the nodes in the IOV. The active RFID technology can provide distance speaking, reading and writing services and can realize active perception. It is suitable for IOV. Middleware technology. Middleware technology is the core technology of the current software research and development. RFID middleware is an intermediate process to implement the data filtering and data format conversion between system hardware devices and application. All kinds of data read-write device can read importing car networking applications after the process of middleware extraction, decryption, filtration, format conversion and eventually react on the user interface for the need of IOV users. For different applications of IOV, providers can develop different middleware, such as vehicle routing guide middleware, emergency middleware and traffic information management middleware, etc. Each middleware development needs the reference of IOV application service requirements and standards. The middleware technology reduces the difficulty of the application development, improve the development efficiency. Protocols development. Protocol development should also refer to the thought of the OSI network layer, based on vehicle networking architecture and be discussed one layer by another. Research and development of new protocol should bring in the latest research results currently, combine the actual characteristics of IOV at the same time and pay attention to the efficiency of the agreement. IOV also need access to the Internet, thus would require the study of the problem of IOV protocol conversion makes the data from IOV interflow with the data of the Internet. IOV, of course, also including the network protocol of network controls, data security transmission, etc.

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Intellectual technology. IOV should be a new type of intelligent network. using of intelligent technology can make the vehicles have some intelligent and be able to actively perceive the changing of road environment and real-time traffic conditions and even to predict the drivers’ next operation requirements, etc. The main content of intelligent technology research includes the artificial intelligence theory, the intelligent control system, the signal processing and identification and the information fusion, etc. Safety reliability. Building a safe and reliable Internet application system is a hot and difficult technology to research at present. Safety and reliability will determine the IOV’s popularization. IOV’s openness, inclusiveness and anonymity bring some inevitable hidden safe trouble. Due to the fast moving of vehicles, changing of driving environmental and frequent access of each node, the car networking application environment is complex and particular, thus puts forward high requirements on its reliability .IOV Should also have the functions of defending network attacking, protecting personal privacy and ensuring a fast data transformation. Conclusions Internet of Vehicles is a kind of brand-new concept, it is a new network applications of Internet of things technology in the field of intelligent transportation, the core of a new generation intelligent transportation system and have broad application prospects and commercial value. Hope this article can provide some useful ideas for further study of Internet of Vehicles. References [1] H. Xie, D.C. Dong and D. X. Ou: A New Generation of Intelligent Transportation based on the Internet of Things. Technology & Economy in Areas of Communications, (2011) No. 13(1), p.33-36, 46. (In Chinese). [2] Z. F. Gu: Vehicle Networking System Architecture and Its Core Technologies (MS, Nanjing University of Posts and Telecommunications, China 2009). (In Chinese) [3] J. Q. Wang, C. W. Wu and X.J.li: Research on Architecture and Key Technologies of Internet of Vehicles. Control & Automation, Vol.27 (2011) No.4, p.156-158,130. (In Chinese) [4] H. X. Zhou: Research on Traffic Information Collection and Processing Methods in Internet of Vehicles (Ph.D., Jilin University, China2013). (In Chinese)

Applied Mechanics and Materials Vol. 721 (2015) pp 56-61 © (2015) Trans Tech Publications, Switzerland doi:10.4028/www.scientific.net/AMM.721.56

Submitted: 10.11.2014 Accepted: 11.11.2014

Vehicle Routing Problem Based on Heuristic Artificial Fish School Algorithm Lei Qin1, 2, a, Yaqin Li1, b and Kang Zhou1, c 1

School of mathematics and computer, Wuhan Polytechnic University, Wuhan 430023, China;

2

School of automation, Huazhong University of Science and Technology, Wuhan 430074, China a

b

c

[email protected], [email protected], [email protected]

Keywords: Vehicle Routing Problem, Artificial Fish School Algorithm, coding

Abstract. Vehicle Routing Problem (VRP) is one of the core issue of logistics distribution, for traditional precision algorithms and heuristic algorithms had low accuracies or easily fell into local optimal solutions, it was difficult to obtain the optimal solution. This paper proposes a heuristic artificial fish school algorithm (HAFSA) for VRP, firstly, three-dimensional particle coding method is applied to vehicle routing code, and infeasible and inadequate artificial fish coding for heuristic repair, secondly HAFSA steps are given, finally the algorithm is tested through a simulative example. The experimental results show that compared with traditional genetic algorithm (GA) and particle swarm optimization (PSO), AFSA and their extension algorithms, HAFSA has a better performance in time and space cost and convergence. Introduction The choice of logistics distribution route is the key link of the logistics distribution network optimization. Rational planning distribution route to the influence of the distribution costs is much larger than normal transport, so people must be on the basis of the comprehensive plan, formulate efficient transportation routes, choose the reasonable transportation means and tools. Vehicle Routing Problem (VRP), its definition is, for a series of need access to the delivery point and receiving point, formulate reasonable running lines, make the vehicles in a certain order through them. In satisfying the constraints, such as the demands for goods, vehicle capacity limits, driving mileage limits and time limits of the vehicles, reach a goal, such as the shortest mileage, the least cost, using the least amount of vehicles, etc [1]. Since the VRP is a NP problem on simplified logistics distribution routes choice, after it was put forward, a lot of algorithms were proposed to solve it, such as genetic algorithm (GA), ant colony algorithm (ACO), particle swarm optimization (PSO) and so on [2-6]. The algorithms can successfully solve VRP, however, GA encoding is more complex, has stronger dependence on the selection of initial population and slow search speed. ACO is easily affected by parameters, has large amount of calculation and easily falls into local optimal solutions, the characteristics of the PSO is prone to premature convergence. AFSA is a species intelligent algorithm, has a good ability to get the global extremum. It is not sensitive to the selection of parameters and initial values, has better adaptive ability, and it is a kind of efficient, parallel, adaptive global search algorithm. Since it was put forward it has achieved great success in combinatorial optimization problems [7-8]. In this paper, in order to avoid the defects of traditional algorithms for VRP, in addition considering the good performance of AFSA for combination optimization problems, using heuristic rules, a heuristic artificial fish algorithm (HAFSA) for VRP is proposed, and combined with the example, HAFSA is verified, the experiment results show that compared with GA, PSO, AFSA and their extension algorithms for VRP, the search time and precision of HAFSA can be greatly ascend.

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The mathematical model of VRP VRP is generally described as follows, deliver goods with multiple vehicles from the distribution center to the receiving points, the position and demand of each receiving point is certain and the capacity of each vehicle is certain, request reasonable arrangement of each vehicle distribution route, to achieve the optimal objective function and satisfy the following conditions: (1) the sum of demand of all receiving points in each distribution path is no more than the biggest load of the distribution vehicle; (2) the demand of each receiving point must be met, and the delivery is only by a vehicle. Suppose there is a distribution center, have M vehicles, and capacity of qk (k = 1, 2, , M ) respectively, there are N receiving points need to transport goods, they need the freight volume of g i } ≤ max{qk } , to minimize the total mileage of the distribution gi (i = 1, 2, , N ) respectively, max{ 1≤i ≤ N 1≤ k ≤ M vehicles as the objective function. For the sake of coding, points 1, 2, , N indicate N receiving points, point N+1 indicates the distribution center, define the variables are as follows: 1 vehicle k travels from point i to j xijk = otherwise 0 1 The freight task of point i is completed by vehicle k yki = otherwise 0

cij is the cost from point i to point j, generally refers to the distance from point i to point j, Z is the

total traveled mileage of all vehicles, the mathematical model of VRP is expressed as follows: N +1 N +1 M

min Z = ∑∑∑ cij xijk

(1)

i =1 j =1 k =1

N

∑g y

s.t.

i

ki

≤ qk , k = 1, 2, , M

(2)

i =1

M

∑y

ki

= 1, i = 1, 2, , N

(3)

k =1

N +1

∑x

ijk

= ykj , j = 1, 2,, N ; k = 1, 2,, M

(4)

ijk

= yki , i = 1,2,, N; k = 1,2,, M

(5)

i =1

N +1

∑x j =1

xijk = 0 or 1, i, j = 1, 2, , N + 1; k = 1,2,, M

yki = 0 or 1, i = 1, 2, , N ; k = 1, 2, , M

(6) (7)

AFSA The basic definitions. The individual state of an artificial fish can be expressed as the vector X = ( x1 , x2 , , xn ) , in which xi (i = 1, 2,, n) as the optimization variable, the food concentration of an artificial fish in the current location is represented as Y = f ( X ) , in which Y is the objective function value, the distance between artificial fish individuals X i and X j is expressed as dij = X i − X j , N is the size of the artificial fish school, visual is defined as the perception distance of an artificial fish vision, step is the biggest step length of an artificial fish moving, δ is the crowding factor, 0 < δ < 1 , and Try_number is the biggest test number of an artificial fish for food. The descriptions of the behaviors. AFSA mainly consists of several kinds of behavior, such as foraging, clustering, tailgating, etc, moreover bulletin board is used to record the condition of the optimal artificial fish individual. When each individual is in the process of optimization, the artificial fish is testing its own state and the state of the bulletin board after each action, if his state is superior to the state of the bulletin board, the state of the bulletin board will be updated, and make the bulletin board record the optimal state of the artificial fish school.

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The choice of behaviors. According to the nature of the problem to be solved, make a judgment about the current environment of the artificial fish, so as to choose a kind of behavior. This evaluation method is to select the optimal behavior, namely test clustering and tailgating, etc, and then evaluate the results of behaviors, choose the best behavior of them to perform it. The process of the algorithm. Artificial fish algorithm is shown in figure 1.

Fig.1 AFSA flow chart HAFSA for VRP Artificial fish coding method. In this paper, the three-dimensional particle coding method from [6] is used to construct artificial fish coding. Suppose there is a distribution center, N receiving points, M vehicles, with a 1× 2N d vector to define each of the artificial fish. The former N elements of a vector show that the task of each receiving point is completed by which vehicle, the latter N elements of a vector show vehicle driving routes. To make vehicle number between 1 and M after decoding, the paper limits each element aij of a vector ai to satisfy the condition aij ∈ [1, M + 1) . For example, there is a distribution center, 3 vehicles, 10 receiving points of VRP, receiving points are expressed by point 1, 2, …, 10, the distribution center is expressed by point 11, the ith artificial fish coding is shown in table 1. Table 1 Artificial fish coding example j

1

2

3

4

5

6

7 1.23

8 2.94

9 2.56

aij

3.89

2.36

1.35

3.69

1.26

3.35

j aij

11 1.83

12 2.56

13 1.26

14 2.09

15 3.49

16 3.21

10 1.48

17 2.41

18 1.56

19 3.69

20 2.14

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Artificial fish decoding method. 1) When the former N elements aij of an artificial fish have been INT, that is int(aij ) , the vehicle k that assigned to point j is confirmed. 2) According to the size of the latter code N elements to determine the vehicle paths, that is, first find the points that completed distribution by vehicle k, then according to the size of the latter aij corresponding j , sort aij a lowest-highest order and determine the path of vehicle k, the coding in table 1 after integer is shown in table 2. Table 2 Artificial fish coding after INT j

1

2

3

4

5

6

7

8

9

10

aij j aij

3 11 1.83

2 12 2.56

1 13 1.26

3 14 2.09

1 15 3.49

3 16 3.21

1 17 2.41

2 18 1.56

2 19 3.69

1 20 2.14

The former N elements of the integer coding refer to the different number of N vehicles, such as vehicle 2, the corresponding task points are points 2, 8, 9, which indicates that the task of points 2, 8, 9 is completed by vehicle 2. The corresponding latter N elements are 2.56, 1.56, 3.69, since the ascending order is 1.56 < 2.56 < 3.69, the path of vehicle 2 is point 8 → point 2 → point 9, and because each vehicle sets out from the distribution center and then back to the distribution center, so the full path of the vehicle 2 is point 11 → point 8 → point 2 → point 9 → point 11. After decoding all paths corresponds to the artificial fish in table 2 are as follows, vehicle 1, point 11 → point 3 → point 10 → point 7 → point 5 → point 11, vehicle 2, point 11 → point 8 → point 2 → point 9 → point 11, vehicle 3, point 11 → point 1 → point 4 → point 6 → point 11. Infeasible, inadequate artificial fish coding and its heuristic repair. Infeasible artificial fish coding. In the execution process of AFSA, sometimes there are infeasible or inadequate artificial fish coding, infeasible artificial fish coding is defined as artificial fish coding that doesn't meet the constraint conditions of VRP after decoding, such as the sum of the required freight of the receiving points in a path is beyond the maximum load of the vehicle in the path, or there are idle vehicles, etc. Infeasible artificial fish coding’s heuristic repair. For infeasible artificial fish coding, heuristic repair strategy in the paper is, first the receiving points in the infeasible path of infeasible artificial fish coding are sorted according to the ascending order of the required freight volume, every time remove the front of receiving points of the permutation, on the premise of feasible paths are still feasible, plug it into the back of its nearest receiving point of feasible paths, until all infeasible paths become feasible paths, the removing and plugging terminate. Heuristic repair strategies are from the following two points, one, each vehicle path should make full use of the capacity of the vehicle, namely maximize the carrying amount of the vehicle as far as possible under the premise of not more than its maximum load, so, when removing the receiving points on the infeasible vehicle path, heuristic rules require to remove the smallest volume receiving points out each time, until the path becomes a feasible path; two, each removed receiving point on the infeasible vehicle path should be inserted into the back of its the most adjacent receiving point in all feasible paths, this way can add the minimum cost to the inserted feasible path. When the infeasible paths of the infeasible coding have become the feasible paths through repair, a randomized method maps the infeasible coding to the feasible coding. The Heuristic repair of the infeasible artificial fish coding can avoid the generation of invalid or infeasible coding during initialization and iteration, generate better artificial fish coding, thus HAFSA algorithm is faster and more effective for the solution of VRP, which will be reflected in the subsequent experiment. Inadequate artificial fish coding. Inadequate artificial fish coding is defined as feasible artificial fish coding that does not make full use of all vehicles. Inadequate artificial fish coding’s heuristic repair. For infeasible artificial fish coding, the latter N elements of an Integer inadequate artificial fish coding represent the paths of all vehicles, firstly sort

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them a lowest-highest order, and then amend the former N elements of the sorted inadequate artificial fish coding, for the elements represent the task of each receiving points is completed by which car, this amending will result in all vehicles involve in the delivery. HAFSA algorithm steps for VRP 1) The initialization of AFSA and VRP parameters. Set up the basic parameters of AFSA and the corresponding parameters of VRP, such as the size of the artificial fish, the perception distance of the artificial fish, moving step length, crowding factor, the number of the vehicles and their load, the positions of the receiving points and their required volumes, etc. 2) Initializes the artificial fish. 3) Do the improved clustering and tailgating behavior for each artificial fish, the default behavior is foraging behavior, and the heuristic repair strategies are applied to the coding after the execution of behavior, makes it become the adequate and feasible artificial fish coding; 4) The artificial fish coding generated from 3) are decoded and calculate the total mileage of all vehicles, select the optimal behavior execution and generate the new artificial fish. 5) Update the bulletin board. 6) Determine whether the iterations number has reached the maximum value, if so, then output; if not, go to 3). The experimental results and analysis To verify the HAFSA for VRP, this paper selects the example 2 in [6] as the experimental example, there are one distribution center and eight receiving points, two vehicles, the maximum load of each vehicle is eight, the required distribution of each receiving point is respectively 1, 2, 1, 2, 1, 4, 2, 2, the distances between the receiving points and the distribution center can be seen in table 5, the receiving point 1-8 is respectively expressed by points 1-8, the distribution center corresponding to point 9, the minimum cost corresponding to the optimal scheduling scheme of the problem is 67.5. Using basic AFSA algorithm and HAFSA algorithm to calculate, the parameters of the two algorithms are set the same, that is the size of the artificial fish N =20, artificial fish vision visual=2.5, the large moving step length step=1, crowding factor δ =0.618, the biggest probing number of an artificial fish foraging Try_number=5, the iterations number of each experiment is 40. For better detect robustness of the algorithm, repeat the experiment 20 times. In addition, GA, DPGA and PSO are used to compare the merits of the HAFSA, experimental results are shown in table 3 below, in which the average computation time in seconds. Table 3 20 experimental results (Each iteration number is 40.) Algorithm

Average

AFSA HAFSA

68.775 67.5

Maximum 70 67.5

Minimum 67.5 67.5

The number of the optimal solutions is successfully obtained. 7 20

Each run time (s) 5.16 2.36

As can see from table 3, HAFSA greatly exceeds AFSA both in search results and search time. In the 20 experiments HAFSA can find the optimal solution in every time, while the optimum solution can be found in only 7 times experiments for AFSA, most experiments failed to find the optimal solution and the convergence was poor. AFSA often carried out invalid search in solving, each experiment running time for 5.16 seconds, which is more than double the time 2.36 seconds of HAFSA, when reaching the optimal solution the distribution of the vehicle paths are as follows: vehicle 1, distribution center → receiving point 2 → receiving point 8 → receiving point 5 → receiving point 3 → receiving point 1 → distribution center, vehicle 2, distribution center → receiving point 6 → receiving point 7 → receiving point 4 → distribution center. Compared to the basic GA, DPGA and PSO, HAFSA's advantage is more apparent, which can be seen in table 4.

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Table 4 20 experimental results (Each iteration number is 20.) Algorithm Average Maximum Minimum GA DPGA PSO AFSA HAFSA

73.25 71.35 70.175 69.55 67.5

75.5 75.7 72.0 70.5 67.5

69.0 67.5 67.5 67.5 67.5

The number of the optimal solutions is successfully obtained. 0 2 2 2 13

When only 20 iterations for each experiment, GA, DPGA, PSO, AFSA are difficult to find the optimal solution, but HAFSA can find the optimal solution in most case. The subsequent experiments show that when the number of iterations increases to 200, the number of PSO and AFSA to find the optimal solution can reach respectively 16 and 19 times in the 20 times experiments, while table 3 shows HAFSA can reach 100% correct rate when the number of iterations is only 40, and the computing time and space cost is much lower than PSO and AFSA. Conclusion This paper proposes a heuristic artificial fish algorithm for VRP, HAFSA uses three dimensional particle coding method to construct artificial fish coding, the infeasible and inadequate artificial fish coding have been repaired, finally seek the optimal solution. The experimental results show that HAFSA can find the optimal solution with the 100% probability for small-scale VRP, compared with GA, PSO and AFSA, etc, HAFSA shows a better convergence and stability, and each experiment in just 2.36 seconds, which is far lower than AFSA 5.16 seconds. In the paper only small-scale VRP problems are solved, how to apply HAFSA in large-scale VRP problems will be the research focus in the future. References [1] G. Laport: The vehicle routing problem: An overview of exact and approximate algorithms, European Journal of Operational Research, vol. 59 (1992) no.4, p. 345-358. [2] Y.W. Zhao, B. Wu, L. Jiang, et al: Double populations genetic algorithm for Vehicle Routing Problem, Computer Integrated Manufacturing Systems, vol. 10 (2004) No.3, p. 303-306. [3] B.M. Baker, M.A. Ayechew: A genetic algorithm for the vehicle routing problem. Computers and Operations Research, vol. 30 (2003) no. 5, p. 787-800. [4] B. Ombuki, B.J. Ross, F. Hanshar: Multi-Objective Genetic Algorithms for Vehicle Routing Problem with Time Windows, Applied Intelligence, vol. 24 (2006) no. 1, p. 17-30. [5] B. Bullnheimer, R.F. Hartl, C. Strauss: An improved ant system algorithm for the vehicle routing problem, Annals of Operation Research, vol. 89(1999) no. 13, p. 319-328. [6] Z.X. Liu: Vehicle scheduling optimization in logistics distribution based on particle swarm optimization algorithm, Journal of Wuhan University of Science and Technology (Natural science edition), vol. 32 (2009) no.6, p. 615-618. [7] X.L. Li, Z.J. Shao, J.X. Qian: An Optimizing Method Based on Autonomous Animats: Fish-swarm Algorithm, Systems Engineering Theory and Practice, vol. 22 (2002) no. 11, p. 32-38. [8] X.L. Li, F. Lu, G.H. Tian, et al: Applications of Artificial fish school algorithm in Combinatorial optimization problems, Journal of Shandong University (Engineering Science), vol. 34 (2004) no.5, p. 64-67.

Applied Mechanics and Materials Vol. 721 (2015) pp 62-65 © (2015) Trans Tech Publications, Switzerland doi:10.4028/www.scientific.net/AMM.721.62

Submitted: 07.10.2014 Accepted: 13.10.2014

An improved Cellular Automaton Model of Traffic Regulations and the Effect of Deceleration Probability Luchao Han North China Electric Power University, Changping, Beijing, China [email protected] Keywords: traffic, cellular automata, VDR model, stochastic factors.

Abstract. In order to evaluate the keep-right-except-to-pass traffic rule, which is in effect in most countries, a suitable mathematical model is necessary. With the help the following model, analysts can objectively evaluate the effects and benefits of various driving regulations and rules. We formalize the situation in which automobiles are being driven on a multi-lane freeway as a process of cellular automata (CA). Following the methods used in the studies of Kerner and Barlovic and using the NaSch model, we formalize the keep-right-except-to-pass rule into mathematical form. We set the driving strategy by adopting the Velocity-Dependent-Randomization (VDR) model, considering the driver’s psychological state with respect to his desire to slow down behind, or to pass, another automobile. Adding stochastic factors during the analysis of the driving process, we examined mathematically the effects of traffic flow, the utilization of lanes, and transportation safety which mostly depends on the frequency of lane changes, to evaluate the keep-right rule. Introduction As living standards improve in most parts of the earth, how to improve the freeway traffic flow and to ensure the safety of driving cause the attention of more and more people. Nowadays the United States of America, China and most of the other countries follow the rule of driving automobiles on the right, where multi-lane freeways often employ a rule that requires drivers to drive in the right-most lane unless they are passing another automobile, in which case they move one lane to the left, pass, and return to their former travel lane. However, some countries such as Britain, Australia and some countries that once being British colonials follow the rule of driving automobiles on the left. Under the guidance of the traditional model of traffic flow, using traffic flow to judge the traffic condition is a good aspect, but in real life the problems of traffic flow have high degree of randomness, complexity and dynamic characteristics in time and space. The traditional model cannot explain traffic-flow, with three phases: free flow, wide moving jams and synchronized flow as well as other strange phenomenon such as meta-stable states, hysteresis effect and platoon dispersion very well [1]. Cremer did researches on the traffic flow with the thought of CA to reveal the main regulations of the system [2].Based on the study of cellular automata traffic flow model, research can be solved with the advantage of the discrete state variables about time and space, i.e. it is suitable for computer implement further updates. We refer to use the modified Velocity-Dependent-Randomization model upon the automobile conditions of acceleration, deceleration and braking on the single lane. Making artificial factors which can make great influence not omitted, we simplify the prerequisite for the acceleration and deceleration, and take the psychological condition of drivers during the driving process as well as the keep-right-except-to-pass rule into consideration. The purpose is to get curves of the traffic flow, lane changing frequency and the lane utilization ratio varied with density under the different traffic load conditions on a certain distance of freeway, after establishing the mathematical model. And then simulate some cases on computer, obtaining and analyze the results of making this rule on traffic. At last, we make the rule better on traffic, and consider the possible impact of the left-driving rule and adding the intelligent system at the final stage.

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Assumptions Using the for transportation model as basis. (1) Simulate one certain lane to a physical model Divide each lane into lattices in the same size, and each lattice is either occupied by an automobile or is empty, as Fig.1 shown. Each automobile just occupy one lattice, numbered in the driving direction and the position of their lane from right to left (or from left to right if the rule is keep-left-except-to-pass) with i and j. Space, time and velocity are discrete, and the clock steps forward. (2) The opposite directions on the freeway can be concluded as one case. Choose one direction to research for simplify the model. (3) If one automobile want to overtake, the lane changing process is: Move to the lattice next to it first, and then go on running on that lane. The process of changing to the next lane is not included into the calculation of time due to the short distance. (4) We can set the rules to avoid the traffic accidents when automobiles all driving on the same lane, therefore the traffic accidents mostly happen on the condition that overtaking other automobiles.

Fig. 1 Physical model Establishment of the model on two lanes Because using the Cellular Automata model is according to operate the rules, which is defined by different cases, over and over again for each time until the final conditions. First we should formulate the rules for the lane-change when drivers would like to overtake the car ahead. We suppose that drivers take the overtaking for the first consideration, which follow most drivers’ will to chase for higher speeds, and that the lowest and highest limit speeds of each road are the same. The rules of lane-change are described as follows: Let denote the desire velocity of the moving and the gap for automobile. When the gap between two automobiles ( ) is smaller than both the left lattice paralleled ( ), and the gap between the moving automobile and the automobile behind ( ) is not smaller than the velocity of the nearest automobile behind ( ), it is the time for the drivers to change to the left lane in order to overtake other automobiles with possibility P. Yet, when the automobile which changed the lane for once has no chance to pass over the leading automobile (if it is possible), this automobile should make it as soon as possible to drive back to the former lane. And the rules for automobiles returning to their former lane are stated as follows: When the gap for the right lattice paralleled ( ) is not smaller than the present velocity of the moving automobile ( ), and the gap between the right lattice paralleled and the nearest automobile behind ( ) is not smaller than the velocity of the nearest automobile behind ( ), it is the time for the drivers to return to the former lane [3]. The adjustment of influence from drivers’ subjective factors: From the findings of actual survey, after the rules of lane-change are satisfied, the cars choose to change lane, but some coaches or other big buses are more likely to give up the opportunity to change lane [4]. This model regards different kinds of transportations as the same cell, therefore we add correction factor of drivers’ subjective factor which is defined as 0.9 to our model. We have the following set of rules, which are updated in parallel for all cars. (1) If the car meets the following conditions, it can change lane to the left. (2)

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(3) (4) (5) (6) Then considering the rules of automobiles running on the same lane, we integrate the achievements of Nagel and other people optimizing and improving the NaSch model [5], and use the approach for driving strategy which is put forward by Knospe for reference [6]. The driving strategy is: The automobiles move (apart from fluctuations) with their desired velocity Vmax at large distances; drivers react to velocity changes of the next automobile downstream at intermediate distances, i.e. to ‘brake lights’; the drivers adjust their velocity such that safe driving is possible at small distances; The acceleration is delayed for standing automobiles and directly after braking events. Then the update rules are as follows, which are condsidered after the rules for the lane-change: Determination of the randomization parameter: (7) (1) Acceleration: When it meets either

,

or (8)

(2) NaSch braking rule: (9) We define , if (3) Randomization, braking: (10) (4) Car motion: (11) The simulation In Fig.2 we can see that the mean velocity increases slowly at first and slows down with the density. With density adding up to 0.2, the mean velocity starts to rapid decreases. The flow f(x) increases before the density gets to 0.1. But when the density approaches 0.15, the flow slow down and continue declining with the density, which is shown in Fig.3. So we can know when the density adds up to a high level the flow rapidly decreases to a low level by this rule.

Fig.2 Velocity with density

Fig. 3 Flow with density

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To know the reason why the flow decreases we simulate the relations between the lane usage and the density. Through the Fig.4 we can see the maximum flow on the right lane is smaller than the maximum flow on the left lane at the first stage, which is in agreement with the data. It seems that the road breaks down on the right lane first, while it stays stable on the left lane, which is likely due to the too lower maximum velocities on the right lane. And because the drivers must drive in the right-most lane unless they are passing another automobile, and when they move to the left, they must return to their former travel lane. So most cars are driving on the right-lane, we can see that point in the Fig.4 obviously.

Fig.4 Lane-usage Summary Applying this model produces a computer simulation operating in the virtual system of cellular automata. The computer, simulating the case of two-lane flow of traffic in this model, shows that the keep-right rule is not optimal but can be improved by raising the mean velocity while controlling the maximum velocity. Therefore we propose a modified rule: continue at increased velocity after overtaking to cut down the frequency of lane-changing and moving to the right lane when your automobile is becoming an “obstacle.” References [1] Kerner B S. Traffic flow: Experiment and Theory. Schreckenberg M.Traffic and granular flow’ 97. Heidelberg, 1998, p.239-267. [2] Cremer M, Ludwig J. A fast simulation model for traffic flow on the basis of Boolean operations. Mathematics and Computers in Simulation, 1986, 28:297-303. [3] Lin Peng, Huili Tan, Lingjiang Kong, Muren Liu. A study on an improved Nagel-Schreckenberg traffic flow model with open boundary conditions. 2003 Acta Physica Sinica, 2002, 12(12): 2713-2718. [4] Jinqing Xu, Zhushi Chen, Yi Ding. A Model Study and Simulation of Lane Changing Based on Driving Behaviors. 2011 Journal of East China Jiaotong University, 2012, 28(6):68-72. [5] Nagel K, Schreckenberg M. A cellular automaton model for freeway traffic. Journal De physique I, 1992, 2(12):2221-2229. [6] Knospe W, Santen L, Schadschneider A, et al. Towards a realistic microscopic description of highway traffic. Journal of Physics A, 2000, 33:477-485.

Applied Mechanics and Materials Vol. 721 (2015) pp 66-69 © (2015) Trans Tech Publications, Switzerland doi:10.4028/www.scientific.net/AMM.721.66

Submitted: 13.10.2014 Accepted: 22.10.2014

Smoke Management System Design for Airport Terminal Liubing Wang a, Shuping Zhang Xi’an University of Architecture Technology, 710055, China a

[email protected]

Keywords: Open Cabin; Fuel Island; Mechanical smoke extraction system.

Abstract. “Open Cabin” includes a solid roof which has enough fire resistance, covers over the entire region whose fire load is relatively high, such as commercial building with a large area. Performance design using “Open Cabin” concept of big space, is to protect areas whose fire load is relatively high from fire using the local fire protection measures (automatic alarm system, automatic sprinkler system, fire compartment and smoke resistance facilities), in order to make up fire protection measures which cannot be set in full range within big space. Then there is no need to separate big space physically for limiting the spread of fire and smoke. Introduction Waiting room of terminal is usually equipped with retail stores, bookstores and restaurants etc.; they commonly deal in daily necessities, books and foods, so fire load is relatively concentrated, and fire risk is bigger. In daily operation process, these store entrances need to maintain open for shopping convenience. The international concourse needs open and large space, reduce partition for strengthening visual guidance, and adapts tall space with good horizontal and vertical connectivity to ensure efficient passenger traffic. It’s hard to set fire compartment in accordance with the conventional requirements, resulting in a contradiction between fire protection design and the current code. In addition, the waiting room of terminal is also equipped with offices, equipment room, VIP lounge etc. Once the fire rises, the fire and smoke can quickly spread out of the shop, become more dangerous to terminal, so it needs key protection. Setting requirements of “Open Cabin” or “Fuel Island”. (1) When design according to the concept of “Open Cabin”: a) “Open Cabin” should use the ceiling with fire resistance limit no less than 1.5h, and does not require surrounded by partition for fire protection; b) The top uses fixed or automatically falling typed hanging wall with fire resistance limit no less than 1h to form a cigarette storing chamber; the effective depth of hanging wall is determined according to the internal height and corresponding clear height of “Open Cabin” and should not be less than 1m; c) The setting of automatic fire alarm and automatic sprinkler system should be in accordance with the requirements of related codes; d) The natural smoke exhaust port is set in the “Open Cabin” bulkhead with no less than 3.5% of ground area, and consistent with the exhaust port on direction in the great hall; natural ventilation is through tuyere below hanging wall, and there is no need to set the mechanical ventilation system; e) The area of “Open Cabin” is recommended to be controlled within 300m2, the cabin decoration materials should meet the requirements of “Code for Fire Protection Design of Building Interior Decoration”. Fireproof distance between cabins shall not be less than 8.0m; If the area of “Open Cabin” is more than 300m2, fire partition no less than 2h should be set between adjacent shops(both end of the partition extend 1m); (2) If small business stalls are set up, they can use the concept of “Fuel Island” to design, and to determine the fire separation distance between fixed or mobile combustible. Control the scale,

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business stalls should be controlled in a certain area, suggested area should not be over 20m2, the separation distance should not be less than 5m, and the stalls should not be located near evacuation exits, so as to avoid affecting the smooth of evacuation exits when fire rises. 2. Business and other functional places are all required to install automatic sprinkling fire control system and mechanical smoke extraction system, the goal of the design is to effectively control the development of early fire spread, maintain the smoke layer at a determined height, and make the smoke does not overflow the local region. Mechanical smoke extraction system can discharge generous smoke in a room, make the high temperature flue gas remained above the opening ports, no overflow or little overflow; but the smoke temperature will significantly decrease due to generous heat is discharged to outdoors, the nozzles also therefore delay action, may not control early fire, and cause more harm. Thus, it’s required to consider the mutual influence between mechanical smoke extraction system and automatic sprinkling fire control system, so as to make sure the mechanical smoke extraction system play a role effectively, which can not only discharge smoke effectively in early period of fire, prevent smoke spillover, but also do not affect timely action of sprinklers, control the fire effectively. Aim at problems above, take a typical commercial retail store(the size of 4×5×2.8m) in terminal building as an example, design two kinds of fire conditions, the mechanical smoke extraction volume are taken at 120m3/(h·m2) and 60m3/(h·m2), and the numerical simulation method is adopted, to discuss how the mechanical smoke extraction system influent the fast response sprinkler head start time, thus provide basis for the selection of the appropriate amount of mechanical smoke extraction. Set the fire in a typical space in the starting layer, fire develops according to t2 fire, the maximum heat release rate is 5.7MW, fire growth coefficient is 0.04689kw/s2, the nozzle action temperature is 68℃, smoke extraction volume of scenario a is 60m3/(h·m2), smoke extraction volume of scenario b is 1200m3/(h·m2). The initial environmental conditions are input to the fire simulation software FDS, the calculation results are shown in Figure 1 and Figure 2.

Fig. 1 Sprinkler actuation time when the smoke extraction volume is 60m3/(h·m2)

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Fig. 2 Sprinkler actuation time when the smoke extraction volume is 120m3/(h·m2)

Fig. 3 Temperature nephogram when the smoke extraction volume is 60m3/(h·m2)

Fig. 4 Temperature nephogram when the smoke extraction volume is 120m3/(h·m2) Figure 1 & 2 are in comparison of sprinkler actuation time when smoke extraction volume diverse. It can be seen from figures, when the smoke extraction volume is 60m3/(h·m2), the nozzle actuation time is 103s; when the smoke extraction volume is 120m3/(h·m2), the nozzle actuation time is 158s, a difference of 55s. Figure 3 shows the comparison of ceiling temperatures at the time

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that the first nozzle acts, it can be seen that the ceiling temperature of figure 3 is higher than that of figure 4, it’s thus clear that smoke extraction has a great impact on ceiling temperature. In the original design the smoke extraction volume of commercial retail store is 120m3/(h·m2), according to the analysis, when the smoke extraction volume is 60m3/(h·m2), smoke layer can be maintained above a certain height, and in this condition the nozzle actuation time moves up 52s compared with that when the smoke extraction volume is 120m3/(h·m2), it’s more beneficial to control the fire in the early time. Summary Commercial and other service facilities are suggested to control fire spread by “Open Cabin” and “Fuel Island”, that means various functional rooms and commercial service facilities in large space adopt strict fire partition and protection measures, control the fire early and effectively in the burning room. When the mechanical smoke extraction system is set in “Open Cabin”, its mechanical smoke extraction volume should be 60m3/(h·m2). References [1]Lingcao Xia, Zhujiang, Wen-li Liu. Large civil airport terminal building fire protection design concept and practice [J]. Building Science, 2010, 11:95-99. [2]Hujing, Lijun Yang. Introduction to the airport terminal building fire protection design [J]. Heilongjiang Science and Technology Information, 2013, 19:218 + 107. [3]Xuwen, Yufu Chen. Large hub airport terminal of fire control design [J]. Architectural Creation 2012:140-146. [4]Shaoquan, Zhixing Tang, Baiyang. Based on the social force model of civil airport terminal fire safety evaluation [J]. Journal of Computer Applications, 2012, S2:248-251. [5] Linqi Wen. Northwest regional small and medium-sized terminals design research [D]. Xi’an University of Architecture Technology, 2013.

CHAPTER 3: Mechanical and Dynamical Principles and Design, Machinery and Manufacturing Engineering

Applied Mechanics and Materials Vol. 721 (2015) pp 73-77 © (2015) Trans Tech Publications, Switzerland doi:10.4028/www.scientific.net/AMM.721.73

Submitted: 10.10.2014 Accepted: 19.10.2014

Influence of Guide Vane Wrap Angle Key Design Parameters on Hydraulic Performance of Nuclear Reactor Coolant Pump Weinan Jina, Rong Xieb, Muting Hao, Xiaofang Wang School of energy and power engineering, Dalian University of Technology, Dalian 116024, China a

[email protected], [email protected]

Keywords: reactor coolant pump, guide vane, warp angle, numerical simulation, Numeca, volute

Abstract. To study the effects of guide vane with different vane wrap angles and relative positions of outlet edge on hydraulic performance of nuclear reactor coolant pump, three-dimensional steady numerical simulations were performed by using CFD commercial software Numeca. The results show that the vane wrap angle changes the head and power characteristics by changing the relative velocity angle in vane outlet. The inner flow field changes while the wrap angle changes. With the wrap angle increases, the shock loss in volute is reducing, but the friction loss in vane passages is getting large. So there exists an optimum wrap angle and relative positions of outlet edge that corresponds to the highest efficiency of a pump. Numerical simulation is performed with the two key design parameters optimized through surrogate model, the internal flow field is improved and then the hydraulic efficiency is improved. Introduction Nuclear main pump is the heart of the nuclear island and also the only high speed rotating device. Its performance and reliable operation generate a direct impact on the capacity and safety of nuclear power plants[1]. In recent years, scholars conducted in-depth study on optimal design and the internal flow analysis of mixed-flow pump. By controlling the blade load distribution to suppress the secondary flow inside the blade meridional plane, Zangeneh[2-4] use the inverse design method to optimize a mixed-flow pump impeller and analyzed the flow pump hydraulic performance and cavitation performance by using numerical simulation methods. Domestic scholars[5,6] did some research on the impact of vane inlet edge position, the blade shapes, different exit angle of the flow characteristics. Surrogate model approach using radial basis function neural network (RBNNs), with the advantages of fast and efficient, and therefore become more and more widely used in turbo machinery optimization. Kim, JH and Kim, KY [7] control two vane design parameters, using a surrogate model approach to improve the efficiency of the mixed-flow pump. The wrap angle of the vane is too long will increase friction loss along the flow path, thereby reducing the hydraulic efficiency of the pump. On the contrary, the wrap angle is too small will reduce the ability to control the fluid flow, and is also not conducive to improving the efficiency of the pump. In view of this, the paper established the nuclear main pump model with different vane wrap angles and relative positions of outlet edge, and each model is simulated by using commercial CFD software NUMECA, which is widely used in the field of turbo-machine. In addition, surrogate model approach was used to optimize vane wrap angles and relative positions of outlet edge, and ultimately won the nuclear main pump vane structure with optimum hydraulic performance. Computational Model The model pump used in this paper was self-designed by our research team. Main design parameters: Rated flow 17886m3/h, Rated pressure head 111.3m, design temperature 616k, design working pressure 17.1MPa, working speed 1750r/m. Since the specific speed is 416, nuclear main pump impeller is designed to mixed-flow style. To study the impact of vane warp angle and outlet edge position to the hydraulic performance of the pump, this paper established a total of 25 nuclear main pump models consist of different warp

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angles from 48°, 54°, 60°, 66°, 72°and different outlet positions from 0°, 4°, 8°, 12°, 16°. Table 1 shows the combination form of the 25 calculation models. The Fig.1 shows the 3D model of impeller and the guide vanes and the whole machine model.Fig.2 is aschematic diagramof the midsection with a 60° wrap angle. The vane outlet edge position in Fig.2 is defined as the initial position (central axis and vanes outlet connections on the horizon), and this model is named A1*. Table 1 25 calculation models Warp Angle Outlet Position Original Outlet Deflection Angle 4° Deflection Angle 8° Deflection Angle 12° Deflection Angle 16°

48°

54°

60°

66°

72°

A11 A21 A31 A41 A51

A12 A22 A32 A42 A52

A13 A23 A33 A43 A53

A14 A24 A34 A44 A54

A15 A25 A35 A45 A55

Fig.1 The three-dimensional geometry of the pump and its impeller and vane

Fig.2 The diagrammatic sketch of middle section

Basic Principle and Numerical Calculation Basic Principle. The general form of the three-dimensional flow control equations in the pump, see equation (1) ∂ (ρφ ) + div (ρuφ ) = div (Γgradφ ) + S (1) ∂t In the equation, φ means solving flux, which is able to take the place of velocity component, Γ is diffusion term, S is Source term, u is velocity vector. Boundary Conditions. At the inlet of the calculation domain, the absolutely total temperature and pressure were given. The absolutely total temperature was assumed to 616k. The absolutely total pressure was assumed to 17.1 Mpa. At the outlet of the calculation domain, the mass flow and the reference static pressure were given, the mass flow was assumed to 3044.1 kg/s, and the reference static pressure was assumed to 17.8 Mpa. The walls were modeled using a no-slip boundary condition. In the calculating, when the global residuals was less than 1e-04, the mass flow relative error between the inlet and the outlet was less than 0.5%, and didn’t vary in the calculation, all the overall performance didn’t change, so the calculation was consider to be converged. Computational Grid. Firstly, the three-dimensional pump geometry for preceding 25 kinds of different combinations model pump was modeled by ProE software.AutoGrid5 is one of the semi-automatic mesh generator, which is used to generate the high quality structured mesh of the blade and the vane. Mesh in the volume was manually generated by the IGG module. In the check of grid quality, the minimum of the orthogonality was more than 24°, the maximum of the aspect ratio was less than 5000, and the maximum of the expansion less than 5 was all meet the computing requirements. Eventually the whole grid number is 2800000, the grid number satisfies the requirement of numerical calculation through the verification of grid independence.

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Analysis of Numerical Results Fig.3 is the hydraulic performance of preceding 25 kinds of different combinations model pump, in which the abscissa for the value of vane wrap angle, ordinate for hydraulic efficiency. With the wrap angle increases, the hydraulic efficiency all become smaller first, and the value have a plunged after the increase in the vicinity of 66°. Only in the initial position of the vane outlet edge, with the value of wrap angle changes, the efficiency values changed little compared to other outlet position, which has a better stability flows. While at the same warp angle, the influence of the relative position of guide vane outlet on efficiency is irregular, thus the effect of guide vane angle and its outlet position on fluid flow are complicated. Impact of vane wrap angle and vane outlet relative position of these two key design parameters for nuclear main pump hydraulic performance is not a single, but interrelated, mutual restraint. There should be more excellent warp angle correspond with optimum installation location of the outlet guide vanes, making nuclear main pump hydraulic performance relative optimum. Therefore, these two parameters were optimized simultaneously in this paper. Table 2 Calculation Result original pump A13 numerical simulation pump B surrogate model pump B

Head H/m 129.87 134.74 132

Efficiency η 0.80984 0.83943 0.83447

Power kW 4766 4776

Fig.3 The hydraulic performance of 25 kinds of different combinations model pump Double Parameter Optimization and Results Analysis In this paper, the above parameter was used to get the training samples shown in Fig.3. by using quadrature sampling method, and then get the agent model through using the sample set to train radial neural network (RBF-ANN) .Surrogate model can be used to predict the hydraulic efficiency of different warp angles and relative outlet position, finally, optimum warp angle and relative outlet position was optimized through the micro genetic algorithm. Nuclear main pump computational model is reconstructed with the optimized vane warp angle and vane relative outlet position, and the numerical calculation result is in Table 2. A13 is the original design model pump in Table2, the guide vane wrap angle is 60 °and the outlet position is the original location; model pump B is optimized using a surrogate model. Table 2 shows that optimization result from the surrogate model is close to the CFD calculations one, so surrogate model can be considered to meet the requirements of the precision. The hydraulic efficiency of optimized model pump improved 2.96% than the original design, pump head reaches 134 meters, slightly higher than the original design to meet the design requirements.

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Design3D,a optimization module build-in Numeca has a strong ability of model optimization, however, compared to the surrogate model approach, its optimization cycle is too long , and only a single blade can be optimized directly. Usually a Design3D optimization process needs two weeks to a month of computing time, while using a surrogate model optimization method, just ten minutes you can get optimal results on the basis of obtaining a sufficient number of data samples. Fig.4 shows the static pressure distribution at the 0.5 times height of the vane of model A13 and model B. In the figure, both of the model A13 and model B exist a area of low pressure at the convex of guide vane inlet (right), but the scope of the area of low pressure in model pump B is less than the model pump A13, and the pressure is higher than the model pump A13, the pressure distribution is more reasonable and uniform, and has a better diffusion and diversion effect.

Fig.4 Static pressure distribution at the 0.5 times height of the vane of model A13(Left) and model B(Right) Fig.5 shows streamline chart at the 0.5 times height and the drawing of partial enlargement. In the Fig.5, both of the model A13 and model B exist vortex at the convex of guide vane inlet(right),it means flow separation occured and low momentum fluids formed after the flow into the guide vane. The vortex size of the model pump B is much smaller than the model pump A13, thus the losses become smaller and the efficiency be improved. This demonstrates that the inlet and outlet angle of guide vane become more reasonable after optimization, thereby improving the flow of fluid in the flow path.

Fig.5 Streamline chart at the 0.5 times height and the drawing of partial enlargement For security reasons, the outlet pipe is usually designed as a cone-type perpendicular to the spherical surface, this form of outlet inevitably produce reflux, shock and flow separation, and be a major factor for the low efficiency.Fig.6 and 7, respectively, shows streamline and static pressure distribution in the middle section of the original model A13 and optimization model B(with vanes). Fig.6 shows the model exists an area of low pressure at the junction between outlet pipe and volute, but this area of low pressure in model pump B is not obvious.Fig.7 shows the pump model A13 produces a wide range of low-speed vortex region, thus preventing the outflow of the volute, resulting in great flow losses. After optimization, the flow in the middle section of the volute has been improved substantially. The size of low speed vortex is significantly reduced and exists only in a small region near the wall of outlet pipe.

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Fig.6 Static pressure distribution in the middle section of the original model A13(Left)and optimization model B(Right)

Fig.7 Streamline in the middle section of the original model A13(Left) and optimization model B(Right) Conclusion In this paper, the numerical orthogonal experimental method was used to analyze the influence rule of vanes wrap angle and the relative outlet position of guide vanes on hydraulic performance of nuclear reactor coolant pump; the corresponding sample set was obtained at the same time, and then the radial neural network (RBF-ANN) were used to train to get a surrogate model, which is able to predict the hydraulic efficiency of different warp angle and relative outlet position of the vane, and then better guide vane value and relative outlet position was searched through the micro genetic algorithm. Finally, through numerical simulation, it verified the feasibility, rapidity and rationality of the surrogate model optimization method. Acknowledgements This work is supported by the National Basic Research Program (973 Program 2015CB057301), and the Fundamental Research Funds for the Central Universities (DUT13JN07). References [1] L. Cai and L.P. Zhang: Pump Technology, (2007) No.4, p.1. [2] M. Zangeneh, A. Goto and T. Takemura: ASME J. Turbomach, Vol. 118 (1996) No.3, p.536. [3] A. Goto, and M. Zangeneh: ASME Trans. J. Fluids Eng, Vol.124 (2002) No.2, p. 319. [4] Oh H W, Yoon E S: Journal of Mechanical Engineering Science, Vol.222 (2008) No.9, p.1861. [5] H. Bing, S.L. Cao, L. Tan and L. Lu: Drainage and Irrigation Machinery Engineering, Vol.30 2012 No. 2, p.125. [6] X. Zhang, Y. Wang, X.M. Xu, H.Y. Wang: Journal of Agricultural Machinery, Vol.41 2010 No.11, p.38. [7] Kim, J. H., and Kim, K. Y: Int. J. Fluid Mach. Syst, Vol. 4 (2011) No. 1, p.172.

Applied Mechanics and Materials Vol. 721 (2015) pp 78-81 © (2015) Trans Tech Publications, Switzerland doi:10.4028/www.scientific.net/AMM.721.78

Submitted: 10.10.2014 Revised: 21.10.2014 Accepted: 22.10.2014

Numerical Investigation on the Flow in Tip Clearance of the Last Three Stages of Industrial Steam Turbine Zhaojun Sha1, a, Rong Xie1, b, Xiaofang Wang1, c, Xudong Ding2, d, Yongfeng Sui2,e 1

School of energy and power engineering, Dalian University of Technology, Dalian 116024, China; 2

Hangzhou steam turbine works, Hangzhou 310022, China.

a

b

c

[email protected], [email protected], [email protected]

Keywords: Steam turbine; tip clearance; numerical simulation; equilibrium condensation model.

Abstract. Numerical investigation was conducted on the full-three-dimensional flow in the last three stages of steam turbine using by the commercial computational flow dynamics software CFX. And the equilibrium condensation model was adopted to describe the wet steam two phase flows. This article analyzes the internal flow of the turbine with tip clearance and the structure of the leakage flow. Introduction In the last stages of the large power steam turbine, because the blade is longer than the other, the rotor does not have belt. Therefore, in the small space between the rotor tip and the end wall, steam flow from the pressure side to the suction side under the action of the static pressure drop. This part of the fluid generates detached eddy in the tip clearance, then this eddy mixes with the mainstream after flowing out of the tip, which rolls forming the clearance vortex leading to the increasing of flow loss and decline of the turbine mechanical efficiency [1]. In general, clearance height is increased by 1% at a time, causes 1% to 2% of the leakage of the mainstream, thus will reduce 1% to 3% of the stage efficiency [2]. As the steam turbine developing to a high load, high efficiency and large flow direction, lengthen the last stages' blade of low pressure part , high-power condensing wet steam stages accounted for the total power capability can reach more than 20%.The exhaust steam humidity of modern condensing steam turbine is as high as 10%, thus, the economic loss the wet steam two phase flow problems bringing about in the large condensing steam turbine and nuclear power plants should not be underestimated[4]. In essence, wet steam condensation two-phase flow in steam turbine is a kind of unbalanced flow process, which includes two kinds of imbalance, one is the thermal imbalance causing by the supersaturated expansion, the other one is causing by the different dynamic characteristics of droplets blending with high speed steam flow [5]. According to the design parameters and the data of the blade the manufacturing industry provided, a numerical investigation on the flow has been carried on in the last three stages of a wet steam turbine. The article analyzes the internal flow of the turbine with tip clearance and the structure of the leakage flow. Numerical Simulation Method This paper adopts the three-dimensional modeling software Pro/E to model the last three stages of the industrial steam turbine. Then use the AutoGrid module of Numeca software to generate mesh,the blade grid is as shown in figure 1.From the first stage to the third stage, the height of the tip clearance is 5mm, 5 mm and 10 mm respectively. The blade adopts the O type grid, grid refinement in the tip clearance, as shown in figure 2. The overall grid number is 1.3 million, y + values more than 30.

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Fig.1 The grid of the last three stages Fig.2 The grid of the blade tip clearance Numerical simulation calculated in CFX software platform, solving based on Reynolds average Navier - Stokes equations. Numerical model adopts the standard k-epsilon model and wall function considering the flow within the flow channel. Finite volume method is adopted for space dispersing. Parameter transfers between rotor and stator by using the mixing plane model. Phase transformation using equilibrium condensation model, selecting the IAPWS IF97 water vapor model, the iterative convergence accuracy is 1.0 x 10 - 6. Setting in let condition as total pressure and total temperature, total pressure is1 bar, total temperature is 99.61 ℃, water vapor mass fraction is 99% and rotational speed is 3000 r/min. Numerical simulation validation 1.2

1.2

Experiment Caculation

Experiment Caculation 1

0.8

0.8

P/P0

P/P0

1

0.6

0.6

0.4

0.4

0.2

0.2

0

0

0.2

0.4

0.6

X/B

0.8

1

0

0

0.2

0.4

0.6

0.8

1

X/B

Fig.3 Comparisons between the numerical and Fig.4 Comparisons between the numerical and experimental surface static pressure for test1 experimental surface static pressure for test 2 Referring to Bakhtar's two-dimensional cascade experiment[6], the inlet steam conditions selected two different temperature .The superheat degree of first experiment wasabout 30 k and the super cooling degree of the other one was around 10k.Figure 3, 4 shows the comparison between the numerical calculation and experimental results. Results show that the numerical results with the experimental are in good agreement in condition 1.In Condition 2 numerical simulation of pressure is slightly higher than the experimental values, the suction surface behind 40% chord length relative tolerance is bigger, the main reason may be affected by the trailing edge oblique shock wave. This conclusion verifies the correctness of the mathematical model and numerical simulation method [10] Analysis of the simulation Figure 5 shows the pressure distribution contour of 50% blade height section. It is visible of the pressure difference between the blade pressure side and the suction side in the figure. The leakage of fluid goes across the gap in the blade tip clearance under the driving of the differential pressure which interacts with the upper wall transverse flow forming the leakage vortex. This process not only causes the reduction of the working medium, but also affects the mainstream and influent the flow at the next stage.

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Fig.5 The pressure distribution contour Fig.6 The Mach number distribution contour of 98% blade height section of 50% blade height section Figure 6 is the Mach number distribution contour of 50% blade height section. The coming flow strikes the blade leading edge, leading to the velocity stagnating and dropping to zero, forms low speed region. The blade is reactionary, the pressure drops and the speed rises when the fluid flows through the blade passage between blades. The blade trailing edge appears obvious oblique shock wave, observed in the last stage of rotor trailing edge, which makes the velocity of the outlet decline. Figure 7 shows the liquid mass fraction distribution contour of 50% blade height section. The liquid quality increase step by step. With reference to the corresponding pressure distribution, the liquid mass fraction increases in the position of the low pressure. The liquid mass fraction is relatively lower at the final rotor trailing edge, mainly due to the oblique shock wave, which causes the reduction of airflow velocity, the rise of pressure and temperature.

Fig.7 The liquid mass fraction distribution Fig.8 The total pressure distribution contour contour of 50% blade height section of first stage's equal spacing section Figure 8 is the total pressure distribution contour of first stage's equal spacing section. The interaction between leakage flow and mainstream is mainly the mixing of clearance leakage vortex and various kinds of flow in the mainstream .Leakage vortex is the largest source of clearance leakage loss, compared with the loss caused by leakage vortex, the loss caused by the no working leakage flow is very small. The figure shows that from 50% exhibition place, the size of the leakage vortex increases rapidly, and the leakage vortex mixes with passage vortex, wall boundary layer and wake, which leads to the loss increasing significantly. The flow of the second and third stages' rotor is similar to the first stage, but affected by the next higher stage, the flow is relatively complex. From the figure 9, the development of leakage vortex can be observed separately. The leakage vortex mixes with passage vortex, wall boundary layer and wake rapidly. Then the vortex moves downstream, influent of the flow of the inlet of the next blade. Figure 10 shows the pressure distribution contour of the middle section of the first stage's tip clearance. There are two low pressure locations, the pressure reduction near the blade is due to the leakage vortex, in the meantime, the pressure reduction away from the blade is due to the passage vortex. The intensity of passage vortex is greater than the leakage vortex, and the leakage vortex generates from the central spanwise, this part of the vortex cloud develops endlessly, finally mixes with the passage vortex and continues to develop downstream, which is consistent with previous analysis. There are two special lines in the figure 11, one is the separation line that a streamline draws close from two sides gradually, and it derives from around the suction surface of the blade inlet. It stays close to the suction surface before 2/3 of the spanwise and then leaves the suction for extending far down stream. Another one is the reattachment line that the streamline separates to two sides. It derives from around the pressure surface of the blade inlet, downstream extension and leaves the suction for

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extending far down stream. This is consistent with the descriptions in the literature in the flow passage, the upper end wall boundary layer oblique flow through the flow passage to the suction surface under the action of lateral pressure. It is the flow characteristic of fluid near the upper wall that determines the similar features of the limit streamlines.

Fig.9 The surface streamline of third stage's rotor blade

Fig.10 The pressure distribution contour of the middle section of the first stage's tip clearance

Fig.11 The relative velocity streamline of the middle section of the first stage's tip clearance Conclusion This article has simulated the last three stages of the steam turbine with tip clearance using the equilibrium two-phase condensation model of the CFX software to make the results more close to the actual results. The article analyzes the internal flow of the turbine with tip clearance and the structure of the leakage flow for the later study about the steam turbine performance varying with the tip clearance and the selection of the tip clearance. References [1] M. S. Niu, S.S. Zang, M. H. Huang: Journal of engineering thermo physics, Vol.29 (2008) No.6, p. 935. [2] Booth, T. C., P. R. Dodge, Hepworth, H. K: ASME, Journal of Engineering for Power, (1982), p. 154. [3] Denton, J. D., and N. A. Cumpsty: Turbo machinery Efficiency and Improvement, (1987), p.87. [4] B.W. Chen: Numerical Study of Wet Steam Two Phase Non-Equilibrium Condensation Flow in Turbine, (MS, North China Electric Power University, China 2012). [5] A.R. Avetissian, G.A. Philippov, L.I. Zaichik, Nuclear engineering and design, Vol. 235 (2005) No.10, p.1215. [6] F. Bakhtar, M. Ebrahimi, R. A. Webb: Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, Vol. 209 (1995) No.2, p. 11.

Applied Mechanics and Materials Vol. 721 (2015) pp 82-86 © (2015) Trans Tech Publications, Switzerland doi:10.4028/www.scientific.net/AMM.721.82

Submitted: 13.10.2014 Accepted: 22.10.2014

A simplified model of semi-open impeller stage and analysis of its effects on the transient flow Muting Hao1, a, Rong Xie1, b,*, Liang Guan2, Zifu Lu1 1

Faculty of Mechanical Engineering , Materials and Energy, Dalian University of Technology, Dalian, P. R. China, 116024; 2

China Aerospace Science and Technology Corporation, Shanghai, P. R. China, 201100. a

b

[email protected], xrwork @yeah.net

Keywords: semi-open impeller, unsteady analysis, simplified model.

Abstract. The centrifugal compressor is one type of vital energy conversion equipment and its unsteady characteristics are extremely complex in actual operation. A semi-open impeller stage with inlet guide vanes, an impeller and a diffuser in a centrifugal compressor was concerned. For simulation of unsteady flow, the full-passage model of the integrate stage requires much more simulating time and memory space, higher computer configuration. Therefore, a single-passage simplified model was established for unsteady analysis. The internal flow characteristics and aerodynamic load on the blade obtained by the simplified model were also compared with that by the full-passage model. The result shows that the precision of the simplified model can meet the engineering requirement. Compared with the full-passage model, the simplified model can give a relatively true reflection of the local flow characteristics and the aerodynamic load on blade surfaces, but it ignores the unevenness resulted from unsteadiness along circumferential direction. Only high-frequency information is retained in aerodynamic load analysis while low-frequency one is diluted. However, as far as the local flow pattern or high-frequency information resulted from unsteady effects is concerned, the simplified model provides the advantages of higher computational efficiency and lower hardware requirements. Introduction In a centrifugal compressor, different rows contain different blades. For comprehensive study on flow unsteadiness, a full-passage model is always chosen for analysis. Therefore, too much computing time and memory space may be spent and high computer configuration is also required. Recently, various methods have been proposed to maximize saving computing time and memory space of numerical simulation. For numerical theory methods, Ni RH [1] proposed a multiple grid scheme for solving Euler equations. About physical computational methods, Wang [2] built up MPI-based parallel computational algorithms for speeding up. From physical model simplification, blade sectors scaling proposed by Rai [3] was widely used through scaling the blade section size while changing blade numbers slightly and its effects on flow was discussed [4]. Then the effect of blade sector scaling on aerodynamic loads was studied by Wang [5] and Li [6]. In addition, the approach of blade number scaling without changing blade section has also been widely used. Zhou [7], Xi [8] analyzed the unsteady characteristics in a centrifugal compressor through domain scaling method. The blade number in rows was adjusted from 15/20/13 to 14/21/14, and then the 2/3/2 model was analyzed. Similarly, Xie et. al. [9] adjusted the blade number from 12/19 to 12/18, and the model of 2/3 was selected likewise. All results above meet the accuracy requirement. Therefore, with certain principles, it will not have a greater impact on overall performance while slightly adjusting blade numbers. Instead, simulating time and memory space required will be reduced. Based on these, a simplified model for a centrifugal compressor was proposed in this study to improve the computing efficiency and to reduce the computing storage required while ensuring the accuracy. The semi-open impeller stage with three rows was simplified as a single-passage model. Its

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transient computing result was compared and analyzed with that obtained by the full-passage computational model. A beneficial reference for improving unsteady simulation was provided. Numerical model and method Numerical model. For the present study, a semi-open stage in a large-scale centrifugal compressor was concerned. The stage consists of 12 inlet guide vanes, 19 impeller blades and 20 diffuser vanes. The 3D geometry of full-passage model applied in this study is shown as Fig.1.

Fig.1 The full-passage model

Fig.2 Scaling of inlet guide vanes

To save computing time and storage space required, the above model was simplified. The inlet guide vanes merely regulate the flow rate and its blade profile is simple. Besides, the vane row is far from the impeller row and thus has little effect on the flow downstream. So in the simplified model, the blade sectional scaling was conduct combined with blade number changed. The profile size of the vane was reduced to

12 of the original size, while adjusting the vane number to 20 to maintain the 20

same mass flow shown as Fig.2. And for the impeller blade, the method of blade number scaling was adopted. The number of impeller blades was adjusted from 19 to 20. Then according to the domain scaling rule, a single passage was selected in the simplified model containing one inlet guide passage, one impeller passage and one diffuser passage, shown as Fig.3 (b). Mesh and numerical method. Meshing was carried out in NUMECA by structured grids used with ten million computational grids for the full-passage model whereas 1.5million grids for the simplified model (Fig.3). Both were verified by the grid independent and Y plus value met the requirements. SST turbulence model was selected to simulate the turbulent characteristics. At the inlet total temperature and total pressure were set while at the exit mass rate of flow was specified. Monitor points were set at the reference of rotational impeller through UDF interface program.

(a) full-passage model (b) simplified model Fig.3 Computational grids Results and discussion Overall Results. Table 1 compares results of two models. The relative errors of overall flow parameters between two models are both less than 0.065% except that the relative error of total pressure ratio is 0.776%. Under comparison, the result from the simplified model is coincidental with that from the full-passage model. Although the simplification may give rise to some errors, its result generally meets the engineering precision requirements. In essence, after scaling the realistic model, the rotor number is adjusted from 19 to 20 and therefore the power capacity is enlarged.

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Table.1 Results comparison of two models Model Simplified model Full-passage model Relative error

Outlet total Temperature (K) 383.912 383.844 0.018%

Outlet Total Pressure（Pa） 210525 210487 0.018%

Total Temperature Ratio 1.2924 1.2874 0.388%

Total Pressure Ratio 2.1426 2.1261 0.776%

Isentropic Efficiency 0.836189 0.836734 -0.065%

polytropic efficiency 0.852543 0.853031 -0.057%

The memory and CPU requirements for two models are compared in Table.2. Obviously the simplified model consumes 13%-20% memory and CPU of that of the full-passage model. The same pattern shows in computing time. The simplification saved a lot computing time and space. Table 2 Comparison of time and CPU Model

CPU

Memory/Real

Memory/Integer

Memory/Character

Memory/Logical

Memory /Double

Full-passage model

5.06E+05

3847974.0

811263.9

36625.0

650.0

12080.0

Simplified model

7.42E+04

509374.6

112949.8

7288.7

130.0

2416.0

Flow analysis. Fig.4 and Fig.5 show transient entropy in two models at t=0.0324s. The transient vortexes can be seen in unsteady flow field. Because the vortex will generate entropy increase and loss, the vortex property, structure, generation and its shedding process can be captured from transient entropy and total pressure distribution. Vortexes generate near the impeller blades trailing edge and developed with rotation. The upstream trailing edge vortex at the impeller exit and the downstream flow at the diffuser inlet then mix to regenerate the new vortex.

a.10% of the blade height b. 50% of the blade height c. 90% of the blade height Fig.4 Entropy in the simplified model

a.10% of the blade height b. 50% of the blade height c. 90% of the blade height Fig.5 Entropy in the full-passage model Fig.6 and Fig.7 present relative velocity around impellers and diffusers. Results of two models exhibit similar pattern. Vertexes generate on the pressure side at the diffuser inlet and low-energy groups block the flow path at the outlet. Besides, by comparing different blade height, the most low-energy groups appear under 90% of the blade height shown in both models. Moreover, a low-energy group increases the flow resistance and blocks the flow in its corresponding passage, resulting in improving the flow condition in its neighbor passages where the low-energy group is weakened relatively, however. This circulation leads to unevenness of flow along circumferential direction, but it only occurs in full-passage model. And in simplified model, it is ignored.

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a.10% of the blade height b. 50% of the blade height c. 90% of the blade height Fig.6 Relative velocity in the simplified model

a.10% of the blade height b. 50% of the blade height c. 90% of the blade height Fig.7 Relative velocity in the full-passage model Above all, compared with the full-passage model, the simplified model may ignore unevenness along circumferential direction while other local unsteady characteristics will be retained. Aerodynamic loads analysis. FFT technique has been applied to transform the aerodynamic loads difference between two sides of one impeller blade at the same z position. Monitor points are noted in Fig.10. Fig.11andFig.12presented static load spectrums in two models, showing spectral distribution characteristic at different position from the leading edge to the trailing edge.

(1)The suction side (2) the pressure side Fig.10 Monitor points

a.10% of the blade height b. 50% of the blade height c. 90% of the blade height Fig.11 Spectrum of load difference in the simplified model

a.10% of the blade height b. 50% of the blade height c. 90% of the blade height Fig.12 Spectrum of load difference in the full-passage model In spectrum analysis, according to the rotation speed, the base frequency is 92.6Hz. Except low-frequency interference, the amplitude at 20 f n (diffuser vane passing frequency) appears near the trailing edge of blades. Compared with that in the full-passage model, low-frequency part of the

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spectrum is diluted in the simplified model but high-frequency information is retained. Furthermore, in the simplified model, 20 f n stands out especially from 4/5 streamline distance between the leading edge and the trailing edge of the impeller blade. It is related to the impeller-diffuser interaction. Flow near the blade trailing edge is affected mainly by diffuser potential repercussion, so the diffuser vane passing efficiency exerts dominating effect on the unsteady flow. All in all when studying some cases related to high-efficiency interference such as impeller-diffuser interaction, the simplified model presents advantages of high efficiency, less computing time and less storage space required with engineering requirements still being fulfilled. Conclusion A simplified method was applied to the semi-open impeller stage in a centrifugal compressor. From the transient simulation of the single-passage model obtained by simplification, flow characteristics and aerodynamic loads were analyzed and compared with that of the full-passage model. Results show that the simplified model meets the engineering requirements. Compared with the full-passage model, the simplified model can truly reflect local flow characteristics and high-frequency aerodynamic loads. But some limitations exist. Unevenness resulted from unsteadiness in the circumferential direction will be ignored and low-frequency information will be diluted. Above all, for improving efficiency and reducing memory required, the simplified model provides a quick and effective direction while ensuring the accuracy of the analysis result. References [1] Ni RH. A Multiple Grid Scheme for Solving the Euler Equations [J]. AIAA Journal, 1982. [2] Wang Yonghong. Research on numerical methods of the three dimensional unsteady flow in turbomachinery and parallel computational algorithms [D]. Nanjing University of Aeronautics and Astronautics,2007.DOI:10.7666/d.d038435. [3]Rai M M. Navier-Stokes simulations of rotor-stator interaction using patched and overlaid grids. Journal of Propulsion and Power, 1987; 3: 389-396 [4] JClark J P,Stetson G M,Magge S S,et al. the effect of airfoil scaling on the prediction unsteady loading on the blade of a 1 and 1 /2 stage transonic turbine and a comparision with experimental results. Proceedings of the IGTI: ASME Turbo Expo, 2000; 8 [5] WANG Yuan-gang, HUANG Xiu-quan. Investigation into Effects of Blade Count on Unsteady Flows in Turbomachines. [J] Science Technology and Engineering, 2012, 12(10): 1671—1815 [6] LI Ya, Huang Xiu-quan, WANG Yuan-gang. Investigation into effects of blade sectors scaling on the aerodynamic loads. [J] Science Technology and Engineering, 2013, 13(11): 1671-1815 [7] Xi Guang, Liu Lei, Jiang Hua, et al. Numerical investigation of stator clocking in a centrifugal compressor[J]. Journal of Engineering Thermophysics, 2008, 29(9) 1495-1498 [8] Zhou Li,Xi Guang, Cai Yuanhu. Numerical and experimental investigation to unsteady igv-impeller-diffuser interaction [J]. Chinese Journal of applied mechanics, 2008, 25(2) 202-207 [9] Xie Rong, Hao Muting, Guan Liang; Miao Weidong; Shi Yanjun. Study on methods of numerical simulation of transient flow in the semi-open impeller stage of a centrifugal compressor. [J] Energy Education Science and Technology Part A: Energy Science and Research, 2014, 32(2) 1085-1092.

Applied Mechanics and Materials Vol. 721 (2015) pp 87-90 © (2015) Trans Tech Publications, Switzerland doi:10.4028/www.scientific.net/AMM.721.87

Submitted: 15.10.2014 Accepted: 21.10.2014

Dynamic Analysis and Optimization of Pivot Points of Telescopic Jib Based on Genetic Algorithm Tao Liua, Weihui Wanga, Renwu Yuana, Fei Lua Xi'an Research Institute of High-Tech, Xi'an 710025, China a

[email protected]

Keywords: Telescopic Jib, Dynamic Analysis, Optimization of Pivot Points.

Abstract. Using ADAMS software to create a virtual prototype model of luffing mechanism of QY20A crane truck. The entire work process of telescopic arm is analyzed, and the result shows that luffing cylinder lifting force is maximum at the beginning moment. The luffing cylinder force is calculated in different conditions based on the analysis and select the maximum force condition as optimized conditions, make the mathematical model of optimization and optimized using genetic algorithm, which played a guiding role for the of telescopic boom design. Introduction The crane telescopic jib is a key part which can affect the overall performance, the locations of pivot points have a direct impact on the stress of the telescopic jib. According to the reflection from the customer service, the weld bead locations of the pivot points of cylinder usually crack after a period of usage[1]. In order to avoid this situation, many crane manufacturers come up with the solution of welding stiffeners on the cylinder support base, but this will increase the weight of the cylinder support base, and the overall heavyweight costs increased. In addition, the locations of the pivot points have an effect on the thrust and the stability of telescopic jib, therefore it’s necessary to optimize the locations of the pivot points. The optimized points should satisfy the condition that the tress on the cylinder reaches its minimum under the premise to meet the lifting moment. Telescopic Arm Dynamics Simulation[2-3] Importing Models. Using Proe builds the model and conserves its copy as “.x_t” by the English or figure, then imports into ADAMS. Importing model would lose the color information, so need change the color of the part in order to division, and give every part matching material property. Definition Constraint and Driving. After importing model into ADAMS, need add constraint to definite the relative relation among parts, so that could be a simulation. In the Telescopic dynamic model, there are eight main components, basic arm, the second knuckle arm, three section boom, turntable, the lifting oil cylinder cylinder, Lifting oil cylinder piston rod, steel wire rope, loading, constraint definition as following: 1. Adding revolution joint between turntable and ground 2. Adding revolution joint between basic arm and turntable 3. Adding revolution joint between lifting oil cylinder piston rod and turntable 4. Adding translational joint between lifting oil cylinder piston rod and turntable 5. Adding translational joint between basic arm and the second knuckle arm 6. Adding translational joint between the second knuckle arm and three section boom 7. Adding spheric pair between three section boom hinge joint and steel wire rope 8. Adding spheric pair between steel wire rope and loading Driving is the motive power which means the movement of one part is the function of the time. There are three actions in this model, hoisting, rotation and extend-retract, but need four drive functions to have the basic arm movements of two arms expansion. Telescopic drive on basic arm and knuckle arm, and three basic arm on the translational joint, add swing drive on the rotation of the

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turntable and ground, hoisting drive adding on translational joint between the lifting oil cylinder cylinder and lifting oil cylinder piston rod. Simulation process load 1000kg in 10s~40s,it is 74degrees between telescopic boom and ground after lifting. Hoisting mechanism rotational motion turn 90 degrees in 40s-72s;Telescopic motion of telescopic boom flexible for agencies in 72s~150s,and reached 25.2m.This stimulation process show the telescopic boom loading and move motions. The Analysis of Stimulation Result. Original condition of telescopic boom, lifting angle is 0, stimulation time is 180s, the steps of the stimulation loading is 1800, the stimulation result as following:

Fig.1 The Force of Luffing Cylinder As shown in the figure, telescopic boom luffing cylinder own the maximum power in the moment of lifting heavy objects in the whole simulation process, the instantaneous stress maximum lifting heavy objects, rotary process remains the same, in the process of expansion, due to the increase of load torque, oil cylinder force increases gradually, after completion of the telescopic movement, as a result of the action of inertial load, oil cylinder force have a concussion. The Realization of the Optimization[4]

θ

Fig 2 Calculation Diagram of Luffing Mechanism The Selection of Optimal Conditions. Consider telescopic boom hinge pivot as the origin of coordinates, horizontal direction as the X axis, vertical direction of Y axis coordinate system is established. Crane luffing system three hinged points with five parameters, oil cylinder hinge points up and down , and the distance from the telescopic boom. In this paper, considering the actual dimension of the telescopic boom and strength requirement, the luffing cylinder hinged point on the distance from the center line of the telescopic boom as a constant, the introduction of three design variables, are defined as follows: X = ( xa , ya , xb ) (1) Noted by X = ( x1 , x2 , x3 )

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The Confirmation of the Target Function. After Optimization design need meet the minimum stress of transformer oil cylinder basing on enough load moment, so given the target function as following:

OB = OD = x32 + 2500(mm) OA = x12 + x2 2 (mm) AB = ( x1 − x3 ) 2 + ( x2 + 500) 2 (mm)

AD = Fz =

OA 2 + OD 2 − 2 × O A × OD × cos( 72.9 + arcos (

OA 2 + OB 2 − AB 2 ) 2 × OA × OB

734236885 × AD OA 2 + OB 2 − AB 2 OA × OD × sin(72.9 + arcos( )) 2 × OA × OB

(2)

(3) (4)

The Confirmation of the Constraints. The bounds of optimization variables. According to the implement requests, the optimization variables should have bounds that: xi min ≤ xi ≤ xi max (i = 1, 2,3) (5) The geometry size constraints. In the triangle OAB and OAD, we can conclude that: OA + OB − AB > 0 OA + AB − OB > 0 OB + AB − OA > 0 (6) OA + OD − AD > 0 OA + AD − OD > 0 OD + AD − OA > 0 (7) The motion constraints. In order to make sure the stability of the luffing cylinder, the motion constraints should be given. When the telescopic jib is moving, the ratio of the maximum and minimum of the luffing cylinder’s length is defined contraction ratio. According to the cylinder design specifications we know that the contraction ratio range from 1.6 to 1.7, so the constraint can be given that: l y max 1.6 ≤ λ = ≤ 1.7 (8) l y min In this paper, the lifting angle reaches its maximum 74.4owhen the length of the cylinder reaches its maximum, and it is on the minimum position when the telescopic jib is horizontal, so we get that: OA2 + OD 2 − 2 × OA × OD × cos(74.74 + arcos( 1.6 ≤

2730

OA2 + OB 2 − AB 2 )) 2 × OA × OB ≤ 1.7

(9)

The Size of Cylinder Constraints Before optimize, the location of the hinged points is not confirmed, so can get the relation as the formula: (10) x −x >0 3

1

The Analysis of the Optimization Result[5]

The basic parameters of genetic algorithm are set as follows: populationsize is 500, generation is 200, tolfun is 1e-10. The information about the location of the pivot points and the stress on the cylinder are shown in the picture as follows: According to the result of the optimization, the stress on the cylinder declines 6.74%, and the stress declines under every condition, so the optimization goal is optimized.

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12

15

Best: 520308.1685 Mean: 2760000448505.645

x 10

Fitness value

Best fitness

5

0

Current best individual

Mean fitness

10

0

50

100

150

200 250 Generation Current Best Individual

300

1

2 Number of variables (3)

3

350

400

6000 4000 2000 0 -2000

Fig.3 Optimization Results of Genetic Algorithm Table 1 The Optimization Results Before optimization Optimized

X1 1280

X2 X3 -900 3980

the thrust force 557926

14944

-815 4812

520308

Conclusion The condition that the stress of luffing cylinder reaches its maximum is confirmed with theoretical arithmetic, and the optimization is made with Genetic Algorithm. The result of the optimization shows that: the stress on the luffing cylinder is improved, and this is instructive in designing work of the telescopic jib. References [1] Tian Minghua. The finite element analysis and optimization researches for the jib of reach stacker. Changsha: Central South University, 2013. [2] Zheng Xijian, Zhang Xuan, Fei Ye, Optimizat Design of the Amplitude Variation Mechanism of Truck Crane Based on ADAMS, Machinery & Electronics, Vol.7 (2008)3-5. [3] Mechanical Dynamics Incoration, ADAMS/View User Guide, 1997. [4] GB/T 3811-2008.Design rules for cranes. Standardization Administration of The Pepole’s of CHINA, 2008. [5] Zhang Jun, Lin Cheng, Zhang Guoming, An Optimizing Design Method of Bus Body Frame Based on Genetic Algorithm, Transactions of Beijing Institute of Technology, Vol.1 (2008), p.45-49.

Applied Mechanics and Materials Vol. 721 (2015) pp 91-95 © (2015) Trans Tech Publications, Switzerland doi:10.4028/www.scientific.net/AMM.721.91

Submitted: 16.10.2014 Accepted: 21.10.2014

Development of Testing Machine for Measuring Unsteady State EHL Film under Heavy Load Naiming Miao1, 2, a, Jianning Ding1, 2, b, Jichang Yang1, c 1

School of Mechanical Engineering, Jiangsu University, Zhenjiang 212013, China;

2

School of Mechanical Engineering, Changzhou University, Changzhou 213164, China

a

[email protected], [email protected], [email protected]

Keywords: Heavy load; unsteady state; elastohydrodynamic lubrication; testing machine.

Abstract. In order to study elastohydrodynamic lubrication characteristics of cylindrical roller under heavy load and unsteady state, a testing machine is designed for measuring line contact film thickness and shape according to optical interference principle. The experimental apparatus is mainly made of glass block reciprocating motion systems, ball or roller rolling rotation system, loading system, speed control systems, lighting systems and image acquisition system. Moving parts is formed by the slider-crank mechanism. Specimen is accompanied by a pair of floating trial roller bearing. Specimen load is applied by leverage. The entrainment velocity of roller is approximate sinusoidal variation during a cycle. Experiment interference image is satisfactory, can provide reliable experimental data for future research. Introduction The constant change of working conditions of lubrication surface will inevitably cause the dynamic effect of oil film, thereby changing the characteristics such as pressure distribution of oil film and shape of oil film. Under the heavy unsteady working condition, the film formation method of lubrication is determined by hydrodynamic effect and squeeze effect, respectively. The hydrodynamic effect under low speed working condition is weak, and squeeze effect will become an important film formation method; while under high speed working condition, the squeeze effect is weak, and the hydrodynamic effect becomes an important film formation method[1]. In the starting and braking process of actual mechanical equipment, since the speed changes with time, the film formation method also changes. With the continuous improvement of science and technology, most machine parts work under the high speed and heavy load condition. Studying the lubrication mechanism and characteristics under this state has certain significance for completing the lubrication theory and the engineering practical value. Experimental study is an important means for studying elastohydrodynamics. Light interference measuring technique is effective for obtaining the oil film thickness and shape within tiny area. At present, most of measuring equipment for lubricating oil film are light load and related to point contact[2-9]. In order to satisfy the operating requirements of cylindrical roller under heavy load and unsteady state, we need to design a measurement testing machine for corresponding oil film thickness and shape. Structural feature of testing machine The study is realized through providing the following technical proposal: An oil film measurement testing machine with sine reciprocating type pure rolling line contact. Its feature lies in: The tested sample roller is only stressed on the maximum circular section, and makes pure rolling along this periphery, that is, pure rolling contact. For each revolution by the crank, the entrainment velocity of running pulley turns from zero to the maximum, and from the maximum to zero; then the velocity turns from zero to the maximum, and from the maximum to zero in reverse direction, making cyclic and periodical change. After enlarged by leverage, the load will be effected on the being measured

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roller. Being measured roller is supported by a pair of floating rollers, meanwhile, the floating roller is supported by supporting roller and load roller. The entrainment velocity of testing roller can be adjusted by the rotate speed of motor according to needs. The contact stress can achieve above 0.9GPa at maximum. Fig. 1 is the schematic diagram of mechanism. The crank-link mechanism makes planar motion through coupling and driven by a motor, further drive the plain glass to make the sine oscillating variable motion, drive the testing roller to make back and forth movement at the velocity of unequal angle, the testing roller is supported by a pair of floating roller, the floating roller is placed on the fixed bearing equipped with rolling bearing, the rolling bearing is also equipped at the bearing contact end of loading lever and floating roller. After enlarged by leverage, the load will be effected on the being tested roller. When the plain glass makes reciprocating motion for one time, the entrainment velocity of the tested roller varies from zero to the maximum, from the maximum to zero for twice, respectively.

Fig. 1 Schematic of the test apparatus Fig. 2 is the test apparatus diagram designed in this paper. The test apparatus has the following characteristics: (1) The tangential velocity of tested sample roller contracting with the plain glass varies in the sinusoidal periodic law, overload; (2) The tested sample roller may be cylindrical roller or steel ball; (3) The tested sample roller only bears the force on the maximum circle cross section and makes pure rolling along this circumference; (4) Simple structure, low fabricating cost, convenient operation, easy to realize real-time control, increased reliability of the test.

1. Rack 2. Computer 3. Light source system 4. Microscope 5. CCD 6. Reducer 7. Motor 8. Slider-crank mechanism 9. Glass plate 10. Trial roller and specimen 11. Guide rail 12. Loading unit Fig. 2 Schematic Diagram of Optic Elastohydrodynamic Test Apparatus Structural design problems When the testing machine is designed, the following structural problems are considered: (1) basically eliminate the bending moment bore by the transparent material to improve its bearing capacity; (2) realize the pure rolling of the test specimen; (3)prevent the roller from axial movement; (4) ensure the uniform load distribution; (5) eliminate the gap on the support member. To solve the above problems, three pairs of high-precision angular contact ball bearing are adopted to realize the pure rolling of the tested roller, among which two pairs of bearing are installed in the supporting wheel, another pair of bearing are installed at the end of loading wheel; In order to

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facilitate loading, a pair of floating trial rollers are used. The trial roller is made in the waist drum shape so as to make the loading on the tested roller to be uniformly distributed. The cage is used to limit the tested roller and trial roller, so as to ensure the roller to have no axial movement. The quadrilateral box in the cage for placing the tested roller is also made in the waist drum shape similar to the trial roller, so as to ensure the roller not to be stuck on return trip; the mounting hole of the support shaft is matched for processing, each shaft is equipped with a pair of angular contact ball bearing opposite to each other, in order to completely eliminate the gap of the support part. Test result verification Using this experimental machine, carry out the preliminary experiment under the condition of isothermy (±1℃), and rich oil lubrication. The used lubricating oil is polyisobutene PB2400, its characteristics are shown in table 1. Table 1 Features of lubricating oil Dynamic viscosity (Pa·s) Density (20ºC) Refractive index kg·m-3 (20ºC) (100ºC) 1050.0 4.7 905.0 1.504 The microscope is the monocular video microscope of type MZDH1065TC with high magnification and continuous variable zoom, which has the characteristic of high magnification and high resolution, can conduct video display and visual observation at the same time, the eyepiece observation be erect image. Using optical fiber cold light source for light for coaxial illumination, the working distance is 82mm, continuous variable zoom range of zoom subject is 1X - 6.5X, multiplying power of visual magnification is 10X - 20X. The light source of experiment is cold light source, adding a narrow band pass filter to leading end, the central wavelength is 600nm. The transparent material K9 plain glass with lower surface coated Chromium film. The diameter of roller in the test is Φ10mm, the overall length is 10.5mm, there is certain convex metric at both ends, the actual roller contour is approximately logarithmically convex type. The roller-glass composite elastic modulus E′=117×109 Pa, the total load during the test is W1=1668N, the maximum contact stress is pH1=0.76GPa. W2=2452N,the maximum contact stress pH2=0.92GPa. Fig. 3 is the dynamic interference figure when period T=1/4 - 3/4, the interference pattern got is relatively satisfactory, can provide reliable effective experimental data for theoretical research and engineering design.

T=1/4

T=4/12

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T=5/12

T=6/12

T=7/12

T=8/12

T=3/4 (a) Maximum pH = 0.76GPa (b) Maximum pH = 0.92GPa Fig. 3 Dynamic interference figure Conclusion The testing machine can work under the working condition of more than 0.9GPa effectively, when measuring unsteady state, the oil film thickness and its shape of cylindrical roller pure rolling contact surface of elastohydrodynamic lubrication can provide effective experimental data for theoretical research and actual engineering design of line contact elastohydrodynamic lubrication film forming mechanism. As can be seen from the dynamic interference image that, for the limited long-term contact roller oil film distribution in the test, there is an obvious elastohydrodynamic lubrication characteristic. There is the oil film necking phenomenon in the outlet in the middle of the contact area, most of the area in central contact region is relatively flat, with the increase of entrainment velocity, flat site decreases little by little, to have horseshoe contraction when there is similar contact in the end, the

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minimum oil film appeared in the area near the end. When reciprocating motion T equals to 1/2, the entrainment velocity is zero, the oil film is sealed in the contact zone and formed sinking. For different loads, closure effect is more obvious when overloaded, the thickness of the minimum oil film in the end zone is much smaller than the middle zone. Acknowledgements The work was supported by the Natural Science Foundation of Jiangsu Province (BK2011238). References [1] Wen Shizhu, Yang Peiran. Elastohydrodynamic Lubrication[M]. Beijing: Tsinghua University Press. 1992. [2] Ford R A J, et al. Studies on the Separating Oil Film (EHD Oil Film Thickness) between the Inner Race and Rollers of a Roller Bearing [J]. Mech. Engng, Trans, the Institution of Engineers, Australia, 1981, p.140~144. [3] Dmytrychenko N, et al. Elastohydrodynamic Lubrication of Line Contacts[J]. Wear, Vol.151 (1991), p.303~313. [4] J. Molimard, M. Querry P. Vergne, I. Krupka, M. Hartl. Calculation of pressure distribution in EHD point contacts from experimentally determinate film thickness[J] Tribology International, Vol.38 (2005), p.391–401. [5] A.D. Chapkova, S. Bairb, P. Cannc, A.A. Lubrechta. Film thickness in point contacts under generalized Newtonian EHL conditions: Numerical and experimental analysis[J], Tribology International, Vol.40 (2007), p.1474–1478. [6] Kazuyuki Yagi, Philippe Vergne, Tsunamitsu Nakahara. In situ pressure measurements in dimpled elastohydrodynamic sliding contacts by Raman microspectroscopy[J] Tribology International, Vol. 42 (2009), p.724–730 [7] E. Ciulli, K. Stadler, T. Draexl. The influence of the slide-to-roll ratio on the friction coefficient and film thickness of EHD point contacts under steady state and transient conditions, Tribology International, Vol.42 (2009), p.526– 534. [8] M. Carli, K.J. Sharif, et al.. Thermal point contact EHL analysis of rolling/sliding contacts with experimental comparison showing anomalous film shapes[J] Tribology International, Vol.42 (2009), p.517–525. [9] Taisuke Maruyama,Tsuyoshi Saitoh. Oil film behavior under minute vibrating conditions in EHL point contacts[J] Tribology International, Vol.43 (2010), p.1279–1286.

Applied Mechanics and Materials Vol. 721 (2015) pp 96-99 © (2015) Trans Tech Publications, Switzerland doi:10.4028/www.scientific.net/AMM.721.96

Submitted: 16.10.2014 Accepted: 28.10.2014

Finite element analysis of Z-shaped pipe in the directly buried heat-supply pipeline with large diameter Mingqiang Lia, Fei Wangb, Guowei Wang, Yonggang Lei College of Environmental Science and Engineering of TUT, Taiyuan 030024, China a

[email protected], [email protected]

Keywords: Numerical Simulation, Directly buried heat-supply pipeline, Z-shaped pipe, Elastic arm length.

Abstract. In order to improve Z-shaped pipe in the directly buried heat-supply pipeline with large diameter stress calculations, guiding the engineering design when the short arm length is less than two times the elastic arms length. The author used ANSYS software to numerical simulation analysis on Z-shaped pipe in the directly buried heat-supply pipeline of DN800, DN1000, DN1200, when the long arm length of 50 m, 100 m, 150 m, and the short arm length from two times to one times of the elastic arm length. Applying boundary conditions in different models with the short arm length shortens the process to Z-shaped pipe of the elbow stress. For Z-shaped pipe in the directly buried heat-supply pipeline the short arm from two times to one times of the elastic arm length, the elbow’s stress value is the minimum when the short arm length is 1.2 times of the elastic arm length. This article breaks the specification limits on the short arm length, improving the flexibility of directly buried heat-supply pipeline with large diameter, reducing the difficulty of construction, and it’s important for guiding the actual project. Introduction Directly buried heat-supply pipeline has already replaced the traditional laying of overhead in municipal heating. With the development of the city, pipeline size become larger and larger, and some areas had been DN1400 pipeline. Currently, perfect stress calculation for large-diameter directly buried heat-supply pipeline is particularly important. Z-shaped pipe is one of the important components of directly buried heat-supply pipeline. In the industry standard [1], when the length of the short arm not less than 2 times of the elastic arm length, the pipe can be divided into two angular sections, and it can be use of L shaped pipe algorithm; when the length of the short arm less than 2 times of the elastic arm length, pipeline is no clear specific approach. Commonly used in engineering practice from the literature [2]. Regard Z-shaped bend compensation as two unequal arm L-shaped pipe compensation, when the short arm length is 1.25-2 times of the elastic arm length, taking the short arm lengths equal to elastic arms length. This article will use the ANSYS software to calculate bend stress analysis of the large diameter Z-shaped pipe when the short arm length is less than two times the elastic arm length, to provide a reference for the design and construction of the directly buried heat-supply pipeline with large diameter. Finite Element Simulation of Z-Shaped Pipe Pipe size and material properties. Use ANSYS software to numerical simulation analysis on Z-shaped pipe in the directly buried heat-supply pipeline of DN800, DN1000, DN1200, when the long arm length of 50 m, 100 m, 150 m, and the short arm length from two times to one times of the elastic arm length. steel pipe use Q235B, Modulus of elasticity1.96E+5MPa; Coefficient of linear expansion 1.26E-5m/(m°C); Poisson's ratio 0.3; The maximum cycle temperature 130°C; Installation temperature 10°C; The minimum depth of the top pipe 1.5m. Solid Modeling, Meshing and Applied Loads. Z-shaped pipeline use solid45 elements to divide, meshing size 0.08m. The elastic reaction force of the soil use combine14 elements.

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Calculation method of comprehensive coefficient of subgrade is from literature [3]:DN800, k=2791kN/m; DN1000, k=3416kN/m; DN1200, k=3935kN/m. Table1 Pipe Size Nominal diameter DN DN800 DN1000 DN1200

Outside diameter of service pipe Do m 0.82 1.02 1.22

Inside diameter of service pipe Di m 0.8016 0.9956 1.1936

Outer diameter of casing pipe Dc m 0.96 1.155 1.37

Wall thickness δ m 0.01 0.013 0.014

elastic arm length Le m 11.3 12.7 13.9

Total weight per unit length G N/m 7720 10402 14806

Figure 1 Z-shaped pipe model diagram Figure 2 Z-Shaped Pipe SINT Stress Map Force and deformation are applied to Z-shaped pipeline [4]. Force is mainly caused by the pressure inside the pipeline medium. Deformation is caused by change temperature within the medium. Ignore the impact of the pipeline soil friction will result in the increasing of pipeline expansion, which makes network design safer [5]. Results and Analysis of Simulation Use ANSYS software to numerical simulation analysis on Z-shaped pipe in the directly buried heat-supply pipeline of DN800, DN1000, DN1200, get their bend stress values in Table2, Table3, Table4. Table 2 DN800 Pipeline Z-Shaped Pipe Bend Stress Values the short arm length the long arm length 50m(1.5DN) 100m(1.5DN) 150m(1.5DN) 150m(3DN)

2.0Le

1.8Le

1.6Le

1.4Le

1.2Le

1.15Le

1.1Le

1.0Le

352 583 811 740

347 573 799 731

339 564 780 711

329 541 761 684

321 531 752 652

319 531 754 646

319 534 760 651

323 546 780 669

1.1Le

1.0Le

Table 3 DN1000 Pipeline Z-Shaped Pipe Bend Stress Values the short arm length 2.0Le the long arm length 50m(1.5DN) 100m(1.5DN) 150m(1.5DN) 150m(3DN)

393 637 847 756

1.8Le 385 627 834 742

1.6Le 375 612 816 732

1.4Le 364 598 801 684

1.2Le 358 593 799 654

1.15Le 358 595 804 651

359 599 810 667

365 612 832 675

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Z-Shaped pipe bend stress values will after the first decreases and then increases, when the short arm length becoming from two times to one times of the elastic arm length. Bend stress values appear minimum, when the short arm length equaling to the elastic arm length. Figure 3 shall apply the results compare of DN800 Z-Shaped pipe. Table 4 DN1200 Pipeline Z-Shaped Pipe Bend Stress Values the short arm length 2.0Le

1.8Le

1.6Le

1.4Le

1.2Le

1.15Le

1.1Le

1.0Le

385 626 822 728

376 605 805 710

367 591 786 684

358 580 774 646

353 577 776 616

353 578 782 613

354 582 791 615

359 595 819 625

the long arm length 50m(1.5DN) 100m(1.5DN) 150m(1.5DN) 150m(3DN) 850 800 750

The long arm 50m(1.5DN) The long arm 100m(1.5DN) The long arm 150m(1.5DN) The long arm 150m（3DN）

700 650 600 550 500 450 400 350

length length length length

1. 0L e

1. 1L e

1. 2L e 1. 15 Le

1. 4L e

1. 6L e

1. 8L e

2. 0L e

300

Figure 3 The Results Compare of DN800 Z-Shaped Pipe From Table 2, Table 3, Table 4 shows, the engineering approach in the literature [2] can ensure project quality, but it is conservative. Compensation of Z-shaped pipe has not fully realized. Increasing the radius of curvature, the stress of the bend will significantly decreased, which improving the safety of the pipeline. Elastic arm length of large diameter heat-supply pipeline much longer than the small one. Make full use of Z-shaped pipeline is significance. Low Cycle Fatigue Z-Shaped Pipe The limit state low cycle fatigue will be important mainly for local component but should also be checked for straight sections with high axial forces. Documentation of the safety against fatigue failure shall pay regard to the relevant actions in such combinations so that a realistic picture is obtained of the variations in size and frequency of the stress variations in the individual components. By the design and installation of preinsulated bonded pipe systems for district heating [6], the number of full action cycles chosen for pipelines in normal operation shall not be lower than the number of equivalent full action cycles stated in Table4. Table4 Equivalent full action cycles Major pipelines 100 Main pipelines 250 House service connections 1000 The directly buried heat-supply pipeline with large diameter belongs to the major pipeline, so equivalent full action cycles ni =100. Use Palmgren-Miner formula to check fatigue security: n 1 (1) ∑i Ni ≤ γ i fat

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99

4

k 5000 Ni = = Si Si Where: k=5000N/mm2,m=4 is commonly used in prefabricated steel pipe insulation; ni is the number of cycles of stress range during the required design life; N i is the number of cycles of stress range to cause failure;

(2)

Si is the design stress range, MPa;

γ fat is the safety factor for fatigue fracture; i is the stress range for the number of different. The first network engineering is grade C, partial safety factor for fatigue γ fat =10, and after calculated Ni =1000, finally get the design stress range Si =889.14MPa. Comparison of Table 2, Table 3, Table 4, all of them can conform the limit state low cycle fatigue. The long arm length and the short arm length must be limited or use big bend radius, when bend stress value is bigger than the design stress range. Conclusions When Z-shaped pipe's short arm length from two times to a elastic arm length, bend stress shows an upward trend after the first decrease, and the minimum appeared in the vicinity of 1.2 times of the elastic arm length. That is to say, the ability to compensate is strongest when the short arm length equal to 1.2 times of the elastic arm length. Heat-supply pipeline with large diameter's elastic arm length is generally longer. Force and deformation will decrease when the short arm length shorter. This is why the bend stress decreases. However, the formula can be directly used for the actual project needs to continue to explore research. Acknowledgement This project was supported by the funding generated by the Postdoctoral Science Foundation of China (No: 2012M520606). References [1] CJJ/T81-2013.Technical Specification for Directly Buried Hot-water Heating Pipeline in City. Beijing: China Architecture & building press, 2013. [2] Wang Fei, Zhang Jianwei. Engineering Design of Buried Heating pipe. China Architecture &building press, 2007, 1(1):89-92. [3] (Danmark) Lande Lao Fupi (edit) He Ping, Wang Gang (translation) District heating Manual. Harbin Engineering University Press, 1998:29-31 [4] Wang Guowei. Large-diameter directly buried heating pipe 90 elbow fatigue life of the finite element analysis. Press, 2010:71-82. [5] Wang Fei, Zhang Jianwei. An Experimental Study on the Friction Factor of Large Diameter Precast Buried Heating Pipes. Building Energy & Environment, 2006, 25(6): 74-75 [6] Design and Installation of Preinsulated Bonded Pipe System for District Heating.BS EN13941:2003.105-107.

Applied Mechanics and Materials Vol. 721 (2015) pp 100-103 © (2015) Trans Tech Publications, Switzerland doi:10.4028/www.scientific.net/AMM.721.100

Submitted: 20.10.2014 Accepted: 22.10.2014

Multiple Weibull Statistical Model of Random Censored Data of NC Machine Tools and Optimal Estimation of Parameters Tao Fu a, Dazheng Wang b, Qinzhong Gong c School of Mechanical Engineering, Jimei University, Xiamen 361021, China a

[email protected], [email protected], [email protected]

Keywords: Mixed weibull distribution, particle swarm optimization.

Abstract. According to the truncation feature of NC machine tools, this paper adopts Johnson rank adjustment method to deal with censored data, finding the distribution model of time between failures. At the same time, in order to increase the accuracy of parameter estimation of reliability data distribution model of NC machine tools, and to avoid the shortcoming that conventional optimization algorithms is difficult to get global optimal solution due to the influence of iteration initial value, this paper uses particle swarm optimization to solve the parameter of weibull mixture model. The result shows that particle swarm optimization can balance solution efficiency and convergence performance, it is not only feasible to estimate the parameter of mixture weibull distribution, but also to get more accurate results. Introduction Considering that when we test NC machine tools though the way of field sampling timing truncation, due to the different time they have been put to use, it will result in the features of truncation, the more NC machine tools the more censored data, which will add difficulty to estimate the parameters of reliability distribution model of NC machine tools [1]. On the other hand, taking into account that NC machine tools usually have multiple failure modes and failure causes, which put a problem to the selection of reliability distribution model of NC machine tools. Literature [2] pointed out that Weibull mixture distribution is a good approximation of the real system's reliability, so multiple weibull mixture distribution is adopted, but it's parameter is difficult to estimate. The way usually used are graphic method [3], nonlinear least squares method [4-5], maximum likelihood estimation method [6-7]. In ancient figure estimation method, regression line is configurated through visual methods; it produces large deviation when data is not enough. Maximum likelihood estimation method is a kind of progressive optimization method, but it is required to solve simultaneous transcendental equations when solving multiple Weibull mixture distribution. Using conventional iterative methods is difficult to solve them and easy to fall into local minimato in the process of optimization without getting global optimal solution. The particle swarm algorithm is a computational model that simulates the mechanism of bird predation; it is a versatile and effective method for solving optimization problems due to the characteristic of parallelism and global search. So this paper adopts particle swarm optimization to solve parameters of weibull mixture distribution, then compare with the results that come from nonlinear least squares method in literature [8]. The following work are done: 1.consider that censored data affects the reliability model of NC machine tools, to make reliability analysis and estimation closer to the actual situation, particle swarm optimization is used to solve the parameter to decrease the difficulty brought about by censored data. 2. Adopt particle swarm optimization to solve the problem that maximum likelihood estimation method is easy to fall into local optimal solution and has low solution efficiency when solving the transcendental equation.

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Model and methods When censored data is failure time, Johnson [9-11] rank adjustment method can be used to decide failure order, the formula is: Rank increment =

(n + 1) - i t i

(1)

1 + number of units beyond present censored unit

Where n is the total number of units at risk and iti is the rank order of failure time i − 1 . The rank increment is recomputed for the next failure following a censored unit. Its adjusted rank then becomes: iti = iti −1 + rank increment (2) The cumulative distribution function is: ∧

F (t i ) =

iti − 0.3

(3)

n + 0.4

Table 1 shows the field failure information of 3 sets of NC machine tools. The data with "+" is censored data. Rank adjustment method is used to calculate the value of the reliability of the data. Table 1 The result of using rank adjustment method to solve censored data i

time

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

39.19 46.02 49.97 69.55 74.73 76.37 76.71 83.46 85.67 85.94 85.96 86.59 87.79 88.7 91.59 92.01 93.19 98.17 103.6+ 110.31 110.64 113.26 158.11+

Adjusted Rand

i ti

F (t i ) =

iti − 0.3

i

time

24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46

169.73 211.37+ 228.35 251.98 307.15 311.83 317.31 317.39 323.76 334.40 416.19 436.49 470..80 476.14 505.37 508.77 514.04 553.10 568.23 623.18 635.84 651.54 1119

n + 0.4

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

0.161 0.392 0.0622 0.0853 0.1083 0.1313 0.1544 0.1774 0.2005 0.2235 0.2465 0.2696 0.2926 0.3157 0.3387 0.3618 0.3848 0.4078

19.036 20.072 21.108

0.4317 0.4556 0.4794

Adjusted Rand

i ti

F (t i ) =

iti − 0.3 n + 0.4

22.187

0.5043

23.222 24.257 25.292 26.327 27.362 28.397 29.432 30.467 31.502 32.537 33.572 34.607 35.642 36.677 37.712 38.747 39.782 40.817 41.852 42.887 43.922

0.5282 0.5520 0.5759 0.5997 0.6235 0.6474 0.6712 0.6951 0.7189 0.7428 0.7666 0.7905 0.8143 0.8382 0.8620 0.8859 0.9097 0.9336 0.9574 0.9813 1.0051

The order of time between failures is t1, t 2,⋅ ⋅ ⋅, tn , and then the probability density of n-fold three-parameter Weibull distribution mixture model is: n

f ( t i , wm , θ m , β m , γ m ) = ∑

m =1

wm (t i − γ m ) β m −1 −1

β m ϑm

βm

t −γ m exp − i θ m

βm

(4)

wm > 0,θ m > 0, β m > 0, γ m > 0 is weighting factor, scale parameter, shape parameter and position parameter. Let F be the set of failure indices and C be set of censored indices. Then L = ∏ f (t i ; wm ;θ m ; γ m )∏ R(t i ; wm ;θ m ; γ m ) F

C

(5)

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Accordingly, log-likelihood functions is: n t −γ m ln L = ∑∑ ln wm + ln β m − β m lnθ m + ( β m − 1) ln(ti − γ m ) − i F m =1 θm

βm

n t −γm − ∑∑ ln wm + i C m =1 θm

βm

(6)

To solve equation (6),we usually seek the first-order partial derivatives of each parameter, and let them as zero to get equations. Then maximum likelihood estimation of parameter is obtained by the method of solving equations. However Weibull likelihood equation of N-fold multi-parameter is complex transcendental equations, they are difficult to be solved even by numerical solution. So it is a good idea to maximize the formula above directly but not to solve it, thus the problem of parameter estimation turns into an optimization problem. In order to use optimization method to find approximate solution, likelihood function is used to construct the objective function, objective function is: min F ( X ) = − ln L( X ) (7) Where, X = ( wm ,θ m , β m , γ m ),1 ≤ m ≤ n is optimization variables, the iterative process are shown in Fig.1. Results and Discussion

Fig.1 Evolutionary process Fig.2 Comparison of the theoretical and fitted swarm of particle optimization reliability functions obtained by different distributions In order to compare easily, the cumulative relative error rate of empirical distribution function Fn(t) and theoretical distribution function F(t) is defined as follows: k

ξ =∑ i =1

Fn (t i ) − F (t i ) × 100% Fn (t i )

(8)

Table 2 Estimated parameters and -lnL values for Weibull distribution with different models (results of nonlinear least squares method, literature 8) Weibull distribution 2 parameters 3 parameters

Single model

2-fold mixed model

β=1.19, θ =285.85, -lnL=302.55, ξ=14.6% β=0.91, θ=226.90, r=32.93, -lnL=296.93, ξ=10.0583%

β1=4.03, β2 =2.19, ξ=8.2923%,θ1=89.02, θ2=493.45, w=0.4623, -lnL=289.10 β1=4.01, β2 =2.11, ξ=6.2116%,θ1=88.84, θ2=473.74, w=0.5023, r1=0, r2=32.40, -lnL=287.02

Table 3 Estimated parameters and -lnL values for Weibull distribution with different models (results of particle swarm algorithm) Weibull distribution 2 parameters 3 parameters

Single model

2-fold mixed model

β=1.1972, θ=283.7710, -lnL=281.6660, ξ=5.8145% β=1.0321, θ=248.9075, r=34.7728, -lnL=277.0344, ξ=5.9812%

β1=1.2689, β2=1.2201, ξ=5.5103%, θ1=299.0627, θ2=276.1647, w=0.1257, -lnL=283.1442 β1=1.0271, β2=1.0385, ξ=5.2690%,θ1=253.4287, θ2=258.9867, w=0.4905, r1=31.0031, r2=28.4401, -lnL=271.3460

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Table 2 shows the results after using nonlinear least squares method, while table 3 shows the results after using particle swarm algorithm. Compare negative logarithmic likelihood values, the one come from table 3 is smaller and close to exact solution. Table 3 also gives related estimate of single model, which is a special case of the mixed model. About the negative likelihood logarithmic value, 3-parameter Weibull model is smaller than the 2-parameter Weibull model, the mixed model is smaller than single model, and the lowest appears in 2-fold 3-parameter mixture distribution, the solution of which is close to exact solution. Figure 2 shows failure data approximating figure of reliability function, single model,2-fold 2-parameters and 2-fold 3-parameter hybrid model. As can be seen from the figure, all of them can better fit the data. We can see from 2-parameter and 3-parameter Weibull distribution in Table 3, due to the introduction of the positional parameters, shape parameter reduce obviously and is close to 1, explaining that NC machine tools are in the accidental failure period. Since failure occurs randomly, so we can adopt Fault Monitoring Maintenance or regular maintenance. According to the positional parameters, we can be reasonably made early troubleshooting time. As is shown in table 3, the cumulative relative error rate of 2-fold 3-parameter distribution is lower, so we adopt it to fit the data, the probability density of the model is: t − 31.0031 1.0271 t − 28.4401 1.0385 −3 0.0385 + × f (t ) = 1.7 × 10 −3 t 0.0271 exp − 1 . 9 10 t exp − 253.4287 258.9867

(9)

When the above function is defined, the reliability of this 3 sets machine tools can be estimated. Conclusion Adopts Johnson rank adjustment method to pretreat failure data, taking trunkated time into account makes analysis more practical. According to the shortage of maximum likelihood estimation method when solving transcendental equation of weibull mixed distribution, we maximize equations directly, turning the problem of parameter estimation into the optimization problem, then use Genetic Algorithms to solve it. The result shows that Genetic Algorithms can balance solution efficiency and convergence performance, it is not only feasible to estimate the parameter of mixing Weibull distribution by using this method, but also to get more accurate results. The method can be effectively applied to other distribution parameter estimation. Acknowledgement The project was supported by the Natural Science Foundation of Fujian (NO.2011J01321) and Major research projects of Fujian (NO. 2012H6016). References [1] Zhang Yingzhi, Shen Guixiang, Wu Kui, Xue Yuxia, He Yu: Journal of Jilin University (Engineering and Technology Edition), Vol.39 (2009) No.2, p.378-381. [2] Jiang R, Zuo M J, Li H X: Reliability Engineering and System safety, Vol.66 (1999) p.227-234. [3] Jiang R, Murthy D: IEEE Transactions on Reliablity, Vol.44 (1955), p.477-488. [4] Markovic D, Jukic D, Bensic M: Journal of Computational and Applied Mathematics, Vol.228 (2009), p.304-312. [5] Jukic D, Bensic M, Scitovski R: Computational Statics and Data Analysis, Vol.52 (2008), p.4502-4511. [6] Howard R, Charles A, Lawrence A K: Journal of American Statistical Association, Vol.69 (1974) No.345, p.246-249. [7] Huang W, Dietrich D L: IEEE transactions on reliability, Vol.54 (2005) No.2, p.310-317. [8] Wang Zhiming, Yang Jianguo, Wang Guoqiang, Zhang Genbao: Journal of Mechanical Engineering Science, Vol.225 (2011) No.11, p.2718-2726. [9] Johnson L G: Theory and technique of variation research. Amsterdam: Elsevier, 1964. [10] Johnson L G: The statistical treatment of fatigue experiments. Amsterdam: Elsevier.1964. [11] Wang W D: Quality and Reliability Engineering International, Vol.20 (2004) No.7, p.667-678.

Applied Mechanics and Materials Vol. 721 (2015) pp 104-108 © (2015) Trans Tech Publications, Switzerland doi:10.4028/www.scientific.net/AMM.721.104

Submitted: 20.10.2014 Accepted: 28.10.2014

Controllable of Underwater Towing Tension Passive Compensation Device Design Research Jing Meng1, a, Zhixin Chen2, b, Tao Jiang2, c and Zhiqiang Xu2, d 1

Fishery Machinery and Instrument Research Institute, Chinese Academy of Fishery Sciences, Shanghai 200092, China; 2

College of Engineering, Shanghai Ocean University, Shanghai 201306, China

a

b

c

d

[email protected], [email protected], [email protected], [email protected]

Keywords: Passive compensation, accumulator, the simulation analysis, tension control.

Abstract. In order to solve the process of fishing, hull has significant heave motion under the influence of marine environment, that caused towing tension instability and fishing low efficiency problems, proposed and developed based on the hydraulic cylinder and the accumulator between the energy conversion and PLC control of towing tension range controllable passive compensation device. According to its working principle, the passive compensation device of compensation system and control system was designed, using the hull heave motion as the control energy, hydraulic cylinder and accumulator combination as a compensation system, selects PLC224CPU and M235 expansion module as the control unit. Finally, the towing tension range change of compensation has carried on the simulation analysis, the simulation results show that the passive compensation device which can realize the adjustment and control of drag tension range, achieves the expected control effect. Introduction Marine fishing operations, because the hull is influenced by the Marine environment have a significant heave movement, resulting in relatively hull trawl moves up and down or so drag steel tension is too large or too small, make the trawl net type variation influence the fishing efficiency[1]. Traditional towed stable tension compensation mainly by drag classes winch tightening, relaxation drag operation to guarantee the stability of the heave drag around the outline tension, but this stability compensator need frequent starting the winch motor, and with the rise and fall of hull, constantly changing the direction of rotation of the motor, great loss of motor, which greatly shortens the service life of the motor[2]. Therefore proposed and developed a passive heave compensation system, which when trawling process, according to the ship's heaving motions automatically adjust the length of warp, guarantee the stability of the drag steel tension that improve the efficiency of the fishing, and don't need to start the winch motor in the process of adjustment. Heave compensation system has been reported rarely at home and abroad, in foreign countries, the United States Global Marine Inc.[3], Germany Liebherr and Norway Hydralift[4]etc. which have been carried out series of studies on the heave compensation system. Domestic, China University of petroleum Lu Bai [5]studied the hydraulic heave compensation system of the drill string, Central South University, Baihai Wu and Shaojun Liu [6,7] etc. It has a lot of research on the bottom of the sea mining heave compensation device, made a series of achievements. While the heave compensation system for trawlers, the present study is less. In this paper, the job of towing system in the complex of the marine environment, proposed a kind of passive compensation device with accumulator[8],according to its working principle, for passive compensation device of compensation system and control system design. Finally, on compensation drag tension range of changes doing simulation analysis, the simulation results show that the passive compensation device which can realize the adjustment and control of drag tension range, achieve drag the effect of constant tension control increased fishing efficiency. Verify the system is effective and feasible.

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Passive compensation system composition and working principle This design adopts the method of energy conversion between hydraulic cylinder and the accumulator, using the heave motion of the hull of the ship as power energy to compensate[9]. The compensation way has the characteristics of energy saving, simple structure, in the process of reactive compensation system will be the waves heave movement into internal energy accumulator, according to different drag steel tension around, through storage and release of the accumulator, automatically adjust the length of warp, which is to ensure the warp tension stability[10]. The passive compensation device is installed between the fuel tank and the Gang drag nets, by hydraulic cylinders, accumulators, cylinders and piping group constitute, unilateral passive compensation device structure shown in Fig.1.

(1) Left towing winch (2) Wire rope (3) Pulley (4) Ceramic oil cylinder (5) Pulley installation seat (6) Accumulator and Gas cylinders group Fig.1 System layout diagram The accumulator is a sealed chamber, a piston chamber configuration, the chamber is divided into isolated oil chamber and gas chamber, the gas chamber filled with compressed air, oil chamber and the lower chamber is connected through a hydraulic cylinder. Compensation cylinder used to bear the weight of the load, the accumulator is used to store and release the hull heave energy[11]. System through the accumulator absorbed and storage hull ups and downs movement to reduce trawling's effects, it does not require frequent start winch motor, can realize the warp tension passive balance function. Working principle of the reactive compensation device: when the hull is relatively trawl upward movement, wire rope tension increased, compression passive compensation oil cylinder piston rod, the hydraulic oil into the accumulator, the pressure in the accumulator rises, completing the process of energy storage; When the hull downward movement relative trawl, accumulator stored energy release, push the piston rod and release rope, compensate for the difference of displacement relative motion. Through accumulator energy storage and release the process to reduce wire rope tension change, ensures the drag tension stability, to achieve a good compensation effect. Passive compensation device design Compensation System. Analysis of unilateral compensation system (system of compensation for the other side is exactly the same), the left of the hydraulic cylinder piston, piston compensation on the movable pulley set, a pulley shaft and support of the movable pulley block support together as analysis unit. The hull heaves motion equation for x = A sin 2π t / T , analysis with hull heave motion the drag tension force situation. As the research object to the cylinder plunger (piston weight is negligible) by the balance equation was: mx1 = P2 A − 2 F (1) In the formula: m for the quality of the plunger block m=253.1Kg; x for the components of acceleration, x1 component of displacement x1=x/m0, m0 magnification of for pulley m0=2, x for the trawler's heaving motions equation x = A sin 2π t / T ; P2 for the hydraulic cylinder of hydraulic oil

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Vehicle, Mechanical and Electrical Engineering

pressure; A for the effective sectional area of the plunger, A= π D2/4, D for compensate hydraulic cylinders diameter D=260mm; F for towing rope tension. Accumulator mathematical model analysis, accumulator piston weight and friction are ignored, and ignore the accumulators and hydraulic cylinders of hydraulic oil pressure loss in pipes, hydraulic cylinder of hydraulic oil in the nitrogen pressure in the pressure accumulator are equal, that is: (2) P2 = Px When the piston in the cylinder position in the accumulator the nitrogen volume for V1 pressure for V1, V1= V0- SA/2 for storage device total volume, S for hydraulic oil cylinder of total trip S=420mm, S1 for compensation hydraulic cylinder of compensation trip S1=100mm, the plunger in the x position in the accumulator nitrogen volume is Vx=V1+ ∆V =V1+ x1A, at this time nitrogen pressure for Px, according to the Boyle's law of thermodynamics, the plunger in the x position, in the accumulator nitrogen pressure Px is: Px = P1 (V1 / VX )

1.4

Px = P1 (V0 − SA / 2) / ( (V0 − SA / 2) + x1 A )

1.4

(3)

By the formula (2) shows the position of the plunger in the x , the hydraulic cylinder of the hydraulic oil pressure is: 1.4 (4) P2 = Px = P1 (V0 − SA / 2) / ( (V0 − SA / 2) + x1 A ) With the hull doing heave motion towing tension equations by formula (1) (4) finishing: 1.4

(V0 − SA / 2) m PA (5) F =− x+ 1 4η 2η (V0 − SA / 2) + 0.5 xA When the heave motion equation of the hull is x = A sin 2π t / T = 0.5 sin π t / 6 , in accordance with the requirements of the design to ensure that the drag force changes in the range of 2.8 × 103N ≤ F ≤ 4.3×103 N. Control system. Considering the PLC system has simple structure, easy to use, easy to implement automation control, high reliability, so this design uses the Siemens S7-200 series modules as the control unit of passive compensation device coordination control[12].The solenoid valve, proportional amplifier VT2000, proportional relief valve, proportional amplifier VT5006, electromagnetic proportional directional valve, tension sensors, displacement sensors and pressure sensors connected to the PLC. Selection of Siemens series PLC CPU 224XP (digital 14 in 10 out, analog 2 in 1 out) and extend the EM235 module (analog 4 in 1 out) as the controller. Electromagnetic proportional directional control valve is used to control the winch motor in fishing systems play a role of SHOOT and HEAVE, electromagnetic directional valve control passive compensation system coordination operation, pilot proportional overflow valve is used for adjusting oil pressure in the hydraulic system. Its core technology is the displacement, tension, pressure sensor signal transmitted to the centralized management system (such as a computer,PLC-200controller, etc.), then by the centralized management system analyzes the data after conversion to transmit control signals transferred to the actuators accordingly output control process, the principle shown in Fig. 2.

Fig. 2 Principle diagram of the control system

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For the convenience of the user operation, using MCGS7.2 the PC program design of development into a graphical interface forms[13]. Building a centralized management platform with MCGS interface, complete with PLC data communication settings. On the configuration interface written analog buttons, control electromagnetic proportional directional valve and electromagnetic directional valve position, while the position of the state of the electromagnetic proportional valve and solenoid valve to the way light is on or off the feedback on the configuration interface. And in a configuration interface write analog input components, Control the variable displacement pump and the winch motor reversing etc. at the same time, the value of the pressure sensor, displacement sensor feedback on the configuration interface display. By operating configuration interface enables precise control of external devices work process. Compensation drag tension simulation analysis According to the compensation system design parameters and equations relationship, Using MATLAB/Simulink on the design of compensation system towing tension variation of simulation analysis[14]. The trawlers heave motion equation for x = A sin 2π t / T = 0.5 sin π t / 6 as sine waves. Other known input parameters, the compensation obtained by simulation boxes tension curve shown in Fig. 3.

Fig. 3 Compensation drag tension simulation curve Fig.3 shows that when not installed passive compensation device, the simulation shows drag tension range of fishing equipment around 2.0×103N~5.0×103N, the range of visible tension fluctuation is larger, which makes the trawl net type variation is the fishing efficiency is low. However, after the application of passive fishing drag tension compensation device changes can be controlled between2.8×103N~4.3×103N, visible passive compensation device can effectively reduce the fluctuation range of the tension that increases the drag Gang tension stability. The simulation shows a passive compensation device designed in this paper is feasible. But the inadequacy of the compensation precision passive compensation device is not too high, in order to further improve the stability of the drag tension also need to increase other devices, this issue needs to be further addressed. Conclusion Through the storage and release the hull heave motion energy so as to reduce the impact of trawl , drag the tension change has been effectively controlled, not only low energy consumption, environmental protection and clean

108

Vehicle, Mechanical and Electrical Engineering

The compensation process does not require human intervention, Mechanism of drag the tension adjusting convenient intuitive, and the adjusting range is extensive; Compared with other similar elastic compensation configuration structure, hydraulic mechanism of small volume, good layout; Does not need frequent start the winch motor, adjust the winch motor rotation direction to balance the two drag of tension, prolong the service life of the winch motor. Acknowledgement The project funds: The project is supported by the national science and technology support plan (2013BAD13B02) (Research on the Key Technology of New Materials Ocean Energy Saving and Fishing Equipment). References [1] S.L. Liu: Fishery Modernization, Discussion on Chinese Ocean Fisheries Development and Fishing Equipment, Vol. 37 (2012) No.6, p.56-60. [2] X.J. Wang, Y.Y. Zhang: Marine Engineering, (The Composite Cylinder Wave Compensation Device, Vol. 3 (2013) No.8, p.86-89. [3] W.L. Chu, L. Chang: Fisheries Research, Simplified Trawl System Modeling and Design of Depth Control System Using Fuzzy Logic, Vol. 5 (2010) No.3, p.84-94. [4] H.V. Hausa: Research Vessel Design, Hydraulic Deck Equipment for Research Vesse, Vol. 9 (2006) No. 2, p.23-25. [5] L. Bai, Y.T. Zhang and Z.L. Zhang: Oil Field Machinery, The Drill String Hydraulic Heaves Compensation System Parameter Calculation and Comparative Analysis, Vol. 38 (2009) No.3, p.10-13. [6] X.Y. Tang, S.J. Liu: Journal of Central South University, Deep Sea Mining Heave Compensation System Modeling and Fuzzy Control Simulation, Vol. 39 (2008) No.1, p.128-131. [7] J. Ni, S.J. Liu and X.F. Li: Computer Simulation: Deep Sea Mining Modeling and Simulation Research on Passive Heave Compensation System, Vol. 26 (2010) No.3, p.13-17. [8] H.B. Wang, Q.F. Wang: Ocean Engineering, Heave Compensation System Research on Hydrodynamic Mathematical Model of Underwater Towed, Vol. 26 (2012) No.4, p.37-39. [9] G.D. Liu: Macroeconomic Management, Support and Expand China's Offshore Fishing Industry, Vol. 16 (2011) No.1, p.42-44. [10] L.J. Ding, X.T. Bao and J.H. Zhang: Fishery Modernization, European Fisheries Energy Saving Technology and Measures, Vol. 37 (2010) No.5, p.60-63. [11] H. Xu, X.Y. Zhao and H. Liu: Fishery Modernization, Study on the Development Strategy of China's Marine Fishing, Vol. 8 (2012) No.1, p.21-24. [12] W.L, Yang, L.Y. Zhang: Ocean Engineering, Underwater Robot Active Heave Compensation System Research, Vol. 26 (2007) No.8, p.68-72. [13] W. Zhang: The MCGS Configuration Software Reference Manual, (Beijing Aviation Institute Publications, Chinese 2012), p.224-258. [14] Li Ying: Simulink Modeling and Simulation of Dynamic Systems, (Xi'an Electronic Technology University Publications, Chinese 2009), p.121-168.

Applied Mechanics and Materials Vol. 721 (2015) pp 109-112 © (2015) Trans Tech Publications, Switzerland doi:10.4028/www.scientific.net/AMM.721.109

Submitted: 22.10.2014 Accepted: 28.10.2014

Engineered-based Machining Parameters Analysis for Aircraft Structural Parts Fang Zhu1, a, Xiongfei Huang2, b and Lijun Meng3, c 1

Department of Aircraft Maintenance and Engineering, Guangzhou Civil Aviation College, Guangzhou 510403, China 2

Department of Marines, Naval Marine Academy, Guangzhou 510430, China

3

School of Electromechanical and Architectural Engineering, Jianghan University, Wuhan 430056, China a

b

c

[email protected], [email protected], [email protected]

Keywords: aircraft structural part, machining parameters, engineered model, cost constraint.

Abstract. Modern aircraft structures need to be further integrated and parts to be enlarged, the structural parts which require high accuracy of size, shape, and good deformed surface finish, so digital machining process has become key technology in aviation industry. Machining parameters selection is an important phase of machining process, it has very important influence on the dimensional quality of an aircraft structural part. This paper develops a methodology to get an optimal machining parameters in an aircraft structural part machining process, which can satisfies the quality specifications and minimizes the expected value of the sum of machining costs at the same time. An engineered-based model is adopted to describe the machining process of aircraft structural part firstly and then a quantitative machining parameter evaluation methods driven by cost constraint is proposed, an optimal machining parameter is obtained through a computer experiment model which can explore a large number of machining process parameter alternatives. At last, we illustrate the validity and the significance of the engineered-based machining parameters analysis method for aircraft structural parts by a case study. The results have shown that the proposed method could significantly increase the efficiency of aircraft structural parts machining process. Introduction Aircraft and aeronautical manufacturing technology has the following tendency: digital manufacturing technology has become key technology in aviation industry, mechanical processing is under development towards high efficient CNC machining, an aircraft structural part is machined through the sequential machining and multiple setups and introduces deviations that propagate through the overall process and influence the final product quality. In this process, the deviation of the quality feature at one stage is caused by both local deviations at the current stage and the propagated deviations from previous stages because key quality characteristics of one stage are used as the datum for its following stage. In an aircraft structural part machining process, different setup plans have different datum and fixture schemes, which may introduce different deviation propagation situation that will propagate along all the machining stages and accumulate in the final product. In order to ensure product quality and opportune machining cost, the optimal machining parameters must been identify from multiple setup plans options. Since the later 1990's, extensive research work has been done on setup planning design. In the single stage machining process, H.C. Wu and T.C. Chang [1] proposed that setup planning is a critical part of automated process planning. A review about some rules for setup adjustment have been given by Del Castillo et al[2], these authors show how the previous setup adjustment rules are all cases of Linear Quadratic Gaussian control. In the case of making some adjustments of setup planning in a multi stage process, Wang and Huang [3] developed an automatic process adjustment method to compensate the mean shift of machining processes by adjusting fixture locators. Thavanrath

110

Vehicle, Mechanical and Electrical Engineering

Chaipradabgiat [4] has researched the optimal fixture locator adjustment strategies for multi-station assembly processes, this method is not directly applicable to the setup planning in machining processes due to the complex interrelations among multistage machining. Considerable efforts have been made to study the setup and manufacturing cost, works published such as Lian and Del Castillo [5], they have done great efforts to control a manufacturing process to achieve those cost objectives with great success. In this study, the accurate model and methodologies for simulating the machining process is developed and the dimensional deviations is predicted in the aircraft structural part produced. Then a quantitative machining parameters evaluation method driven by cost constraint is proposed to clarify what is optimality of machining parameters. At last, a computer experiment model is developed to explore a large number of process design alternatives, which provides guidance for process design. The Engineered-based aircraft structure part machining parameters analysis The proposed methodology deals with the machining parameter analysis problem in an aircraft structural part with an integrated setup and fixture planning strategy. The proposed method is shown in Fig. 1 Generating the candidate machining parameters

Evaluating the candidate machining parameters

Solving the problem

Quality=f (fixture planning) based on engineered model

A sequential decision making process

A computer experiments design Kriging model

Candidate fixture formation

Quality specifications Constraint

Candidate setup formation

Cost criterion

Output

Candidate setup sequence

Optimization problem formulation

Optimal machining parameters

Fig.1 General Framework of the proposed methodology Existing machining process models roughly fall into three categories: (1) worst case model, root sum square model or interpolation of these two models, (2) Monte Carlo simulation models, and (3) engineered models that study the impact of process variables on tolerance accumulation. The last type provides a new opportunity of simultaneously allocating product and process parameters. The paper adopted the last type model. To limit the scope of this model, we will focus on datum variation and fixture variation. For an N-stage process, the model is in the form of a linear relationship, as shown in Equation (1): (1) Where Ak-1 represents the variation propagating effect, xk-1 is the key quality character. uk is the fixture variation at stage k. Bk represents a contribution of the fixture offset uk at the current stage k on the product characteristics xk. Ck is the measurement matrix. The natural variation and unmodelled variations in the process are represented by a noise input to the system, noted as wk, while vector vk corresponds to sensor noises. Equation (1) can be re-written as: (2) where is the state transition matrix tracing the datum schemes transformation from stage i to k-1, that is, for k＞i and . x0 is assumed to be a zero vector. So, for a selected fixture layout fk in stage k, the state space model will be: (3)

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For,k>i, ,is the decisions on corresponding datum schemes at upstream stages. The ultimate goal of machining parameters analysis in this paper is to achieve the highest product quality at the lowest cost, so a cost function needs to be defined for an objective function in order to obtain an optimal machining parameters decision. A Taguchi quality-loss function is used as the cost function. According to the definition, the expected value of the quality-loss function can be expressed as: Loss cost = b[σ u 2i + (µui 2 − Tui )2 ] (4) Where b is the loss coefficient, and should be determined according to the practical situation. representing the tolerance of a corresponding fixture variable, σ u i is the standard deviation of yi. The optimal setup planning decision is determined by minimizing this expected total cost function. The determination of an optimal machining parameters decision is a sequential decision problem. In machining stage i , be corresponding to the fixture layout from fi to fi+1, the datum scheme changes from di to di+1. Then, to fixture layout fi, the equations describing the product quality can be expressed as follows: (5) and the product To fixture layout fi, the process status is represented by the fixture location offsets quality measurements .Therefore, the state variables at stage i are defined to include fi, , and , which can uniquely represent the process status at stage i. This cost can be interpreted as the cost consumed to provide enough process precision for stage k, corresponding to the selected fixture layout fk. A computer experiments design will assist to establish a surrogate prediction model and to search the optimal process parameters. The Kriging model can be used to solve this function. The optimal setup planning strategy is determined recursively backward from the terminal condition to the beginning point fixture layout choice 1. The backward induction method can get the ideal fk .The iterative model refinement steps are stated as: (1) Kriging model fitting. In the ith iteration, construct the Kriging model based on ni available experimental design S= {s1,...,s ni } with response data Vs = { V1,..., V ni } (2) Model refinement. Calculate RMSE at test points generated by maximin-LH design and add the points that yield large RMSE to the experimental design. (3) Let i ←i+1 and repeat procedures (1) and (2). Case study A case study is conducted to demonstrate the developed method by applying it to a coverplate machining process of a maintenance door, which is an example of an aircraft structural part. The part design and their associated design specifications are defined in Fig. 2.

Fig.2 Design specifications of the machining part

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There are three fixture systems consisting of 18 locator position deviations formed by ui at each stage i(i=1,2,3). Corresponding to these fixture layout candidates fk(k=1,2,3), the coefficient matrices in state space models, were determined numerically used quation (1). In the case, without loss of generality, only the variation of fixture locators are included in the process variable vectors . To different fk, can get . According to those assumptions on fixture layout, based on Kriging model, the computer experiment outcome is established, as shown in Table 1. Table 1 Cost of different fixture layout Q1

Q0 f1 0

f2 0

f3 0

f1 1

f2 1

f3 1

f1 2

y

y

y

y

y

y

y

33.652

41.547

19.713

36.767

20.145

23.339

36.716

Q2

y2f3

f2 2

y

33.428

Q3

34.137

f3 0

As shown in Table 1, the optimal machining parameters are identified as the y --- y1f2 --- y2f2 with the total cost of 73.286. Concluding remarks In this paper, a new methodology was developed a model-based approach to determine an optimal machining parameters in an aircraft structural part machining process. The case study was implemented to obtain an approximate solution and demonstrated that the proposed method can minimize the cost with the final product quality at the same time satisfying the key product characteristic quality constraints. References [1] H.C.Wu and T.C.Chang. Automated Setup Selection in Feature-Based Process Planning.

International Journal of Production Research, 1998, Vol.36, No.9, p.2325-2341. [2] Del Castillo. E., Pan. R and B.M.Colosimo: A unifying view of some process adjustment method. Journal of Quality Technology, 2003, Vol.35, p.286-293. [3] Wang,H. and Huang,Q. Using error equivalence concept to automatically adjust discrete manufacturing processes for dimensional variation control, ASME Transactions, Journal of Manufacturing Science and Technology, 2007, Vol.129 (2), p.644-652. [4] Thavanrath Chaipradabgiat, Jionghua Jin and Jianjun Shi. Optimal fixture locator adjustment strategies for multi-station assembly processes. IIE Transactions, 2009, 41, p.843-852. [5] Lian, Z. and Del Castillo, E. Setup adjustment under unknown process parameters and fixed adjustment cost. Journal of Statistical Planning and Inference, 2006,

Applied Mechanics and Materials Vol. 721 (2015) pp 113-117 © (2015) Trans Tech Publications, Switzerland doi:10.4028/www.scientific.net/AMM.721.113

Submitted: 27.10.2014 Accepted: 01.11.2014

Parametric Design of Deep Groove Ball Bearing Based on Pro/Program and Family Table Naiming Miaoa School of Mechanical Engineering, Jiangsu University, Zhenjiang 212013, China School of Mechanical Engineering, Changzhou University, Changzhou 213164, China a

[email protected]

Keywords: deep groove ball bearing, parametric design, Pro/Program, family table.

Abstract. Due to the same series of rolling bearing standard part with the same topology structure and different dimension parameters, a series of rolling bearing products can be designed by parametric design method. Based on the development tool of Pro/Program and family table combined with three-dimensional modeling, this paper takes deep groove ball bearing for example to discuss the means and steps of rolling bearing parametric design. By entering the relevant parameters, such as the bearing outside diameter, bearing inside diameter, bearing width, ball number and other known conditions, we can accurately and quickly generate a new rolling bearing solid model. The result shows that in the design environment of Pro/ENGINEER, using parametric design to make three-dimensional modeling can shorten design cycle time and improve design efficiency significantly. Introduction Bearing, an important part supporting the shaft and its pieces, is mainly used to reduce friction and wear between a rotating shaft and a support, can also keep the running accuracy of shaft [1]. The rolling bearing has many advantages, such as flexible start, small frictional resistance, high efficiency, simple lubrication, it is widely used in the mechanical industry [2]. At present, domestic and foreign rolling bearings become more and more light-weighted, miniaturized, componentized and customized in variety and specifications. The rolling bearing is one of the highest standardized and serialized degrees, the same series of rolling bearing standard part with the same topology structure and different dimension parameters, so a series of rolling bearing products can be designed by parametric design method. While the greatest advantage of parametric design is that the similar parts can be achieved by modifying the model dimension. Pro/ENGINEER, a well-known 3D product design software of PTC Company in America, now has become one of the most popular CAD/CAM/CAE software in today's world since its creation in 1988, which is widely used in electronics, communications, machinery, mould, industrial design, auto, bicycles, aerospace, household appliances, toys and other industries [3]. The parametric design [4, 5] means that, the relevant parts of the sketch are modified automatically by amending the dimensions of one or more parts of the sketch , thus realizing the quick design of the mechanical products of similar structures. The introduction of parametric design could effectively reduce the time required for artificial change of the sketch or design calculation, greatly enhancing working efficiency. Based on the properties of rolling bearing structure, this paper takes deep groove ball bearing as an example to discuss the methods and procedures of parametric design of rolling bearing based on two development tools of Pro/Program and Family Table of Pro/E software. The parametric design based on Pro/E Parametric design. The main function of Pro/E is the parametric feature-based modeling design to drive the entity through complete and accurate parameters and data, each design size of the product model has a corresponding parameter. Pro/E also provides a method to establish parameter symbol, size, tolerance, and relation between characters by arithmetic operation, logical operators, standard

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mathematical functions and curvilinear relationship, we can enter the information, the parameter relationship between the features of position and shape of the modified parts in the form of the command line or relational file to create the required model. On the Pro/Program. The three-dimensional model of product can be easily built using Pro/E, while the Pro/Program of which realize the parametric design thought. The Program in Pro/E is a log file which records the information such as the modeling procedures and conditions of the model, most of which are generated automatically by Pro/E, namely every time a feature is established. Pro/E create the Program of this feature, then the users can edit it according to the design intent, which makes the product design more flexible, and it takes little time to design different products . ON the Family Table. Family Table is a tool using the table to drive the dimension parameter model, entering the dimension parameters, features and model parameters that can be driven in the universal parts model into the table to create a new part by entering new parameters into the table. The functions of Family Table are to generate and store a large number of simple and careful subjects, to generate a series of parts from general part files without re-construction, thus generating series parts saves time and energy. Parametric modeling of deep groove ball bearings Deep groove ball bearing consists of the coaxial distributed inner ring, outer ring and ball, the general sketching is as shown in Figure 1, the nominal parameter includes bearing outside diameter, bearing bore diameter, bearing width and the number of ball bearing [6]. In the early time of product design, after determining the model, use the following set of data as known conditions for creating the common parts of shaft: bearing inside diameter (20), bearing outside diameter (32), bearing width (7), and ball number (4).

Fig. 1 The general drawing of deep groove ball bearing Fig. 2 the sketch of inner and outer ring Create nominal parameters. Before modeling, create the above four nominal parameters and the auxiliary parameter A in the program and define the relationship between A and bearing bore diameter and outside diameter. The method is as follows: tools→programs→edit design→open notepad to add the input parameters between INPUT and END INPUT, the program as follows: INPUT N_D NUMBER; /* bearing bore diameter W_D NUMBER; /*bearing outside diameter B NUMBER; /*bearing width Z_TH NUMBER; /*ball number END INPUT RELATIONS A= (W_D－N_D)/2; /*auxiliary parameter A END RELATIONS “Do you want to incorporate your changes into the model: [Yes]” appears on the info window, then Click Yes to incorporate the program into the model →Enter→Input the following known

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conditions for the bearing design: bearing bore diameter(20), bearing outside diameter(32), bearing width(7), ball number(4). Create the inner and outer rings of bearing. Take FRONT plane as the sketching plane, to create the inner and outer rings of bearing using rotation method [7]as shown in figure 2, turn 360 degrees to get the three-dimensional model of the inner and outer rings of bearing as shown in figure 3. Click on the newly built 3D model, Click on tools→ relationship, to add the relations as shown in Figure 4 to the design size of the inner and outer rings d2~d9→determine.

Fig.3 3D model of bearing inner and outer rings

Fig.4 Schematic of bearing inner & outer ring added relationship

Create a ball. Take FRONT plane as the sketching plane to create a ball by rotation, then create all balls arrayed around the central axis of bearing, to get the entity model after arraying as shown in Figure 5( the discrete angle is 90° and the number of the initial arrays is 4) . Click on tools - > relationship, to add the following relationships to the design size and array size of the ball → determine. d12=A/2; ball diameter d13=N_D/2+A/2; diameter of ball distribution circle d14=360/Z_TH; the array discrete angle while arraying p17=Z_TH; arraying ball number

Fig.5 The initial bearing model after array

Fig.6 Bearing parameters manager

Fig.7 Input new value dialog box

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Parametric design combined with Program module. Using the program through the embedded Program module of Pro/E to design the fully parameter-driven parts can effectively address the problem of the serialization oriented design of similar parts. With the editing for Program, the creation of the automation features of Pro/E can generate different versions of model. Click on the "tools" - > "program" - > "edit design", insert the following statement between INPUT and END INPUT in the popup notepad editor, then the bearing characteristic parameters can be inputted. W_D NUMBER Please enter the size of bearing outside diameter. N_D NUMBER Please enter the size of bearing bore diameter. B NUMBER Please enter the bearing width Z_TH NUMBER Please enter the ball number Once the edits are completed, save the parameterization design program, with the execution of regeneration commands, Pro/E system will execute this program automatically, the users can generate new bearing models just by entering the characteristic parameters that need to modify. Figure 6 shows the editor for bearing parameters, choose corresponding parameters through the editor to input corresponding values, to generate the bearings with different parameters. Figure 7 shows the dialog box into which a new value is entered, Figure 8 shows the before-and-after comparisons of bearing models after changing the parameters.

(a) N_D =40, W_D =52, B =7, Z_TH =26 (b) N_D =25, W_D =47, B =12, Z_TH =12 Fig.8 The before-and-after comparison chart after changing the parameters. Create a family table. Parametric design based on Pro/E is realized with a combination of 3D modeling, process control and family table. First create a representative three-dimensional part model by human-machine mode, which is called universal parts. Based on the created universal parts 3D model, further create a set of design parameters which can completely control the shape and size of the 3D model of parts according to the design requirements and size relations, establish the relationship between the design parameters and do an assignment by compiling the control program. After the completion of the above, add a series of design parameter value in the family table, the system will automatically regenerate, thus generating a number of sub-components of different specifications. The parts family table of the inside and outside circle, holder, rolling body of rolling bearing can be established using this method, then use the assembly function of Pro/E to assemble the parts of the same model, not only the classification is clear but also the product model can be quickly obtained in the production process. We do not need to spend a lot of time on product modeling to establish various types of bearing model, which greatly improves the design efficiency and shortens the design cycle.

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Conclusions (1) Using Pro/E to make a 3D parametric design for bearing can get a new model by modify the main control parameters and regeneration, the parametric design make the product design more selective and more flexible. (2) Using the Pro/Program module of Pro/E to design deep groove ball bearing can establish the required similar product model automatically by editing the Program, which shorten the modeling time and development period of the similar products, improve the design quality and efficiency. (3) Pro/E family table brings great convenience to seriation design of rolling bearing product, makes the establishment and modification of model more convenient, and provides methods and thoughts for seriation design of other similar mechanical products. Acknowledgements The work was supported by the Natural Science Foundation of Jiangsu Province (BK2011238). References [1] Xiongchao, Xiong Hegen. The parametric design of self-aligning ball bearing based on Pro/E. Mechanical and electrical engineering technology, 2010, 39 (9):p.69-72. [2] Pu Lianggui, Ji Minggang. Mechanical design (8th ed). Beijing: higher education press, 2006.5. [3] Lin Qingan. Introduction of fully proficient in Pro/Engineer wildfire 5.0 mould design foundation. Beijing: Electronic Industry Press, 2011.3. [4] Han Guocai, Zhangli. Parametric design of machinery parts special model library based on Pro/E. Journal of manufacturing automation, 2006, 28 (4) p.14-16. [5] Tian Qihua, Zhaowei, Du Yixian. The research and implementation of Mechanical parts 3 d CAD system based on Pro/E. Mechanical manufacturing, 2004,42 (476) p.42-44. [6] Cheng Daxian. Mechanical design manual. Beijing: Chemical industry press, 2008. [7] ZhaYougang. Pro/ENGINEER mechanical design course of wildfire version 5.0. Beijing: Mechanical industry press, 2010.

Applied Mechanics and Materials Vol. 721 (2015) pp 118-121 © (2015) Trans Tech Publications, Switzerland doi:10.4028/www.scientific.net/AMM.721.118

Submitted: 27.10.2014 Accepted: 01.11.2014

Parameter Study of Opposite Tape-spring Flexure Hinge Hui Yanga, Rongqiang Liub, Yan Wangc and Jianguo Taod School of Mechatrontics Engineering, Harbin Institute of University, Harbin 150080, China a

[email protected], [email protected], [email protected], [email protected]

Keywords: Folding and deployment, tape-spring, flexure hinges, finite element analysis.

Abstract. Parameter study for the qusai-static folding and deployment of opposite tape-spring flexure (OTSF) hinges is presented. The full factorial method is employed to design of experiments, and the qusai-static folding and deployment nonlinear analysis is obtained by ABAQUS/Explicit solver. Parametric studies show that both the section radius and the central angle have significant effects to the peak moment of quasi-static folding and deployment of the OTSF hinges. However, the maximum Mises stress in complete folding configuration is more sensitive to the central angle than the section radius. Introduction Flexible hinges are folded elastically and are able to self-deploy by releasing stored strain energy, which are just consisted of only several components and can be manufactured conveniently. They have several advantages for space applications including lower mass-to-deployed-stiffness ratio, lower cost and self-latch. Some researchers tried to extend the tape-spring to larger structures by adopting monolithic structures which included thin-walled cylindrical shells [1], two omega-shaped thin metal shells [2] and large tape-spring [3]. MOBREM and Adams [4] showed that the total stowed energy caused by structural behaviors of flexible hinges is needed to optimize to control the dynamics of deployment while ensuring that the structure will self-deploy. The opposite tape-spring hinges possess the property of more uniform stress distribution. Parameter study is main contribution of this study. Problem Description The common configuration used in opposite tape-spring flexure (OTSF) hinges is shown in Fig. 1. The total length of OTSF hinges L is fixed as 100 mm, the thickness t is 0.12 mm and the length of clamp end lc is 10 mm. The outer surfaces of two tape-springs contact with each other along longitudinal direction. Starting from the straight configuration, the OTSF hinge behaves linearly up to a peak moment of folding Mf, then it snaps and possesses as a constant moment spring under bend loading until the hinge is folded completely. The OTSF hinge deploys after releasing all constraints and reaches the peak moment of deployment Md, then Md linearly down to a minimum moment. Numerical simulation Finite element (FE) model. ABAQUS/Explict was employed to simulate the folding and deployment of OTSF hinges under pure bend loading. The five-level full factorial method is used to design of experiments for getting enough sample points. The material of OTSF hinges is titanium-nickel alloy Ni36CrTiAl with mass density ρ=8.0×103 kg/m3, Young’s modulus E=36.94 GPa, Poisson’s ratio ν=0.35, yield stress σy=0.98 GPa, Ultimate stress σu=1.194 GPa. As mentioned before, the FE models are set up in ABAQUS/Explicit with four nodes fully reduced integrated shell elements (S4R) with element size 2 mm and the detail finite element modeling method can be seen in reference [5]. The boundary constraints for OTSF is shown in Figure 2.

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Fig. 1 Dimensions of the OTSF hinge Fig. 2 Boundary constraints for OTSF hinge Design of experiment. For the purpose of deriving the function of Mf, Smax and Md, finite element analyses (FEA) should be performed for a series of design points determined by design of experiments (DOE) within the design domain. There are some available DOE methods, such as orthogonal arrays, central composite design, Latin hypercube, full factorial, etc [7]. The five-level full factorial design is used in the following study, the section radius is changed from 16 mm to 20 mm by every 1 mm, and the central angle is changed from 70 ° to 90 ° by every 1 °. Thus, there are total 5×5 sample design points which will be used in FEA. Table 1 shows the simulation results of 25 design points under pure bend loading. Table 1 FEA results for the selected 25 design points No. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

R /mm 16 16 16 16 16 17 17 17 17 17 18 18 18 18 18 19 19 19 19 19 20 20 20 20 20

φ /° 70 75 80 85 90 70 75 80 85 90 70 75 80 85 90 70 75 80 85 90 70 75 80 85 90

Smax /GPa 0.17636 0.185551 0.195899 0.206518 0.21195 0.181712 0.187985 0.190251 0.205318 0.210239 0.181634 0.18355 0.191845 0.206865 0.211981 0.18531 0.187418 0.191672 0.209207 0.213132 0.18734 0.189152 0.196821 0.209589 0.216343

Md /(N·m) 0.050045 0.076976 0.089659 0.118785 0.165928 0.062591 0.089497 0.097274 0.135292 0.1761 0.07226 0.109663 0.126246 0.182532 0.195239 0.096849 0.116557 0.13491 0.187113 0.203189 0.105802 0.13449 0.150935 0.221316 0.252418

Mf /(N·m) 0.04972 0.063496 0.085799 0.121076 0.156498 0.053628 0.072476 0.100372 0.137433 0.18778 0.059701 0.083128 0.116277 0.163092 0.224477 0.068198 0.09541 0.135926 0.19248 0.257916 0.07607 0.109681 0.160299 0.223385 0.287062

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Parametric studies In this section, influences of the opposite tape-spring hinges section radius R and central angle φ on Mf, Smax and Md are investigated based on Table 1. The Mf, Smax and Md are obtained by employing ABAQUS/Explicit solver [7]. The baseline hinge geometry is used for the parametric study with L=100 mm, lc =10 mm and t=0.12 mm. The section radius R ranges from 16 mm to 20 mm, and the central angle φ ranges from 70 ° to 90 °. Thus, it is possible to analyze the effects of design parameters R and φ on the OTSF hinges quasi-static folding and deployment behaviors from the following figures. Effects of section radius and central angle. Fig.3 shows the influence of section radius R and central angle φ on the peak moment during OTSF hinges quasi-static folding. The influence of section radius on peak moment of folding process presents better linearity. However, central angle’s effect is nonlinear. It can be seen that the larger the section radius is or the bigger the central angle is, the greater the peak moment of folding. When the section radius R changes from 16 mm to 20 mm, Mf is increased by 109.8% – 231.6%. When the central angle φ changes from 70 ° to 90 °, Mf is increased by 214.8% – 278.2%. That means Mf is sensitive to both the section radius R and the central angle φ.

(a) Effect of section radius (b) Effect of central angle Fig. 3 Variation of Mf with central angle φ and section radius R Effects of section radius and central angle. The effects of the section radius R and central angle φ on the maximum Mises stress Smax in the complete folded configurations are shown in Fig. 4. It can be observed that the greater the central angle is, the higher the maximum Mises stress. When the section radius R changes from 16 mm to 20 mm, Mf is increased by 0.47% – 6.23%. When the central angle φ changes from 70 ° to 90 °, Mf is increased by 15.01% –20.18%. It should be pointed out that the maximum Mises stress is more sensitive to the central angle φ than the section radius R.

(a) Effect of section radius (b) Effect of central angle Fig. 4 Variation of Sf with central angle φ and section radius R

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Effects of section radius and central angle . The peak moment during OTSF hinges quasi-static deployment versus different section radius R and central angle φ is shown in Fig. 5. It is clear that the larger the section radius is or the bigger the central angle is, the greater the peak moment of deployment. When the section radius R changes from 16 mm to 20 mm, Md is increased by 52.13% –111.41%. When the central angle φ changes from 70 ° to 90 °, Md is increased by 109.80% –231.56%. It should be pointed out that the peak moment of deployment is more sensitive to the central angle φ than the section radius R.

(a) Effect of section radius (b) Effect of central angle Fig. 5 Variation of Md with central angle φ and section radius R Conclusion According to above parametric analysis, it shows that the section radius and the central angle have important effects on both Mf and Md, but Smax is more sensitive to the central angle than the section radius. However, Mf, Smax and Md are less sensitive to the changes in section radius comparing with the changes in central angle. From the figure listed, an increase in Md and Mf due to the section radius or the central angle change often leads to an increase in Smax and mass of the OTSF hinges. Acknowledgement This project is supported by the College Discipline Innovation Wisdom Plan in China (Grant No. B07018), the Self-Planned Task (No.SKLRS201401A02) of State Key Laboratory of Robotics and System (HIT). The corresponding author is Hui Yang. References [1] J. RIMROTT F P, G. FRITSCHE, Fundamentals of stem mechanics, IUTAM-IASS Symposium on Deployable Structures: Theory and Applications, Cambridge, UK, 1998, p. 321–333. [2] J. BLOCK, M. STRAUBEL, WIEDEMANN M, Ultralight deployable booms for solar sails and other large gossamer structures in space, Acta Astronautics, 2011, 68(7-8), p.984–992. [3] O. SOYKASAP, S. PELLEGRINO, P. HOWARD, et al, Folding large antenna tape spring, Journal of Spacecraft and Rockets, 2008, 45(3), p.560–567. [4] G.W. MARKS, M.T. REILLY, R.L. HUFF, The lightweight deployable antennas for the MARSIS experiment on the Mars express spacecraft, 36th aerospace mechanisms symposium. Glenn Research Center, NASA CP-2002-211 506, 2002, p.183-196 [5] H. Yang, R.Q. Liu, H.W. Guo, J.G. Tao, Folding and deployment of a new thin-walled tube flexible hinge, Applied Mechanics and Materials, 2014, 635-637, p.365-369. [6] LINDMAN H R, Analysis of variance in experimental design, Springer., New York: 1992. [7] H. Yang, Z.Q. Deng, R.Q. Liu, Y. Wang, H.W. Guo, Optimizing the quasi-static folding and deploying of thin-walled tube flexure hinges with double slots. Chin J Mech Eng-En, 2014, 27(2), pp. 279-286.

Applied Mechanics and Materials Vol. 721 (2015) pp 122-126 © (2015) Trans Tech Publications, Switzerland doi:10.4028/www.scientific.net/AMM.721.122

Submitted: 27.10.2014 Accepted: 01.11.2014

Research on the Methods for Common-Rail Pipe Holes Abrasive Flow Machining Lifeng Zhu1, a, Kai Wang2, 3, b, Huan Wu 3, c, Dong Xiu3, d, Lizhong Sun3, e 1

Changchun University of Science and Technology, Changchun 130022, China;

2

Jilin University State Key Laboratory of Automotive Simulation and Control, Changchun, China; 3

Changchun Institute of Equipment and Process, Changchun 130012, China.

a

[email protected], [email protected], [email protected], [email protected], e [email protected]

Keywords: Common rail; holes; bilateral Abrasive Flow Machining.

Abstract. Abrasive Flow Machining method is an effective means for the common rail inside the tiny hole and cross-hole debarring polishing and machining. In this article, using FLUENT software analysis Abrasive Flow Machining common rail runner and three-dimensional numerical aperture structure, in the inlet velocity for the next 60m / s condition, contrast single, bilateral Abrasive Flow Machining method, derived both steady-state pressure, image analysis and comparison of the results of the dynamic pressure, velocity and turbulence kinetic energy, properly using numerical calculation to guide production process procedures, provide a reference for the optimization of the abrasive flow processing.. Introduction In recent years, car ownership in China is growing, vehicle fuel consumption continue to rise, making the problem of automobile fuel economy by social attention. In order to save fuel efficiency, high performance diesel engines commonly used high-pressure common rail fuel system. Common rail pipe is one of the most important parts in fuel supply system; an engine common rail pipe structure is shown in figure 1.

Figure 1 Common rail parts three-dimensional map In view of the common rail pipe parts design requirements, must ensure that the internal pipe no glitches, no cross-hole chamfering, line the inner surface of the cavity is smooth [1], so as to reduce the pressure loss of the high-pressure oil, increasing the flow rate of oil, obtain better atomization effect, reduce the fuel consumption. Abrasive Flow machining technology [2] break through the traditional processing methods, by extruding a flowable abrasive material, the abrasive particles are constantly on the grinding surface of the workpiece, deburring and polishing is completed. In this article, using numerical analysis software FLUENT abrasive Flow Machining process of hole rail tube numerical analysis, comparison and better guidance for future technical rules, to obtain the optimal processing methods.

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Model establishment and parameter Settings In the process of numerical analysis, assuming chemical dissolution or crystallization does not occur between the process and the medium with the particle phase; the particulate solid abrasive medium having the same pressure; granular solid abrasive media were satisfied with the conservation of momentum and energy conservation equations; the interaction between the abrasive and the particulate solid medium is realized through the drag coefficient [4, 5]. According to the engine type design requirements, according to the engine type design requirements, determine the common rail outlet nozzle diameter of Φ0.16mm, for common rail holes to simulate processing conditions. Pipe parts model creation and the division of grid work. According to the characteristics of Abrasive Flow Machining technology, select uncoupled implicit double precision solver in FLUENT, using standard k − ε solid liquid two phase Mixture turbulence model for numerical analysis; the abrasive stream media carrier based main phase, the second phase is set to silicon carbide particles, the volume fraction of 0.1; select the pressure inlet boundary conditions and pressure outlet boundary conditions as boundary conditions, the rest is defined as a solid wall boundary conditions; consider the influence of gravity. 2 The numerical analysis of single, bilateral processing methods The diagram of common rail unilateral Abrasive Flow Machining method, as shown in figure 2 (a): e d g e6

ed g e1

e dg e1 2 ed g e 18 e d g e24 e dg e 30 ed g e 36

ed g e2

(a) (b) Figure 2 The diagram of common rail unilateral Abrasive Flow Machining method The edge. 1 is set to import pressure, the edge. 6, edge. 12, edge. 18, edge. 24, edge. 30, edge. 36 is set to export pressure, the rest of the outer contour is set into a wall boundary conditions. Common rail pipe on both sides of abrasive flow machining method, as shown in the diagram as shown in figure 2 (b), edge.1, edge.2 is set to the pressure of imports, the edge.6, edge.12, edge.18, edge.24, edge.30, edge.36 is set to pressure outlet, the remaining wall of the outer contour of the boundary conditions set. After the success of the parameter setting, using two-phase kinetic equation SIMPLEC algorithm, use solver to solve the liquidity area calculation, after initializing the iterative calculations, analysis common rail holes Abrasive Flow Machining process of hydrodynamic values. After the calculation, get the residual monitoring curve of as shown in figure 3.

(a) (b) Figure 3 Residual monitoring curves As shown in figure 3 (a), unilateral processing methods began to converge in 80 iterations, as shown in figure 3 (b) bilateral processing methods began to converge in 300 iterations, this shows that the model structure and solution design parameters set reasonable, both methods are in line with the requirements can be satisfied with the converged solution.

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Using the three-dimensional numerical simulation methods for Abrasive Flow Machining process common rail numerical analysis, we can more accurately simulate real abrasive flow machining holes Common Rail case, get Abrasive Flow Machining common rail runner and processing characteristics of the hole structure. Numerical analysis and comparison of the steady-state pressure. Using three-dimensional numerical simulation analysis way, in the inlet velocity for the next 60m / s condition, single, common rail bilateral Abrasive Flow Machining of all holes structure, get the steady pressure as shown in figure 4 image contrast.

Figure 4 The steady pressure image contrast You can clearly see from the common rail pressure three-dimensional steady-state simulation results, in common rail pipe flow passage is bigger pressure difference, close to the common rail pipe cross hole rounded corners in the pressure of rapid decline, steady pressure in the common rail pressure at the obvious holes, this shows that the movement of abrasive flow where the most intense. Image contrast display, tiny hole and cross-hole round at the rail inside the tube, simulation numerical processing method of steady pressure on both sides are in the single processing method of steady pressure simulation numerical more than three times. Numerical analysis and comparison of the dynamic pressure. As shown in Figure 5, in the import speed of 60m / s conditions, using three-dimensional numerical analysis methods simulate a single, bilateral situation Abrasive Flow Machining common rail all holes structure, access to dynamic pressure image contrast.

Figure 5 Dynamic pressure image contrasts Two kinds of processing methods, in the cross-channel rail tube dynamic pressure tube at a substantially uniform, in the cross-hole round at the dynamic pressure suddenly increased, it is advantageous to the rounded corners polishing, dynamic pressure at the nozzle still maintained a larger. From the numerical simulation analysis, bilateral dynamic pressure processing method is a dynamic pressure of about four times the one-sided processing method, and unilateral abrasive flow machining methods into the common rail part of the dynamic pressure is slightly uneven.

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Speed numerical analysis and comparison. In order to better analyze the processing characteristics of the abrasive flow, the abrasive flow processing and the flow characteristics of the abrasive numerical analysis, in the inlet velocity for the next 60m / s condition, get inside the common rail speed image contrast as shown in Figure 6.

Figure 6 Speed image contrast As can be seen from the velocity images, when the common rail holes and the hole wall junction channel instantaneous velocity becomes large, the hole in the common rail abrasive flow at maximum speed. From the results of numerical simulation, as the speed of the boundary layer and the flow path surface of the orifice at the difference increases, the chance of contact of the abrasive grains and the flow cell walls will increase, and the relative slip abrasive flow machining surface boundary between volume also increased, removal channel surface is also bigger, more conducive to abrasive flow for finishing runner holes. Obviously, bilateral processing speed of the image to be more uniform, moreover, the speed of analog value to the velocity analog value of a weak double sided processing method.

Figure 7 The image of turbulent kinetic energy Numerical analysis and comparison of turbulent kinetic energy. Turbulent kinetic energy is half of the turbulent velocity fluctuation variance and fluid quality product, it reflects the turbulent mixing capabilities, from the turbulent kinetic energy image shown in Figure 7 can also Description: abrasive flow in the common rail pipe branch holes of the most intense movement, the energy accumulated in the inner wall of the hole is much larger than the hole in the cavity wall of the flow channel turbulent kinetic energy. As such, abrasive Flow hole round at the intersection of the most active, finishing the inner wall of the nozzle hole is stronger, the surface quality can be obtained over a spray hole. As can be seen from the turbulent kinetic energy of image contrast, turbulent kinetic energy of the bilateral process three times stronger than the numerical simulation of turbulent kinetic energy unilateral processing analog value, however, bilateral processing methods in the middle two spray holes intersecting holes fillet value than other, slightly uneven.

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Conclusions As can be seen from the Abrasive Flow Machining common rail pipe orifice flow channel simulation results, using abrasive flow machining technology to fine buffing holes parts is an effective processing method. By comparing the analog value of a single, double-sided processing method steady pressure, the dynamic pressure, velocity and turbulent kinetic energy, obtained bilateral processing method is superior processing effect can be obtained with unilateral processing method. References [1] Junye Li, Weina Liu, Lifeng Yang,et al. The Development of Nozzle Micro-hole Abrasive Flow Machining Equipment[J].Applied Mechanics and Materials, 2011, (44-47): 251-255. [2] Junhua Liu, Weina Liu, Lifeng Yang and so on. Design and numerical simulation of micro-hole abrasive flow machining equipment, common rail pipe [J]. Mechanical design and manufacturing, 2010 (10): 54-56 [3] Junhua Liu. Research and technology research tiny hole abrasive flow polishing apparatus [D]. Changchun: Changchun University of Technology, 2011. [4] Shuguang Fu, Yundan Lu, Xiang Cheng. Based on descaling nozzle Fluent internal flow field numerical simulation [J]. Manufacturing Automation, 2010, 32 (2): 89-92. [5] Guozeng Feng, Zheshu Ma. Diesel engine cylinder liner three-dimensional temperature field numerical calculation and analysis [J]. Mechanical design and manufacturing, 2008 (9): 1-3

Applied Mechanics and Materials Vol. 721 (2015) pp 127-130 © (2015) Trans Tech Publications, Switzerland doi:10.4028/www.scientific.net/AMM.721.127

Submitted: 28.10.2014 Revised: 29.10.2014 Accepted: 01.11.2014

Intermediate slabs and optimization in forging process of balance elbow by FEM BoJun Xiong1,a, Kelu Wang1,b, Jun Fang1,c , and Yun Huang 2,d 1

School of Aeronautical Manufacturing Engineering, Nanchang Hangkong University, Nanchang 330063, China; 2

Jiangxi Hongdu Steel Works Co., Ltd., Nanchang 330013, China

a

b

c

[email protected], [email protected], [email protected], d

[email protected]

Keywords: balance elbow; forging; Finite Element simulation; intermediate slabs

Abstract: Based on Deform-3D software, a 3D rigid-plastic FE model of forging forming process was established, then simulation analysis effective strain distribution, temperature distribution and load-stroke curve of three kinds of intermediate slabs(S1,S2,S3) in forging process. The results show that the optimized intermediate slab (S3) of effective strain distribution and temperature distribution is most homogeneous. And the maximum load force is minimum, the Shapes and dimensions of forging reach the preset value. Introduction In all kinds of tracked vehicle, balance elbow is a key component of connected mobile device and suspension, and must have a certain strength requirement. While forging is one of the main method of titanium alloy structural parts forming, the performances of forging are very different under different conditions in forging process, so the study on the intermediate slabs of the balance elbow forging process has important significance[1]. With the development of the computer and finite element method (FEM), FEM can be used to simulate metal forming process and gain better understanding of material flow within dies and using the finite element method (FEM) to deal with the forging process [2-4], FE simulation not only can provide an efficient and reliable approach to clearly investigate the whole forming process, but also can save design time and cost [5,6]. In this paper, through the finite element numerical simulation method can get material strain field, stress field and temperature field and other related information, then predict the forming defects and improve the quality of forgings in forging process for different intermediate slabs. Finite Element Modeling Geometry model. Three kinds of intermediate slabs (S1, S2, S3) shown in Fig.1. The solid model of forging and die were established in UG software。 Then forging and die were imported into the finite element software, the geometry model of the titanium alloy balance elbow forging were established and shown in Fig.2, Fig.3. Simulate Parameters Determination. The balance elbow forging process was studied by Deform-3D software. The forging material of TC27 titanium alloy is used. During the balance elbow forging forming process, primary forging process parameters were the same as the experiment, i.e. die preheating temperature, friction factor, bend forging temperature, initial forging temperature, speed of hydraulic press and forging hammer speed. The billet shape size is shown in Fig.1, and other simulate parameters are shown in Table 1.

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Fig.1 Three kinds of intermediate slabs

Top die

Intermediate-slab

Bottom die

Fig.2 Curved groove Top die Bending billet Bottom die

Fig.3 Finish forging type slot Table 1 Simulate parameters of balance elbow forging process Forming parameters Values 220 Die preheating temperature (℃) Friction factor 0.3 820 Bend forging temperature (℃) 820 initial forging temperature (℃) Speed of hydraulic press (m/s) 0.05 Forging hammer speed (m/s) 3

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Simulate Results and Discussion Effect of intermediate slabs on the Effective Strain. Fig.4 shows the effective strain distributions of the forging of three kinds of intermediate slabs at the end of forging forming process. It can be found from Fig.4 that the distributions of effective strain are basically identical. (1)When the intermediate slab is S1, the maximum effective strain is 3.02, the minimum effective strain is 0.08, the difference of that is 2.94. (2) When the intermediate slab is S2, the maximum effective strain is 2.82, the minimum effective strain is 0.08, the difference of that is 2.74. (3) When the intermediate slab is S3, the maximum effective strain is 2.45, the minimum effective strain is 0.02, the difference of that is 2.43. It is indicated that the effective strain of forging of 1nd intermediate slabs is greater than others, and the inhomogeneity of effective strain is the greatest. Comparing with 2st intermediate slabs, 3rd intermediate slabs can reduce the maximum and inhomogeneity of effective strain effectually. (a)

(b)

(c)

Fig.4 Effects of three kinds of intermediate slabs on effective strain distributions ((a)S1; (b)S2; (c)S3) Fig.4 Effects of three kinds of intermediate slabs on effective strain distributions Effect of intermediate slabs on the Temperature. Figs.5 shows the temperature distributions of the forging of three kinds of intermediate slabs at the end of forging. It can be found that the temperature of the corner region is larger than other regions. (1)When the intermediate slab is S1, the maximum temperature is 994˚C, the minimum temperature is 820˚C, the difference of that is 174˚C. (2) When the intermediate slab is S2, the maximum temperature is 1020˚C, the minimum temperature is 814˚C, the difference of that is 206˚C. (3) When the intermediate slab is S3, the maximum temperature is 970˚C, the minimum temperature is 810˚C, the difference of that is 160˚C. It is indicated that the temperature decreases quicker for 3rd intermediate slab, and the homogeneity of temperature is the greatest. The forging of the internal temperature gradient is small, and more beneficial to the forging forming. (a)

(b)

(c)

((a)S1; (b)S2; (c)S3) Fig.5 Effects of three kinds of intermediate slabs on temperature distribution Effect of intermediate slabs on the load force. Figs.6 shows the load - stroke curve of three kinds of intermediate slabs in forming process of three kinds of intermediate slabs . It can be found from Fig.6 that under the three kinds of intermediate slabs load - stroke curve difference is larger, the 3nd intermediate slab of steps is greater than other two intermediate slabs, This is the increase of stroke due to intermediate slab thicker than other two intermediate slabs(1)When the intermediate slab is S1, the maximum load force is 1.20×108N. (2) When the intermediate slab is S2, the maximum load force is 1.12×108N. (3) When the intermediate slab is S3, the maximum load force is 1.08×108N.The Figs.6 also shows that the maximum load force of forging under 3nd intermediate slabs is smaller than

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others .From the view of the maximum load, the optimal intermediate slab of the balance elbow forging is S3.

Fig.6 The load - stroke curve of three kinds of intermediate slabs in forming process Conclusion (1) When the intermediate slab is S1, the effective strain is the largest; the distribution of temperature is relative homogeneous and the maximum load force is the largest. (2) When the intermediate slab is S2, the distribution of temperature is inhomogeneity and the maximum load force is smaller than 1nd intermediate slab, the effective strain is larger than 3nd intermediate slab. the shapes and dimensions of forging is better. (3)When the intermediate slab is S3, the distribution of effective strain and temperature is most homogeneous. The maximum load force is minimum, and the Shapes and dimensions of forging reach the preset value, this is because intermediate slab is suitable to forming on the forging process. (4) From the above, the optimal intermediate slab of the balance elbow forging is S3.and the optimized intermediate slab can increase the uniformity of strain and temperature of forging and improve the quality of forgings filling, size of forge flash is more uniform. References [1] N. Akgerman, T. Altan, Application of CAD/CAM in forging turbine and compressor blades, Trans. ASME J. Eng. Power 98(1976) 290. [2] XIE Jian-xin, PEI Qiang, LIU Jing-an. UBET analysis of process of extruding aluminium alloy ribbed thin-wall pipes through a porthole die [J]. Trans Nonferrous Met Soc China, 2002, 12(2): 183-188. [3] KIM H, YAGI T, YAMANAKA M. FE simulation as a must tool in cold/warm forging process and tool design [J]. Journal of Material Processing Technology, 2000, 98: 143-149. [4] HA0 Nan-hai, TIAN Zhu-ping, WE1 Xing-hua. Die land optimization of section extrusion by finite element method [J]. Trans Nonferrous Met SOC China, 2001, 11(6): 884-886. [5] R. Shivpuri and E. Eruc: Int. J. Mach. Tools Manufact, Vol. 32 (1995), No.3, p.379. [6] L. Hua, X.G. Huang and C.D. Zhu: Theory and Technology of Ring Rolling [M] (Mechanical Industry Publications, Beijing 2001), p.1-7.

Applied Mechanics and Materials Vol. 721 (2015) pp 131-134 © (2015) Trans Tech Publications, Switzerland doi:10.4028/www.scientific.net/AMM.721.131

Submitted: 30.10.2014 Accepted: 06.11.2014

Analysis of Hydroelectric Unit’s Upper Bracket Based on Test and FEM Mimi Xia1,a, Yonggang Li 2,b 1

School of Energy and Environment, Xihua University, Chengdu 610039, China

2

Chongqing Agricultural Machinery Testing Center, Chongqing 402160, China a

b

[email protected], [email protected]

Keywords: upper bracket, structure analysis, finite-element, strain test.

Abstract. To research the load upper bracket of Francis hydroelectric unit, then established the finite-element model, and analyzed the structure stress of 7 operating condition points with the ANSYS software. By the strain rosette test, acquired the data of stress-strain in the area of stress concentration of the upper bracket. The inaccuracy was considered below 5% by analyzing the contradistinction between the finite-element analysis and the test, and match the engineering precision and the test was reliable. The finite-element method could be used to judge the stress of the upper bracket, and it could provide reference for the Structural optimization and improvement too. Introduction Higher requirements were put forwarded for unit’s reliability and stability as the development of hydropower in recent years [1]. Upper bracket was an important part in Francis turbine unit. It supported the gravity of runner, the main shaft, generator shaft, guide bearing, upper and lower bearing, generator rotor, and the thrust bearing, including the hydraulic thrust, so its stiffness and strength directly affected the unit’s safety and the performance of the thrust bearing [2]. This article was based on a Francis unit in a hydropower station from west of China, and all simulation results were compared with the test. The geometry model and mesh of upper bracket Upper bracket is loaded part which crutch the thrust bearing. Its construction is showed in fig. 1.The whole construction type is radial, the four “I” type braces is around the central part. Every brace is contacted to central part by welding. The whole load reached to concrete by the block of upper bracket [3]. Some oil and water pipes are abounded to simplify the model [4].

Fig.1 The Upper bracket model Calculation operating conditions and boundary conditions Except the gravity of itself and rotating components on axial direction, the hydraulic thrust also is the part of load when the unit running. The generator rotor is 75t. The rotating thrust is 12.2t. The thrust bearing is 3.7t. The runner is 6.4t. The upper bracket itself is 9.3t.

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Calculation results and analysis As it can be seen from table 1, maximum equivalent stress is 93.3Mpa, absolutely, under the compression strength value too far. Maximum load appear on the axis direction on upper bracket because of the maximum hydraulic thrust, and it’s on 30MW operating condition. The place of maximum equivalent stress is in the corner of central apart board. This is due to that region is high curvature area, besides it’s close to intent load. The place is upper board on the central apart of upper bracket, because here is the direct contacting area of thrust block of thrust bearing, and the axis load is supposed to transmit to upper bracket. Table 1 the equivalent stress and maximum deformation of 7 operating conditions Operating conditions Maximum Equivalent stress Maximum deformation distribution (MPa) (mm) rotor jacking 4.5 0.049 static load 79.3 0.089 10MW 89.5 1.083 20MW 91.7 1.147 30MW 93.3 1.183 40MW 92.2 1.158 42MW 92.5 1.161 Test process The DH5922 dynamic tester is used for this test, and resistive strain gauge is BX120 type. Sensitive coefficient is 2.10. There are 4 test points on the central apart board, and 5th point is located on the upper board of central apart.

Fig.2 Location of pasting strain gauge After FEM calculation, 1st point should be close to this area as near as possible because of stress centralization. 2-4 points are placed on the board of central part, all showed in figure 2. Check resistance value, basal, covering and lead wire, to guarantee that strain gauge work [5]. 1-5 board places have been polished and cleaned before test, and oil pollution and oxide layer on steel plate also been cleaned, finally, accurate position of strain gauge pasting is located by scribing on the board.

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Test results Both test points and operating conditions are 5 and 7, defined as No.1-5 and No.1-7. 40MW operating condition is rated condition, and 42MW is over rated 5% condition, all results showed in below table 2. Table 2 The FEM results of the minimum principal stress Test Operating condition points 1 2 3 4 5 6 7 1 -0.379 -39.561 -56.156 -61.515 -65.081 -63.528 -64.595 2 -0.637 -23.956 -35.553 -38.642 -41.586 -40.481 -40.574 3 -0.467 -17.842 -25.764 -26.545 -30.351 -29.917 -30.156 4 -0.393 -10.154 -15.737 -16.315 -17.124 -17.586 -16.912 5 -0.478 1.472 2.435 2.471 3.384 3.278 3.198 Comparison and analysis The minimum principal stress maximum value concentrated on the corner of central part. Most area of four braces supported axial compressive stress. Outside the steel plate and below board, it showed lower construction stress because these areas are away from load centralization. Comparison is show in figure 3.

Fig.3 The minimum principal stress contradistinction of FEM analysis and test Some information can be found from the figure 3. Some slightly errors existed between simulation and test data. This is due to that material mechanical properties become worse through years. Hydraulic thrust changing over unit running, the flow rate, guide vane opening, running head, and ring stop leakage ring seal clearance all can infect its value. The unbalanced magnetic pull strength from generator rotor, eccentric moment when unit running, vibration due to draft tube pressure pulsation may inflect the upper bracket stress [6]. Moreover, due to the system error and the measuring environment, the error can’t be avoided. The maximum relative errors are 4.3%, 3.7%, 2.9%, 4.5% and 4.7%, all under 5%. Conclusions Simulation is correct by comparison between simulation and test, and errors are in the reasonable range. It can provide reference for upper bracket construction optimization later. After FEM analysis, the equivalent stress and maximum deformation are both in the unit safe range. The upper bracket central part, block and brace below board all are stress centralization area, some measures such as add welding steel plate may be taken to make sure unit safe.

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References [1] Xifang Chen, Hydro-generator monitoring structure operation and maintenance, China Water Power Press, Beijing, 2008. [2] Yannian Bai, Hydro generator design and calculation, China Machine Press, BeiJing, 1982. [3] Chong Zhang, Changhong Lin, Finite Element Analysis of Main Components of Generators for Nabang Hydropower plant, J. Water Resources and Power. 9(2013) p.174-178. [4] Yuhui Yu, Xiaojun Zhang, Analysis of hydro generator load upper bracket, J. Private Science and technology. 11(2011) p.52-55. [5] Jun Zheng, Hongwang Zhao, Xingmao Duo, Summarization of Stress and Strain Test Method, J. Automobile Science & Technology. 1(2009) p.5-9. [6] Bin Tan, Wen Fang, Analysis of medium hydro generator upper bracket, J. Dongfang Electric Machinery. 4(2003) p.313-318.

Applied Mechanics and Materials Vol. 721 (2015) pp 135-139 © (2015) Trans Tech Publications, Switzerland doi:10.4028/www.scientific.net/AMM.721.135

Submitted: 30.10.2014 Accepted: 01.11.2014

The Optimization Method of Tailor Welded Blank Forming Process Based on Numerical Simulation Yong Gan a, Jiaxing Li b, Yang Chen, Hongzhao Li School of Mechanical and Electrical Engineering, Guilin University of Electronic Technology, Guilin 541004, China a

[email protected], [email protected]

Keywords: Tailor welded blanks; Numerical simulation; orthogonal experiment; Optimization scheme

Abstract. The tailor welded blank stamping is a complicated process with numerous forming parameters, so it is difficult to determine the relationship between the forming parameters and the formability. The TWBs forming is numerically simulated by an orthogonal experiment design while the thickness ratio, blank holder force and frictional coefficient are forming parameters, and the maximum thickness thinning and the maximum weld movement are index. The influencing degree of forming parameters on the index and the optimal combination of forming parameters are analyzed. The optimization design is used to carry out numerical simulation and verification which can effectively improve stamping formability of TWBs. The simulation results also show that optimization schemes can offer the reference for the forming parameters optimization of the TWBs. Introduction In order to meet the special requirements of different parts, TWBs are defined as two or more sheets of blanks welded together by laser before stamping [1]. Due to the needs of the economy, safety and environmental protection, TWBs have been widely used in auto panel parts. The development of the TWBs cover parts is a quite complex process from the beginning of the automobile modeling, parts design, process design, mold structure design, mold manufacturing, mold test, to ensure successfully products[2]. At the same time, the sheets are affected by the thickness ratio, different properties, the weld-line movement among forming process parameters and often appear wrinkle, crack and rebound during the stamping process of the TWBs. At the beginning of the mold process design, the good design can shorten the making cycle, improve production efficiency, and reduce product cost. Some scholars are trying to combine optimize theory with finite element technology in the field of TWBs stamping with the development of the finite element technique [3-6]. The forming parameters including the thickness ratio, blank holder force and frictional coefficient influencing on the forming of the TWB cover parts are studied by using the combination orthogonal experiment design with finite element method in the article. The Establishment of the finite Element Model The analysis of the forming technology of the metal parts. In this paper, the research of TWBs has been widely used in auto-body panels, and the 3d model is shown in Fig. 1. TWBs have a complex combination of surface structure, contain a variety of different sidewall structure and height, and then drawing pieces and parts itself are basically the same model. Therefore, the analysis of the part used in TWB forming properties has good representation. Since the part are composed of two or more materials with different thickness and less thickness welded together, inhomogeneous deformation of the tailor-welded blanks will reduce forming property, such as wrinkle, crack and rebound. The numerical simulation model. Unlike the single blank, the rational weld modeling is the preliminary problem of the numerical simulation in the TWB forming process. At present, there are two strategies to model Tailored Blanks and Heat Affected Zone in a finite element program. The first

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strategy is to model the weld accurately. In this situation the weld type is taken into account, i.e. the dimensions and the shape of the weld, and also the volume fractions of martensite in the weld and the Heat Affected Zone are taken into account. This approach requires a fine element mesh in the weld area. The second strategy is to neglect the weld type, only the place of the weld is taken into account. In this last approach the weld and Heat Affected Zone are modeled with one row of (coarse) elements, or are simply neglected which holds that in the last option the originated martensite is not taken into account. Some research has shown that the second model strategy can be useful for TWB drawing simulations without high local deformations and save time in TWB drawing. Since the appearing stress states in the products treated in this paper are expected to be relatively simple, the weld itself will not be modeled in the Tailored Blank simulations.

Fig. 1 Three-dimensional model of the TWBs

Fig.2 Finite element models

The finite element model of TWB is shown in Fig. 2. Low carbon steel DQSK36 is commonly used in auto-body panels and its material property is shown in Tab. 1. The B-T shell element is used for meshing the finite element model. The rigid finite element is treated in tool modeling, such as punch, die and binder. The material model is described by elastic-plastic model of meeting the Hill yield criterion. Considering the artificial dynamic effects, stamping velocity is usually selected from 2 m/s to 5 m/s. In this paper, stamping velocity is selected 4 m/s. Table 1 Material properties parameter material Young Modulus E/GPa Poissons Ratio µ Strength Coeff K/MPa Hardening Exponent n Lankford Param R00 Lankford Param R45 Lankford Param R90

corresponding value DQSK36 207 0.28 520.4 0.232 1.73 1.35 2.18

The Optimization of Numerical Simulation The orthogonal experimental design is a scientific method of arranging multi-factors and multi-standards based on combining with the mathematical statistics and orthogonal principle. The features of this optimization method can select some representative points from a mass of data. It will spend a lot of time to individually simulate the combination of the multi-factors during the forming process of TWB. The number of experiments can be reduced and the typical experimental scheme can be determined by using orthogonal experimental design. Meanwhile, the orthogonal experiment results can analysis the influence degree of various factors on the index, the average level and the optimal combination of various factors. The factors and levels orthogonal table. The orthogonal experiment is designed under the condition of 4 levels from each of factors, which are the thickness ratio, blank holder force and frictional coefficient. There are some factors including wrinkle, crack and weld are the key to reduce forming property of TWBs. Due to various factors have little effect on the maximum thickening rate, the maximum thickness thinning and the maximum weld movement are defined as index numbers. The factors and levels orthogonal table are shown in Tab. 2.

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The project and analysis in the orthogonal table. In this article, Table 2 shows that three factors and four levels L16 (45) orthogonal experiment is designed without considering the interaction among the various factors. L16 (45) orthogonal table can arrange five factors which the last two factors have not been arranged. Orthogonal table design and experimental results are shown in Tab. 3. Kij is the sum of the index values of various factors under the same levels. kij is the averages of the index values of various factors under the same level. Rj is defined as range. Table 2 Factors and levels Factors levels 1 2 3 4

A thickness ratio (mm) 0.8/1.0 0.8/1.2 0.8/1.4 0.8/1.6

B Thinner blank/ thicker blank BHF (KN) 100/200 140/200 200/100 200/140

C frictional coefficient 0.10 0.11 0.12 0.13

Table 3 Project and analysis in the orthogonal table number 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

A 1 1 1 1 2 2 2 2 3 3 3 3 4 4 4 4

factor B 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4

index C 1 2 3 4 2 1 4 3 3 4 1 2 4 3 2 1

the maximum Thickness thinning (%) 23.293 24.340 26.885 28.694 28.467 29.704 32.812 30.341 30.265 33.148 29.335 31.652 34.769 34.573 33.358 35.214

the maximum weld movement (mm) 4.657 4.218 3.472 3.557 5.285 5.606 4.235 4.381 5.724 5.548 5.329. 5.353 6.731 6.626 6.318 6.542

Table 4 The results of the index value calculation results

The maximum thickness thinning (%)

the maximum weld movement (mm)

K1j K2j K3j K4j k1j k2j k3j k4j Rj K1j K2j K3j K4j k1j k2j k3j k4j Rj

A 103.21 121.32 124.40 137.91 25.80 30.33 31.10 34.48 8.68 15.90 19.51 21.95 26.22 3.98 4.88 5.49 6.55 2.57

B 116.79 121.77 122.39 125.90 29.20 30.44 30.60 31.48 2.28 22.40 21.99 19.35 19.83 5.60 5.50 4.84 4.96 0.76

C 117.55 117.82 122.06 129.42 29.39 29.45 30.52 32.36 2.97 22.13 21.17 20.20 20.07 5.53 5.29 5.05 5.02 0.51

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the m axim um w eld m ovem ent（ m m )

the maximum thickness thinning（%）

The analysis of orthogonal optimization results. The ranges in Tab. 4 show that the influence degree of the three factors on the two indexes is analyzed, i.e. for the maximum thickness thinning, R1>R3>R2, that is A>C>B, for the maximum weld movement, R1>R2>R3, that is A>B>C. Obviously, the influence degree of the three factors on the two indexes is inconsistent. For the index of the maximum thickness thinning, the smaller the maximum thickness thinning is, the less likely the TWBs crack. For the index of the maximum weld movement, the smaller weld movement is, the higher the forming quality of TWBs is. Therefore, the smaller range R value is, the better forming property of TWBs is. The index and factors in the orthogonal table are shown in Fig. 3. Fig. 3 can determine the optimal combination of various factors on the index, i.e. for the maximum thickness thinning, the optimal combination is A1B1C1, for the maximum weld movement, the optimal combination is A1B3C4. Obviously, the above two optimal combinations are inconsistent. The better optimal combination is found out by analyzing the influence degree of various factors on the two indexes. For factor A, A1 of its value is better and its influence of the two indexes is consistent. For factor B, B3 of its value can control the maximum thickness thinning in a certain range and minimize the weld movement. For factor C, its influence of the two indexes is opposite and the main concern is the influence of the maximum thickness thinning. Therefore, C2 of its value can reduce the maximum thickness thinning and have no effect on the weld movement. The better optimal combination A1B3C2 is found out by synthesizing the above visual and balance analysis results. 35 33 31 29 27 25 A1 A2 A3 A4

B1 B2 B3 B4

C1 C2 C3 C4

factors and levels

8 7 6 5 4 3 A1

A2

A3

A4

B1

B2

B3

B4

C1

C2

C3

C4

factors and levels

Fig. 3 Factors and index in the orthogonal table

(a) The FLD before optimization

(b) The FLD before optimization

(c) The FLD after optimization Fig. 4 Comparison of optimization forming limit diagram The simulation verification. The better optimal combination is numerically simulated by the orthogonal experimental design. The FLD of TWBs is compared before and after optimization which can help us to analyze stamping forming property, as is shown in Fig. 4. Fig. 4(a) and Fig. 4(b) show

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that the TWBs will be easy to wrinkle in the steps and sidewalls of the thick side or crack in the rounded corners and Anti- drawing parts of the thin side before optimization of forming parameters. Fig. 4(c) shows that the better optimal combination can remove the crack, significantly reduce wrinkle area, and occur the small amount of wrinkling in the bottom and sidewalls of the TWBs. Conclusions The tailor welded blank forming was numerically simulated by an orthogonal experiment design. Orthogonal experiment table is design with three factors and four levels while the maximum thickness thinning and the maximum weld movement are index. The orthogonal experiment results can analyze the influence degree of various factors on the index. The better optimal combination A1B3C2 is found out by analyzing and balancing the influence degree of various factors on the index. The better optimal combination is numerically simulated by FEM software. By comparing the FLD of TWBs before and after optimization, the results show that the optimal design of forming can improve the stamping forming property, shorten the making cycle and provide a method of guidance for the forming parameters optimization. Acknowledgements This work was financially (2013GXNSFAA019298)

supported

by

the

Guangxi

Natural

Science

Foundation

References [1] Ghoo B Y, Keum Y T, Kim Y S. Evaluation of the mechanical properties of weld metal in tailored steel sheet by CO2 laser. Journal of Materials Processing Technology, 2001, 113 (1-3): p.692-698. [2] Ni Yun, Huang Yaling, Zhang Yuehong. Stamping Simulation Analysis of Auto-Body Panels Based on DYNAFORM [J]. Hot Working Technology, 2011, 40(11): p.108-111. [3] Chen Long, Huang Pu, Wang Jiong, et al. Optimization of tailor-welded front longitudinal forming based on orthogonal experiment and grey system theory [J]. Journal of Plasticity Engineering, 2012, 19(4): p.1-5. [4] Wang You-hua, Yuan Guo-ding, Jiang Yin-fang, et al. The forming parameter optimization of square box with tailor welded blank [J]. Machinery Design & Manufacture, 2012, (2): p.173-175. [5] MANABE K, YANG M, YOSHIHARE S. Artificial intelligence identification of process parameters and adaptive control system for deep-drawing process. Journal of Material Processing Technology, 1998, 80-81: p.421-426. [6] Zhao Mao-yu, Xue Ke-min, Li Ping. Auto panel forming process parameters optimization of multi-objective quality. Journal of Mechanical Engineering, 2009, 45(8): p.276-282.

Applied Mechanics and Materials Vol. 721 (2015) pp 140-143 © (2015) Trans Tech Publications, Switzerland doi:10.4028/www.scientific.net/AMM.721.140

Submitted: 31.10.2014 Accepted: 06.11.2014

Research on gear mesh misalignments under the system deformation Xiaohe Deng * Hubei Key Laboratory of Advanced Technology of Automotive Parts, Wuhan University of Technology, Wuhan 430070, China [email protected] Keywords: gear mesh misalignments, system deformation, substructure method

Abstract. The gear transmission is a complex elastic system. The system deformation under the load will make the position of mating gear deviating from the ideal position. The deformation varies with different gear shifts and load that will cause the different type of gear mesh misalignments. Therefore, the researches on the gear mesh misalignment should consider the effect of deformation of gear system under different working conditions. The paper established an elastic model of commercial vehicle transmission system. The gear mesh misalignments under different shifts and different load are obtained considering the integrated system deformation. The variation values of gear mesh misalignments are analyzed correspondingly. Introduction Gear transmission system is a complex elastic system containing different components, each component in the transmission system has certain layout structure, and one component connects to another component through variety of connections to transfer power. Gear system produces elastic deformation in the process of power transmission. For multistage gear transmission system, the gear system deformation is closely related to the gear shift and load condition. The deformation can lead to gear mesh deviation, and the gear mesh misalignment is an important measure of deviation of gear mesh. In the study of gear mesh misalignment, it is necessary to consider the influence of load condition and transmission path. Gear system includes the support system, transmission system and structural system. The support system mainly includes shaft and bearing. The transmission system mainly includes the gear system; the structure system mainly includes the gearbox. Each part has its different structural features, the characteristics of coupled together constitute the overall characteristics of gear system. Therefore, in order to know the comprehensive gear mesh characteristics, it’s necessary to set up the whole analysis model including the above systems. For such a complex system, it is particularly important to choose appropriate modeling method. Gear transmission system

Fig. 1 Schematic diagram of a transmission

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Physical model. For the study of a commercial vehicle mechanical transmission, sub model method is used to establish model of gear transmission system. The structure of the transmission is shown in Fig. 1. The input shaft is installed by the constant mesh gear. From left to right in turn, is the 3rd shift, the 2nd shift, the 1st shift, the reverse shift and the 5th shift. The position of input shaft and output shaft is coaxial. The power of forward shift is transferred from the constant mesh gear of input shaft to each shift gear pairs of the intermediate shaft and output shaft, except for the direct shift. In the direct shift the power transferred from the input shaft to the output shaft. The gears of intermediate shaft are fixed on the intermediate shaft and the gears of output shaft are set on the output shaft. The basic parameters of each shift gear are shown in table 1. Table. 1 Basic parameters of gear pairs Gear pairs constant 1st 2nd 3rd 5th rev Gear tooth 19 29 15 37 23 31 31 29 46 25 13 28 33 Hand(L/R) L R R L R L R L R L L R L modulus( mm ) 2.5 2.25 2.2 1.95 1.75 2.5 Pressure angle(°) 20 20 20 20 20 20 Center dis. ( mm ) 69.5 69.5 69.5 69.5 69.5 57 84 Helix angle (°) 28 32 31 31 28.5 24 24 Mathematical model. The basic idea of substructure method [1] is breaking up the whole model into parts first, and then accumulating parts to the whole. Firstly, artificially dividing the whole system into several parts (called substructure) purposeful, do detailed analysis of individual substructure, then establishing the connections between the various subsystems and separated parts according to their relationships. Finally integrating the subsystem, and rebuilding the equivalence with the original structure and efficient new system. Through this method and principle, the model of complex structures can be transformed to several relatively simple substructure models. Without considering the elastic deformation of connection position, the condition of displacement coordination interface of the gear and the shaft meet {δ p }G = {δ p }S (1) where, {δ p } is The displacement of the inner center node of the gear, G

{δ }

p S

is displacement of the

center node of the shaft where the gear installed. The condition of displacement coordination interface of the shaft and the rolling bearing {δ bi }s = {δ il }B (2) where, {δ bi }s is the displacement of the center node of the shaft the Rolling beared. {δ il }B is the displacement of the center node of the rolling bearing.The condition of displacement coordination condition of the rolling bearing and box {δ gi }B = {δ bi }S (3) where, {δ gi } is the displacement of the center node of the connection interface of rolling bearing and B

box, {δ bi }S is the displacement of the center node of the box where the bearing installed. By using the method of substructure, the fixed constraints are applied to the connecting position to fix the box and the vehicle. The coupling nodes are established in the connection position between the box and the bearing. The stiffness matrix of box can be obtained through the finite element models. The box is connected with the outer ring of the bearing. The stiffness of the connected position between the bearing and the box under loading will influence the initial position of the bearing outer ring, which will cause the dislocation of the bearing. Bearing is standard part, bearing stiffness data can be obtained according to its parameters. Shaft substructure can be simplified to equivalent beam, and the lumped parameter model can be set up, or the finite element model can be established. The elasticity relationship between the two nodes is determined by the rolling bearing stiffness characteristics.

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Based on the above process and method, the boundary conditions of meshing gear can be obtained, and the gear mesh misalignment can be calculated. Results. The variation of gear meshing misalignments under different path and load are analyzed. Misalignments under different transmission paths. The maximum torque of the transmission is 146.5Nm , we choose the 100% of load as the maximum torque. The gear mesh misalignments of the constant meshing gear and the relative meshing gear of the transmission under the same torque but different shift are shown as in table. 2 and table. 3. The relative meshing gear refers to the shift gear pairs participating in the power transmission, except for the constant mesh gear. The 4th shift is also called the direct shift, in which the input shaft and output shaft connect through the synchronizer directly. At this time, no meshing gear transmit torque, the torque is directly transferred from the input shaft to the output shaft. For the forward gear shift, the largest value of misalignments of driving gear is from constant meshing gear in the 5th shift, and largest value of total misalignments is from the constant meshing gear pairs in the 5th shift. The gear mesh misalignment of driving gear is also very large in the reserve shift. Since the using frequency the reverse shift is lesser than the forward shifts, and the real torque in the reverse shift will less than the maximum torque. The frequency used shifts and forward shifts are more important to concern. Therefore, the calculated working condition represents the wrest situation. Table.2 Misalignment of constant gear pairs under different shifts ( µ m ) shift Diving gear misalignment Driven gear misalignment Total misalignment

1st 22.381 0.881 21.500

2nd -3.080 3.946 -7.026

3rd -1.690 2.444 -4.134

4th 0 0.289 -0.289

5th 55.980 -2.949 58.929

rev 44.020 1.735 42.285

Table.3 Misalignment of the correlated gear pairs under different shifts ( µ m ) shift Diving gear misalignment Driven gear misalignment Total misalignment

1st 10.396 -8.813 -1.583

2nd 3.914 -3.657 7.571

3rd 1.678 -0.762 2.441

4th / / /

5th -0.271 -1.290 1.019

rev 6.907 2.414 2.412 1.16 4.495 1.254

Misalignments under different load The displacement of gear shaft. The transmission torque in the 1st shift is larger than in the other shifts. Since the maximum torque of the input shaft is 146.5Nm, the gear mesh situation in 100%, 50%, and20% of the maximum torque is analyzed. The deformation under different load in the 1st shift is shown in Fig. 2. The value of shaft deformation increases since the input torque increases. The distance between the center of the installed gear and the center of the front of the input shaft is 161.259mm. The angle displacement of the driving gear shaft occurs due to the torsional deformation, which makes the deflection of gear meshing. The corresponding deformation of gear center position as shown in table 4.The torsional deformation is linear change with load bending deformation and the increase of the proportion to the load amplitude. Under the condition of different load in the same shift, the shape of gear shaft deformation curve remains unchanged; the amplitude of the deformation curve is linear related to the value of the load. Table.4 Shaft displacement under different load 100% load 50% load 20% load µ m 93.023 69.811 45.391 Bend displacement ( ) mrad -6.244 -3.122 -1.249 Twist displacement ( ) Gear mesh misalignments. In the 1st shift, the constant mesh gear and the 1st shift gear are engaging. The gear mesh misalignments are shown in table 5. It shows that the maximum value of misalignment is from the driving wheel of constant gears in 100% load cases of gear driving wheel,

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the total amount maximum value of misalignment is from the constant gear mesh pair. The total value of misalignment for the 1st shift gear is relatively small. In the same gear case, the mesh misalignment is positive correlating to the load value. Twist displacement(mrad)

Bend displacement(µ m)

800 100 50 20

600 400 200 0 0

5

100 15 Shaft length(mm)

200

0 -2 -4 -6

100 50% 20%

-8 0

5

10 150 Shaft lenth(mm)

200

(a)Bend displacement (b)Twist displacement Fig. 2 The displacement of input shaft in the 1st shift under different load along shaft length direction Table. 5 Gear misalignments under different load ( µ m ) 100% load 50% load 20% load misalignment The costanct mesh gear pairs The 1st shift mesh gear pairs

Diving gear Driven gear Total Diving gear Driven gear Total misalignment

22.381 0.881 21.500 10.396 -8.813 -1.583

16.906 0.747 16.159 -5.210 -6.442 1.232

10.233 0.512 9.721 -2.000 -3.716 1.716

Conclusion This paper built the numerical simulation model of the transmission system based on the sub-structure method. The calculation of the gear mesh misalignment considering the system deformation can provide boundary condition for the analysis of gear mesh researches. The gear mesh misalignments under different shifts are closely related to the layouts of the gear transmission system. The misalignment is positive correlating to the load value under the same gear shift. References [1] Li Runfang, Wang Jianjun. Gear system dynamics: vibration, shock and noise (in Chinese). Science press, 1997 [2] Smith J. D. Gear noise and vibration [M]. New York: Marcel Dekker, 2003. [3] Li Shuting. Finite element analyses for contact strength and bending strength of a pair of spur gears with machining errors, assembly errors and tooth modifications [J]. Mechanism and Machine Theory, 2007, 42(1), pp, 88-114. [4] Song C, Zhu C, Lim TC, et al. Parametric analysis of gear mesh and dynamic response of loaded helical beveloid transmission with small shaft angle [J]. Journal of Mechanical Design, 2012, 134(8), pp, 135-142. [5] Hotait M. A, Talbot D, Kahraman A. An investigation of the influence of shaft misalignment on bending stresses of helical gears with lead crown [J]. Gear,2008,11-12, pp, 54-62 [6] Haigh, J., and J.N. Fawcett. “Effects of Misalignment on Load Distribution in Large Face width Helical Gears,” Journal of Multiday Dynamics, Proceedings of Institution of Mechanical Engineers, 2003, 217(2), pp. 93–98.

Applied Mechanics and Materials Vol. 721 (2015) pp 144-148 © (2015) Trans Tech Publications, Switzerland doi:10.4028/www.scientific.net/AMM.721.144

Submitted: 01.11.2014 Accepted: 07.11.2014

Applying Multi-objective Particle Swarm Optimization to Maintenance Scheduling for CNC Machine Tools Zhihui Yao a, Min Zhou b, * 1

School of Wuhan University of Science and Technology, Wuhan 430000, China a

[email protected], [email protected]

Keyword: CNC machine tools; maintenance scheduling; multi-objective particle swarm optimization

Abstract. This paper focuses on the maintenance scheduling for CNC machine tools. A bi-objective mathematical model is built with the repair time and maintenance cost. A multi-objective particle swarm optimization (MOPSO), which combines the global best position adaptive selection and local search, is proposed to solve the mathematical model. The results show that MOPSO has a better performance than other method for solving the maintenance scheduling. They also show that MOPSO is an effective algorithm that has strong convergence. Introduction The CNC machine tools have become the mainstream equipment of the contemporary machinery manufacturing. The complexity of its structure and the diversity of components, control of the intelligent, and processing technology of diversity, complexity of machining parts, which determine the difficulty and professional of the CNC machine tools maintenance [1]. According to statistics, the costs of CNC machine tools maintenance account for 20% -30% of the total life cycle costs[2].How to develop a combine maintenance scheduling of low maintenance cost, high efficiency of maintenance, and shorter time of maintenance is very important. A math model was constructed based on the semiconductor manufacturing equipment problem by Huimin Ma et al., and then the Binary Particle Swarm Optimization was used for solving the model [3]. A new method of pre-maintenance scheduling decisions for semiconductor was provided, also to expand the knowledge of evolutionary algorithm application [4].An optimizing model of scheduling of maintenance craft was formulated based on actual maintenance procedure and restriction of resource for vehicle equipment by Junwei Cheng et al., and the total man-hours service was effectively reduced [5]. According to above researcher's literature, the maintenance scheduling of CNC machine tools has not studied very well. PSO has been widely used in many scheduling problem. However, there are few literatures to apply PSO to solve the maintenance scheduling. Based on the actual requirements of the enterprise, the mathematical model of CNC machine maintenance scheduling is built, which considers the timeliness and economical of CNC machine repair time. The multi-objective particle swarm optimization algorithm is proposed to solve the optimization problem. Description of Problem Suppose an enterprise has M types of CNC machine tools and K maintenance groups. The CNC machine tools are numbered i = 1, 2, , I and the maintenance groups are numbered k = 1, 2, K .The number of CNC machine tool by maintenance group k is j = 1, 2, J k . The fault repair time of k maintenance group to n component failure maintenance of CNC machine tools requires for tink .The repair time of k maintenance group to i CNC machine tool requires for tik .When X = 1 showing that i CNC machine is scheduled to k maintenance group to jth repair, otherwise X = 0 ; The c jk represents the k group of maintenance personnel repairing the jth CNC machine tool; Li represents the loss cost per unit of time of CNC machine tools downtime; w i represents the CNC machine tools scarcity degrees. The maintenance scheduling for CNC machine tools is to select the most appropriate ijk

ijk

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maintenance group and to determine the sequence for each fault CNC machine, to achieve optimal performance of performance indicators (e.g. minimum maintenance time, maintenance cost of the lowest et al.). Assuming that maintenance resources of each maintenance group does not conflict with each other; the maintenance group at any time, only repairs a malfunctioning machine, until the end of the maintenance tasks; CNC machine tools have been running at full capacity; the prepare and the rest time of each maintenance group is T = 15min. The maintenance tasks assigned objective, when only consider the impact of the production time to production tasks after the repair, is computed as I

I

N

i =1

i =1

n =1

f1 = m in{å t ik + ( i - 1)T } = m in å {å t ink + ( i - 1)T }

(1)

The maintenance tasks assigned objective, when the impact of the repair time to the generation task and the influence of the scarcity and downtime costs of CNC machine tools to the generation task is taken into account, is computed as I

f 2 = m in{(å t ik + ( i - 1)T )w i Li } i =1

(2)

In order to better save maintenance costs and complete maintenance tasks, comprehensive consideration of the above factors: min ( f1 , f 2 ) (3) St. c(),k = 0 X ijk = 0 or 1

(4) (5)

åå X

(6)

k

ijk

=1

j

N

tik = å tink

(7)

n =1

Where constraint (4) represents the earliest maintenance start time of maintenance group is 0; constraint (5) represents the decision variables expressed as 0,1 adjustment variables; constraint(6) represents only one set of maintenance personnel to conduct a maintenance of i CNC machine; constraint (7) represents that a CNC machine repair time is the total of all components of the repair time. Multi-objective Particle Swarm Optimization The Particle Swarm Optimization (PSO) was proposed in 1995 by Professor Eberhart and Dr. Kennedy [6-7]. PSO gradually becomes a hot topic in combination optimization because of the simplicity of rules, the facility of program, the speediness of convergence and the few empirical parameters of dependent, etc.[8].PSO also has been widely used to solve multi objective optimization problem (MOP), for which can use efficiently cluster parallel to search non-dominated solutions, and each iteration can produce multiple non-dominated solutions, the memory function can make particle tracking the personal best and the global best, and the most important is the feature of functional and form of reconciliation [9]. Multi-Objective Particle Swarm Optimization algorithm based on global best position adaptive selection and local search is used to solve maintenance scheduling for CNC machine tools [10]. PSO is a stochastic population-based approach which uses a swarm of randomly initialized particles to fly through the search space. At each generation, particles update their velocities and positions using knowledge from their past experience and their neighbors [11]. This algorithm uses an external archive A to store non-dominated solutions during the particle flight. In order to avoid too many particles are selected from the file to gbest .The external file members is calculated the maximum number of gbest adaptively according to their crowding distance. Calculate the crowding distance of individual from the external archive, a maximum number G of g best individual for each external max

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Vehicle, Mechanical and Electrical Engineering

archive is adaptively allocated in accordance with the ratio of individual crowding distance and the sum of crowding distance with all the particles archives. The maximum number G is computed by: max

Gmax inf

æ ö ç max ( crowd j ) ÷ ç j =1,2,, s ÷ =ç s ÷ × N Gmax j ç ÷ crowd å j ç ÷ è j =1 ø

æ ö ç ÷ ç crowd j ÷ =ç s ÷×( N - åGmaxinf ) çåcrowd j ÷ ç ÷ è j=1 ø

(9)

is G value of the individual crowded distance to infinity in external archive; G is Where G the G value of the individual crowding distance to not infinite in external archive. The crowd is the crowding distance of j - th particles in external archive, j = 1, 2,, s , S is the particles individual number of finite crowding distance; N is the total number of particles in the swarm. The priority is determined according to G value of the external archive members, the greater of the G , the higher of the priority, and the priority of the same value G between the members of external archive is determined randomly. The g of G particles is determined by their order of priority of the individual in external archive, that the guiding particle is the G particle of the smallest Euclidean distance, which is selected from the population by the way of Sigma. Calculate the locations and velocities of N particles xi (t ) , vi (t ) using (10) and (11). All the particles is done non-uniform mutation by the Pm probability, which the mutation probability decreases from1 linear to 0. vi (t +1) = w × vi (t ) + c1r1 ( pbesti (t ) - xi (t )) + c2 r2 ( pbesti (t ) - xi (t )) (10) xi (t +1) = xi (t ) + vi (t +1) (11) max inf

max

max j

max

j

max

max

max

best

max

max

Case study The statistics of common failure parts of CNC machine tools in2013 from the maintenance records data of CNC machine tools in an enterprise axle production workshop is are shown in Figure 1.

Figure 1 Common failure parts of CNC machine tools A total of 14 sets of CNC machines is selected in the experiment, they are CNC cylindrical grinder, vertical lathe, roughing axles lathe, finishing axle lathes, forming grinding machines, wheel lathe, and number respectively M1 to M6. Each machine malfunction number i from 1 to 14. All equipment is maintained by five groups (K1-K5), each has three maintenance personnel, and maintenance personnel salary is20 Yuan / hour. According to maintenance records, failure probability that two or more parts fail at the same time is very low, generally about 1%, so assumption that each CNC machine has only one component fails is made in this paper, namely n = 1.In this article, common

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failure components are seals, electrical components, hydraulic valves, bearings, lubrication and gear six kinds. They were selected respectively for six types of CNC machine tools. The common failure component and the repair time of corresponding maintenance group are shown in Table 1, where blank indicates that the corresponding maintenance group can’t repair this type of CNC machine tools. Downtime for each unit cost of losses of CNC machine is shown in Table 2; selected types of CNC machine tools and weights are shown in Table 3. Assuming that each CNC machine started to repair time is 0, the state of each service group are idle. The way of equipment rarity maintenance is taken to schedule maintenance tasks of CNC machine tools now, which is sorted according to the scarcity of the CNC machine tools (weights). The detail procedure is as following: from the beginning of the first failure of CNC machine tools, choose the shortest time maintenance group to repair; if this maintenance group is repairing other CNC machine tools, then selecting the second shortest time maintenance group to repair, until the maintenance group determined. If all maintenance groups are busy, the first of idle maintenance group is selected to service these CNC machine tools. Table 1 Corresponding group repair time (unit: min) Machine tool type K1 K2 K3 K4 Maintenance group M1 40 30 25 M2 30 35 M3 75 60 55 M4 125 120 130 M5 30 45 M6 90 90 110

K5 40 80 120 40 80

Table 2 Loss cost per unit of time (unit: Yuan / min) number machine type weight number machine type weight 1 M6 0.02 8 M1 0.12 2 M3 0.25 9 M3 0.25 3 M1 0.12 10 M2 0.22 4 M6 0.02 11 M1 0.12 5 M2 0.22 12 M4 0.26 6 M4 0.26 13 M5 0.13 7 M5 0.13 14 M5 0.13 The MOPSO – GL algorithm parameter is set as following: the weight parameter w is 0.7, cognitive scaling factor c1 is 2, social scaling factor c2 is 2, the particle swarm size N is 100, archive size A is 100, and the maximum number of iterations M is 200. According to the above model and design algorithm, MATLAB7.0 programming is used to solve maintenance scheduling, and the one solution Gantt chart is shown in the Figure2 by MOPSO. max

Fig.2 Maintenance scheduling solution

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Vehicle, Mechanical and Electrical Engineering

All the fault machine maintenance needs 255min, and the total damage cost is 3530.82yuan using the existing way of maintenance. All the fault machine maintenance needs 230min, and loss cost is 3317.2yuan using MOPSO algorithm for scheduling. Two methods are compared to conclude that time saved 20min, and the total cost reduced 14.5%. Conclusion The mathematical model of maintenance scheduling for CNC machine tools are established, and solving the model by MOPSO algorithm. Then comparing simulation results show that MOPSO algorithm saves much more maintenance time and cost than the existing way of maintenance scheduling method. The MOPSO algorithm is successfully applied to CNC machine maintenance scheduling in this paper, which broadens the applications of MOPSO, and studies show that the algorithm is effective to solve this problem, and help to improve enterprise equipment maintenance efficiency. Acknowledgements Fund Project: National Natural Science Foundation (71,271,160); Metallurgical Equipment and Control, Ministry of Education Key Laboratory Open Fund (2013A06). References [1] Y.Z.Jia: Manufacturing Information Engineering of China, Vol. (2006) No.6, p.42 [2]Y.Li:Domestic and Foreign-made CNC Lathe Reliability Contrast Analysis (MS, Jilin University, China 2006),p.8 [3]H.M.Ma,S.L.Xu and C.M.Ye:Industrial Engineering and Management,Vol(2008) No.6, p.66 [4]S.Zhang,H.M.Ma and L.Ma:Application Research of Computers,Vol.28(2011) No.6, p.2055 [5]J.W.Cheng,C.Wang and X.G.Liu:Ordance Industry Automation,Vol.30(2011) No.4, p. 5 [6]E.R.C.and K.J:.Proceedings of the Sixth International Symposium on Micro Machine and Human Science(Nagoya,1995)p.39 [7]K.J. and E.R:International Conference on Neural Networks(Perth,Australia,1995)p. 1942 [8]S.C.and C.A.C:.International Journal of Computational Intelligence Research Vol.2(2006) No.3, p.287 [9]F.Pan,W.X.Li and Q.Gao:PSO and multi-objective optimization (Beijing Institute of Press, China 2013), p. 139 [10] M.Huang.Y.Jiang and A.Mao: Journal of Computer Applications,Vol.34 (2014) No.4, p.1074 [11] T.H.Kim, I.Maruta and T.Sugie: Mechanical Engineering Science,Vol.224(2010)No.2,p.389

Applied Mechanics and Materials Vol. 721 (2015) pp 149-152 © (2015) Trans Tech Publications, Switzerland doi:10.4028/www.scientific.net/AMM.721.149

Submitted: 01.11.2014 Accepted: 06.11.2014

On the Axisymmetrical Dissemination of Glycerine Driven by Shock Wave Lei Yang a, Xianglong Yang and Zhongwei Huang College of Civil Engineering, Shenzhen University, Shenzhen 518060, China a

[email protected]

Keywords: instability, dissemination, liquid front, breakup.

Abstract. Pressure transducers and high-speed photography technology were applied on the experimental device which formed the axisymmetrical dissemination of glycerine. The instability development at gas/liquid interface and the primary breakup were recorded by high speed photographs. It can be concluded that the wavelength of initial disturbance waves will decrease with the incensement of shock wave intensity. At the same time, the degree of mixing of spike and airflow will also be increased. The acceleration of liquid front remains unchanged in the earlier stage and rise rapidly in the later stage. Introduction Quality of the spray and dissemination is a critical factor in the power of Fuel-Air-Explosive which is mostly determined by the primary breakup of liquid fuel. The primary breakup of liquid fuel is caused by the combination of Richtmyer-Meshkov instability and Rayleigh-Taylor instability. Many valuable research works were carried out in the R-M instability and R-T instability of liquid/gas interface in the past fifty years. Samirant [1] acquired the experimental photos of the explosive dissemination of liquid fuel with ultra-high speed photography and x ray photography. Gardner [2,3] studied the process of fuel dissemination and divided it into near field stage and far field stage. Shi [4] studied the mixed zone of liquid/gas interface at the later stage of Richtmyer-Meshkov instability. Li [5] achieved the primary breakup of liquid/gas interface with shadowgraph and planar laser induced fluorescent (PLIF). In this paper, with vertical rectangle shock tube combined with trapezoid dissemination section, axisymmetrical dissemination of liquid was obtained. Applying the high-speed photography technology, the development process of instability on glycerine/air interface of liquid axisymmetrical dissemination was studied. Experimental Facility Fig.1 shows the front view of the vertical experimental facility which contains three main sections. The high pressure section with 800mm length was full of Nitrogen. The transversal surface of high pressure section was 40mm*20mm rectangle. The low pressure section with 600mm length was full of ambient atmosphere whose transversal surface was also 40mm*20mm rectangle. The dissemination section was an isosceles trapezium expansion section connected with the ambient atmosphere. The trapezoidal bottom width is 40mm, the altitude of trapezium is 600mm and the base angle of trapezium is 105°. The dissemination section was made by polyester plate which has high impact toughness and high transmittance. Fig.2 shows the side view of the experimental facility. The high speed camera records the flow field. The frame rate of high speed camera is 10000 FPS and the image resolution is 512*768 pixel. 53.4 ml of glycerine was injected on the top of 2# mylar diaphragm before experiments. The high pressure section was filled with high pressure Nitrogen which will broke the 1# mylar diaphragm to generate moving shock wave which moved upward. An electrical signal was generated by the shock wave and amplified by the charge amplifier. With accurate delay, the amplified signal went into the

150

Vehicle, Mechanical and Electrical Engineering

synchronizer and trigged the high speed camera. The moving shock wave kept moving and broke the 2# mylar diaphragm to generate the axisymmetrical dissemination of glycerine. Light source

Dissemination section 2# Mylar diaphragm

glycerine 2# Pressure Transducer Air

1# Pressure Transducer

2# Pressure Transducer

Low pressure section

1# Pressure Transducer

1# Mylar

diaphragm

Pressure Gauge

N2

High-speed Camera

charge amplifier

High pressures section

Fig.1 The Front view of experimental facility

Fig.2 The Side view of experimental facility

Results and Discussions Table 1 Experiment Cases Case Number Specification 1

2

3

4

Thickness of mylar diaphragm 1(mm)

0.05

0.075

0.125

0.15

Pressure of high pressure section(MPa)

0.41

0.62

0.90

1.07

Mach number of Shock wave

1.35

1.46

1.57

1.63

P5 (MPa)

0.36

0.49

0.65

0.74

The experimental liquid was glycerine with 1.26 g / cm3 density, 63.3mN / m surface tension and 1412mPa ⋅ s coefficient of viscosity. The Atwood number approximately equals to 1. Four different thickness diaphragms were used in the experiments and achieved different axisymmetrical dissemination of liquid driven by shock waves with different Mach number. The instability can be regarded as a two dimension problem. The detailed experiment cases were shown in Table 1.

a. 0.8ms

b.2.4ms

c.4.0ms

d.5.6ms

Fig.3 Photos of case 1

e.7.2ms

f.8.0ms

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Fig.3 is the photos of high speed camera in case 1. As shown in Fig.3.a, before the fracture of 2# mylar diaphragm, the liquid front kept flat. In Fig.3.b, at the time of 2.4ms, the interface of glycerine and high pressure air entered the transparent observation window. In Fig.3.d, The bubbles and spikes can be observed clearly at the liquid/gas interface under the combined effect of R-M instability and R-T instability. The bubbles and spikes were distributed randomly at the interface. With the movement of bubbles and spikes, the width of glycerine /air mixing zone was increased and the height of bubbles and spikes was also increased. For the development of instability, new bubbles and spikes would be formed at the interface. As shown in Fig.3.e, the front of bubbles kept circle during the development of glycerine/air mixing zone. Because of the effect of the surrounding liquid and drafting of the wall surface, there were signs of atomization at the top of spikes. Fig.3.f shows the primary breakup taken place at the top of liquid. The liquid mass changed into ribbon shape fragments. Strong atomization was taken place at the top of spike which was the black shadow part at the middle of Fig.3.f. Fig.4 is the photo of high speed camera in four different cases with the same position of experimental liquid. It can be observed that the flow fields are mostly in the same figure. With the incensement of the thickness of mylar diaphragm 1 and the mach number of moving shock wave,the perturbation wavenumber on the interface of glycerine/air increased. Depend on the impact wave model [6], the wavenumber k = 2π / λ ( λ is the wavelength of disturbance wave). From the above, it can be concluded that the wavelength of initial disturbance wave decreased with the incensement of Mach number of shock wave.

a. case 1, 5.7ms b. case 2, 4.8ms c. case 3, 4.1ms d. case 4, 4.1ms

Fig.4 Photos of the dissemination process of four cases

a. case 1, 7.4ms b. case 2, 6.3ms c. case 3, 5.7ms d. case 4, 5.4ms

Fig.6 Relationship between acceleration of liquid front and time

Fig.5 Photos of the primary breakup of four cases Fig.5 shows the primary breakup of the liquid in the four different cases. It can be observed that because the liquid kept moving upward, the thickness of liquid layer became more and more thin. Finally, the high pressure air behind the liquid layer broke through it and formed the primary breakup of experimental liquid. By Comparing the four different cases, it can be drawn that the stronger the shock wave was, the larger the range of the primary breakup of the spike. As shown in Fig.5.c and Fig.5.d, high concentration of droplets formed by the secondary breakup appeared behind the glycerine/air mixing zone. The development speed of the spike increased with the incensement of shock wave. And the effect of K-H instability and surface tension was stronger that enhanced the mixing degree of spike with airflow. By photographic interpretation, the displacements of liquid fronts were achieved. By using the central difference formula on the displacement-time relationship, velocity-time relationship can be

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achieved. And by using the central difference formula on the velocity-time relationship, acceleration-time curves can be achieved. The time-step remains 0.4ms in the central difference formula. Because the displacement of liquid front was too little to get an accurate displacement by photographic interpretation at the earlier stage that before 1ms, the inaccuracy of velocity and acceleration which calculated by central difference formula is too large. The discrete points of the acceleration-time curves were started from 1ms. The velocity at 0ms was obviously 0 m/s. The acceleration at 0ms was calculated approximately by Newton's second law of motion. It can be observed from the acceleration-time curves that the variation trend have no difference under the action of different shock waves. Before 3ms, because the displacement of liquid and the velocity of liquid front were too small to change the shape and thickness of experimental liquid, the interfacial area of high pressure air and the liquid was almost unchanged, the driving force was also unchanged and kept the acceleration unchanged. At the later stage, with the fast movement of liquid front, the thickness of liquid decreased rapidly and the interfacial area increased rapidly, the driving force which caused the acceleration increased rapidly.

Conclusions (1) The R-T instability and R-M instability of liquid dissemination was recorded at the test section with high-speed camera. The bubbles and spikes at the glycerine/air mixing zone were clearly observed. For the block of the flange plate, the early stage of the instability cannot be observed. (2) With the incensement of driven pressure and mach number, the wavelength of initial disturbance waves decreased and wave number increased. And with the incensement of mach number, the effect of K-H instability became more dramatic to make the spikes breakup and mix with the airflow. (3) The positions of primary breakup didn’t change with the incensement of shock wave. The acceleration of liquid front remains unchanged in the earlier stage and rise rapidly in the later stage.

Acknowledgements Projects supported by the National Natural Science Foundation of China (Grant No. 11102116 and 11102117) are gratefully acknowledged.

References [1] M. Samirant, G. Smeets, C .Baras, etc, Dynamic measurements in Combustible and detonable aerosols. Propellants Explosives Pyrotechnics Vol. l4, p.47-56, (1989) [2] D.R .Gardner, Near-field dispersal modeling for liquid Fuel-Air-Explosive, Sandia National Laboratories Report. SAND-90-0686, (1990) [3] D. R. Gardner, M.W. Glass, A coupled near-field, far-field dispersal model for Fuel-Air-Explosives. Sandia National Laboratories Report. SAND-90-0687, (1991) [4] Shi Honghui, Kishimoto Masami. Fluid mechanics in the transient acceleration of a liquid column [J], Explosion and shock waves, (2003), 23(5), p.391-397. [5] Li Lei, Cui Jian, Dong Yucai, etc, Experimental investigations to the interfaces breakup during liquid explosive disseminations process, Chinese science bulletin [J], (2009), 54(12), p.1693-1700. [6] R.D. Richtmyer, Taylor instability in shock acceleration of compressible fluids [J], Commun. Pure App. Math., (1960), 13, p.297-319.

Applied Mechanics and Materials Vol. 721 (2015) pp 153-156 © (2015) Trans Tech Publications, Switzerland doi:10.4028/www.scientific.net/AMM.721.153

Submitted: 27.10.2014 Accepted: 01.11.2014

Static stiffness analysis of high stiffness rotary hinge locking mechanism for SAR antenna Qiang Cong1, a, Yan Wang2, b, Congfa Zhang1, c, Rongqiang Liu2, d and Jin Wang3, e 1 2

China Academy of Space Technology, Beijing 100090, China

School of Mechatronics Engineering, Harbin Institute of University, Harbin 150080, China 3

a

Unit 61330, People’s Liberation Army, Xinxiang 453000, China b

c

d

[email protected], [email protected], [email protected], [email protected], e [email protected]

Keywords: Locking mechanism, high stiffness, rotary hinge, static stiffness analysis.

Abstract. Rotary hinge plays an important role in the support structure of the deployable planar antenna. The high stiffness hinge locking mechanism is designed in this study which is composed of active half, follower half, pre-meshing spring and rotary axes. The static stiffness of the mechanism is investigated by theoretical analysis, numerical method and experiment. The test apparatus is constructed in order to measure the prototype. The tests included locking force, locked stiffness, drive torque are performed which are used to verify the analysis and the design. Introduction Deployable truss structure is a requirement of space borne planar antennas which is used to ensure the high precision and high stiffness[1, 2]. Rotary hinge plays[3,4] an important role in the support structure of the deployable planar antenna. In order to get high stiffness of deployable antenna, it is feasible to design locking mechanisms that worked cooperated with rotary hinges to lock the deployed antenna[5]. In addition high stiffness rotary hinge locking mechanism must get the character of high preload, small size and low weight to save the cost of being used in the deployable planar array antenna[6]. In this study, high stiffness rotary hinge locking mechanism that used in space borne planar antenna was taken for the study. And with the idea of using preload spring to improve the locked stiffness, the design of precept, analysis, simulation and tests of prototype was carried out. Structure design High stiffness rotary hinge is composed of active half, follower half and rotary axes as shown in Figure 1. Active half provides locking force for the hinge, follower half is locked part and axes provide rotary axes for antenna deploying motion.

Fig. 1 High stiffness rotary hinge Fig.2 Simplified locking mechanism graphic Both trigger mechanism and locking force of connecting the two antenna panels are provided by active half. The follower half supplies connection interfaces for the active half. Simplified locking mechanism graphic is shown in Figure 2. Before deploying, there is some distance between the active

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half and the follower half without relative movement. The spring is compressed in the Figure 2. When the antenna panels deploys, the angle between the two antenna panels begins increase from 0°. When distance between the panels reach a certain value, the main hook of one antenna panel captures follower half hook of the other panel using the relative movement between the panels. Furthermore, the two hooks move to pre-meshing position. Static stiffness theoretical analysis Force diagram of locking mechanism in locked statue is shown in Figure 3. Firstly take no account of stiffness in the case of structural deformation and the structural parts except spring are assumed as rigid bodies. Although the two hinge halves tend to separate by external moment M, they are tightly pressed together under spring pre-meshing force F. When moment generated by the pre-meshing spring about the rotary axes is larger than the external moment M, i.e., M< F×R, the pre-meshing spring has no deformation and the two halves do not separate. The abbreviation When M>F×R, the pre-meshing spring has no deformation and the two halves separate. Stiffness of the high stiffness rotary hinge k is written as k MR 2 M (1) = s k= θ M − FR where k s is stiffness of the pre-meshing spring, R is the distance from active line to the rotary axes of the hinge (m), δ is deflection of the spring and θ is distortion angle. The moment-deflection relationship is shown in Figure 3. For a certain locked force the rotary radius R should be large enough to resist the greater external moment M. However large rotary radius leads to structural dimension bigger. For F=1000N and R=0.1m the stiffness-moment profile is shown in Figure 4. It is indicated that when the moment is larger than 100Nm the spring is suppressed and the stiffness decreases suddenly.

Fig. 3 Force diagram

Fig.4 M- δ curve

Fig. 4 Stiffness profile The structural parts are considered as rigid bodies in all previous theoretical derivation. In practical application the structures parts just possess a nonlinear and certain stiffness. To simplify operation the stiffness of the structural parts is set as linear and equivalent stiffness of the locking mechanism is shown in Figure 5. It is worth noting that the rotary radius R is regardless in the following study and the dimension is changed to N/m. kz kz ks kc kj

kg

kc kj

kj

kg kj

Fig. 5 Simplified stiffness Fig. 6 Further simplified stiffness All the springs are compressed and the equivalent stiffness between the active half and the follower half is written as k z k s kc k g kj k′ = − (2) k z k s kc + k z k s k g + k z kc k g + k s kc k g 2

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Considering of the rotary radius, the linear displacement is converted to angular displacement and the stiffness is changed as follow k z k s kc k g R 2 kj − R2 k= (3) k z k s kc + k z k s k g + k z kc k g + k s kc k g 2

When M 0 α dt α 1, Re(α ) = 0 a Dt = t −α Re(α ) < 0 a (dτ ) , is an arbitrary plural. Let α > 0 be a real number, so

(2)

∫

Where, α α a Dt is fractional differential. Fractional integral of Riemann-Liouville is defined as follows −α f (t ) = a Dt

d α f (t )

=

[d (t − α )]α

a

Dt−α is fractional integral, and

(t − τ )α −1 f (τ )dτ , ∫a Γ(α ) t

(3)

where t ≥ a , Γ(α ) is the Gamma, it is defined as Γ( x ) =

x

∫e

−t x −1

0

t

(4)

dt

Similarly, the fractional derivative of Riemann-Liouville is defined as follows α a D t f (t ) =

1 dm Γ(m − α ) dt m

t

f (τ )

∫ (t − τ )α a

− m +1

dτ ,

(5)

where m − 1 ≤ α < m , m ∈ N , these expressions define uninitialized fractional calculus. With the study of fractional calculus in the control, a new area of control theory appears which is fractional control theory and control. The transfer function of differential equations for the fractional order PIλDµ controller is G c (s ) = K p +

KI s

λ

+ K D s µ , (λ , µ > 0)

.

(6)

As can be seen from the above equation, integer order PID controller is a special circumstances of fractional order PIλDµ controller when λ = 1 and µ = 1 .Due to the two adjustable parameters λ and µ of fractional order PIλDµ controller which can also continuously change, it’s more flexible than the integer order PID. By selecting the parameters properly, fractional order PIλDµ controller can improve the control performance of the system. 3.2 Digital implementation of fractional controller Fractional calculus can usually be achieved in some digital or similar methods. Oustaloup algorithm[9] is used to fit the Laplace operator s γ of fractional calculus in this paper. When the frequency domain of fitting is (ω b , ω h ) , the Laplace operator s γ can be instead by the following approximate system H (s ) = K

N

s + ω k' . s + ωk k =− N

∏

Where, pole-zero and gain of filter can be obtained directly by the following formula

(7)

Applied Mechanics and Materials Vol. 721

1 (1−γ ) 2 2 N +1

k+N +

ωh ωb

ω k' = ω b

1 (1+γ ) 2 2 N +1

207

k+N +

,

ωh ωb

ω k = ω b

, K = ω hγ .

(8)

γ

is the order of fractional calculus, 2n+1 is the order of higher-order system, due to the characteristics of the Oustaloup algorithm, set ω b ω h = 1 . Design fractional differential module by the Oustaloup algorithm, then the simulation model of fractional order PIλDµ controller can be constructed with blocks of Simulink. 3.3 The design of fractional order PIλDµ controller based on PSO PSO[10] is proposed by Kennedy and Eberhart in 1995, and it’s a stochastic optimization algorithm based on swarm intelligence which is developed on antifogging the behavior of birds foraging. Every possible solution of optimization problems of PS0 algorithm is a particle in the search space, and fitness decide whether the solution is OK. Particles adjust themselves by tracking two optimal solutions: one is found by the particle itself, called individual optimal solution Pid ; the other is found by the whole group, called global optimal solution Pgd . In every iteration, the particles update its velocity and position through the individual and groups extreme extremes, which are Vidk +1 = ϖVidk + c1r1 Pidk − X idk + c2r2 Pgdk − Xidk , (9)

(

)

X idk +1

X idk

(

)

+ Vidk +1

= . (10) ω 2， ，D , i = 1,2, , n , k is the current iteration, Vid is particle Where, is inertia weight, d = 1， velocity, c1 and c 2 is non-negative constants called acceleration factor, r1 and r2 is the random number distributed in [0,1], the position and velocity are limited in a certain interval [− X max , X max ] and [− Vmax , Vmax ] .

Real coded is used for fractional order PIλDµ controller in PSO, the particle can be directly encoded as [K p , K i , K d , λ , µ ]. The purpose of optimization is that the deviation tends to zero, have smaller overshoot and faster response. The common performance indicators of errors include ISE, IAE, ITAE, ISTE and so on, and choice ITAE here, it is defined as J=

∫

∞

0

t e(t )dt .

Design the fractional controller based on PSO, the flowchart is shown in Fig. 1.

Fig.1 The flowchart of optimizing the fractional order PIλDµ controller by PSO

(11)

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Vehicle, Mechanical and Electrical Engineering

4 Simulation Suppose the population size is 50, the dimension is 5, the number of iterations is 100, the inertia factor is ω = 0.6 , the acceleration factors are c1 = c 2 = 2 , the minimum fitness is 0.1, the range of velocity is [− 1,1] , the scope of parameters K p , K i , K d , λ, µ are [0,10] , [0,10] , [0,100] , [0,2] , [0,2] . Design a fractional order PIλDµ controller for the temperature system of the above heating furnace G f 1 = 3.5 + 0.008s −1.2 + 70s 1.2 . Then let ITAE as the goal, to optimize the parameters of fractional order PIλDµ controller through PSO. Then find the most optimal fractional controller by the Simulink block, as shown in Fig. 2.

Fig.2 The simulation diagram Optimize the parameters of fractional order PIλDµ controller by PSO, get J = 1139 , and the optimal fractional order PIλDµ controller is G f = 4.3128 + 0.0099s −1.1855 + 66.2514s 1.1746 . Fig. 3 is the curve of ITAE. According to Fig. 3, when iteration is 70 the fitness is no longer change, and the parameters no change ether. Let λ = µ = 1 , then can get the optimal PID controller by PSO Gi = 4.0008 + 0.0197 s −1 + 50.3939s . The fractional order PIλDµ controller and PID controller based on PSO are substituted into the control system, and then get the step response curve through the simulation. And compare with the step response of the given fractional order PIλDµ controller, as shown in Fig 4.

Fig.3 The curve of ITAE of PSO optimizing Fig. 4 The step response curves of the system As shown in Fig 4, the ts of the step response of fractional order PIλDµ controller based on PSO is 90s, and no overshoot. The ts is shorter than the original fractional order PIλDµ controller, and it rises faster. While optimize the PID controller based on PSO, the ts is 200s. It has a longer convergence time, slower rising velocity, and larger overshoot than fractional order PIλDµ controller based on PSO.

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So the fractional order PIλDµ controller has better convergence accuracy and faster convergence speed than PID controller. And PSO can optimize the controller parameters faster and more convenient, to achieve the optimal control. 5 Conclusions With the research and development of fractional control theory, fractional control has become a new hotpot in the research of the control in recent years. For many complex practical systems, the integer controller has not well improved the control performance of the system, so it is necessary to adopt fractional order control system to accurately describe the characteristics of the system. A fractional order PIλDµ controller based on particle swarm optimization method is designed in this paper. And optimize the 5 parameters of fractional order PIλDµ controller using PSO. Then compare it with the PID controller which is optimized under the same conditions. Simulation results show that, the fractional order PIλDµ controller based on PSO has better convergence stability, faster response times and higher accuracy value, and can get the optimal fractional order PIλDµ controller faster and more convenient, then to achieve the optimal control. So the fractional order PIλDµ controller has better dynamic performance than PID controller, and greatly improves the control quality of the system. 6 References [1] J.P. Sun, Y.Y. Qi. Application of DMC in temperature control system of electric heater. Computer Simulation, Vol. 30 (2013) No. 6, p. 386-388. [2] S.H. Duan. Design of temperature control system of electric heating furnace based minimum beat no-wave algorithm. Foundry Technology, Vol. 21 (2013) No. 8, p. 1081-1082. [3] D.D Wen. Temperature control system of resistance-heated furnace based on fuzzy immune PID. Metallurgical Industry Automation, (2007) No. 6, p. 43-46. [4] L.P. Zhang, L.X. Ma, Zh.Zh. JIN. Design of fuzzy self-adaptive PID control system for resistance furnace temperature. Hot Working Technology, Vol. 41 (2012) No. 14, p. 234-236. [5] Podlubny I. Fractional Differential Equations (San Diego: Academic Press, 1999). [6] J.Sh. Mou. Study on tuning of fractional-order PID controller (MS., East China University of Science and Technology, China 2013). [7] T.H. Yu. The Research of fractional controller parameters optimization based on swarm intelligence algorithm (MS., Dalian University of Technology, China 2013). [8] H.F. Xu, J. Kong. Particles group optimization of PID control of heating furnace temperature control system. Hot Working Technology, Vol. 42 (2013) No.14, p. 212-214. [9] Oustaloup A, Levron F, Mathieu B, etal. Frequency-band complex noniteger differentiator: characterization and synthesis. IEEE Transaction on Circuit and Systems-I: fundamental Theory and Applications, Vol. 47 (2000) No. 1, p. 25-39. [10] C. YANG Cheng, YANG Chuan-qi. A PSO based approach to optimize PID parameters. Process Automation Instrumentation, Vol. 27 (2006), P. 27:95-96.

Applied Mechanics and Materials Vol. 721 (2015) pp 210-213 © (2015) Trans Tech Publications, Switzerland doi:10.4028/www.scientific.net/AMM.721.210

Submitted: 07.10.2014 Accepted: 13.10.2014

Self-Scheduled and Robust Control for Tilt Rotor Aircraft Yu-Yan Cao a,*, Xin-Min Wang b and Cheng Peng c School of Automation, Northwestern Polytechnical University, Xian 710129, China a

b

c

[email protected], [email protected], [email protected]

Keywords: Self-scheduled, Convex Combination, Robust.

Abstract. A kind of self-scheduled and robust control algorithm for the conversion mode of tilt rotor aircraft is proposed in this paper. The mathematical model of the aircraft is established by modeling each component and combining. Then the conversion corridor is obtained by trim results. The convex combination of several vertex system combined with two-dimensional interpolation is designed to solve the problem of self-scheduled while nacelle tilting. Last sufficient and necessary conditions for the existence of the state-feedback controller are given and a MATLAB tool is raised to ensure performance of the system. Introduction The tilt rotor aircraft combine the vertical or short take-off and landing (V/STOL) capability of the helicopter with the speed and range of conventional fixed wing airplane. It has three modes: the helicopter mode, the conversion mode and the airplane mode. The transition process is obviously complicated and very important for tilt rotor aircraft. The dynamic response of the aircraft will be governed by time-varying aerodynamic forces and moments with the tilt of the nacelle. System modeling and controller design for tilt rotor aircraft must take into account the dynamic coupling between wings and rotors, mass distribution change and multiple control actuators. As aerodynamics do vary in large ranges while in conversion mode, the dynamic response in nacelle tilting and the final balanced kinetic parameters will also exhibit large variations. Consequently, the flying quality of tilt aircraft may deteriorate and flight safety may be threatened. To guarantee satisfactory flying quality, flight control system is required to maintain stability during the tilting transition phase. Many scholars have studied how to design the flight control system. Rolf. Rysdy k and Anthony J Calise have used adaptive model inversion to design the tilt rotor flight control system. [1] Ajay Verma and John L. have studied trajectory generation for transition from VTOL to Wing-borne flight by using inverse dynamics. [2] The method of using neural networks to design the nonlinear adaptive control of tilt rotor is interpreted. [3] However the scheme of conversion has been infrequently mentioned. In this paper, a kind of self-scheduled and robust control is proposed to ensure the safety and timeliness of the conversion mode of the tilt rotor aircraft. Model of Tilt Rotor Aircraft In modeling tilt rotor flight dynamics, the model of rotor is the most important part. The aerodynamic model of rotor must include the aerodynamic model of aerofoil, the induced velocity model, and the blade flapping model. For the details of calculating the aerodynamic forces of a rotor, one can refer to Refs.[4]. Assume that the aerodynamic model of the wing is rigid without elastic deformation. In the free wake, the aerodynamic force models of wing, fuselage, horizontal tail, vertical tail, and nacelle can be obtained from Ref.[5] while the method to calculate the aerodynamic force model of the wing in the rotor-disturbed region from Ref.[6]. According to the above-cited modeling method, the forces and moments of each component are obtained and thus the forces and moments of the tilt rotor aircraft can be combined by Equ. (1).

Applied Mechanics and Materials Vol. 721

Fx Fxw Fxf F F F y yw yf Fz Fzw Fzf + = M x M xw M xf M y M yw M yf M z M zw M zf

211

Fxht Fxvt Fxr Fyht Fyvt Fyr Fzht Fzvt Fzr + + + M xht M xvt M xr M M M yht yvt yr M zht M zvt M zr

(1)

where x, y, z represent the three directions of the body axis, w, f , ht , vt and r represent wing, fuselage, horizontal tail, vertical tail and rotor respectively. Put the forces and moments into the six-DOF nonlinear aircraft equations, and the nonlinear mathematical model of tilt rotor aircraft is obtained. As the tilt rotor aircraft is a hybrid aircraft, it uses both helicopter and airplane control strategies to control the aircraft. At different flight stage, tilt rotor aircraft uses different control strategies. As the nacelles are rotating forward from helicopter towards airplane mode, the amount of control required from the proprotors decreases as the dynamic pressure increases and the airplane mode control surfaces become more effective. Once the nacelles are positioned for airplane mode, the proprotor contribution to lift has essentially been phased out and essentially only the thrust effects remain. The airplane control surfaces are always active, even though they don’t have much effect at low airspeeds. Fr

80

F 70

Fw

Mw

60

F

Fvt M

M vt

βM

Fht M ht

x

tilting angle(deg)

δc δl δ cd δ ld δe δr δa

90

Mr

50 40 30 20

M

βM

Ff

Mf

10 0

0

10

20

30 40 Velocity(m/s)

50

60

70

Fig.1 Nonlinear model of the tilt rotor aircraft Fig.2 Conversion corridor obtained by trim results Hence, the whole nonlinear mathematical model of the tilt rotor aircraft is shown as Fig.1, where δ c is the collective pitch and δ l is the longitudinal cyclic pitch; δ cd is the differential collective pitch and is δ ld differential longitudinal cyclic pitch; δ e , δ a , δ r are elevator deflection, aileron deflection and rudder deflection, respectively; β M represents the nacelles’ tilting angle; x is the state vector of the aircraft. Convex Combination System in Conversion Mode Before the aircraft converting between the helicopter mode and the airplane mode, a proper conversion path should be decided to ensure the safety in conversion. Conversion can be made within this corridor having a wide range of airspeeds, conversion angles and fuselage attitudes. The principle of choosing conversion corridor is described as follows: the lift of the aircraft is greater than the gravity so that the aircraft can be controlled; the pitch angle of the aircraft should make slow changes at low speeds. When the aircraft’s forward speed gradually increases, the pitch angle could be kept fixed in the vicinity of a given value to make the conversion flight of the aircraft as stable as possible. The conversion corridor can be optimized by the experience from flight tests as well as trim results. Fig. 2 indicates the conversion corridor obtained by trim results. The height shows little change for the pitch angle changes little during conversion. However, as shown in Fig. 2, the velocity changes a lot in this process and the aerodynamic parameters are closely related to the height and the Mach number, so the mathematical model of the aircraft ought to change with the state of the aircraft. Thus, one set of controlling parameters cannot guarantee the security of the flight in conversion mode. The model of the aircraft during the process of conversion can be described by the equation below with the time-varying variable:

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S (ω ( t ) ) x = A (ω ( t ) ) x + B (ω ( t ) ) u

(2)

where, x = [u, w,θ , q ] , u = [δ l , δ e ] , ω ( t ) ∈ Ω , and Ω = [Vmin ,Vmax ] × β M , β M represents the parameter space made up of the tilt angle β M and velocity V . The known LTI system is defined as Si ( i = 1, 2, , N ) , and any system S (ω ( t ) ) can be approximated by S * (ω ( t ) ) , that is, the convex combination of several vertex system Si . The weighting coefficient of Si is defined as α i . The approximation can be described by Equ.(3). T

T

min

N

N

i =1

i =1

max

S * ( ω ( t ) ) ∑ α i Si → S ( ω ( t ) ) , ∑ α i = 1

(3)

If the number of the vertex system is approach to infinity, the combined system can approach to the original system exactly. lim S − S * ≤ ε (4) N →∞ Suppose G1 and G2 are transfer functions of two operating points. The normalized stable coprime factorization of G1 and G2 are defined as Gi = N i Di−1 , i = 1, 2 (5) and N i , Di ∈ RH ∞ . The transfer function of the plant G is described by a linear interpolation of the coprime factorization (5) as N = α N1 + (1 − α ) N 2 ,0 ≤ α ≤1 G = ND −1 , D = α D1 + (1 − α ) D2

(6)

The range of the parameters can be decided by the upper and lower bound of the conversion corridor. The bound of the velocity for each tilt angle is different in the conversion corridor, so for every tilt angle parameter, the corresponding upper bound, lower bound and the mid value of the ° ° ° velocity are chosen as the velocity parameters. The tilt angle parameters are β M = ( 0 ,10 , 90 ) , so there are 30 vertex systems in all. Tilt angle and velocity are aware, the weighting coefficients of the convex combination can be decided by two-dimensional interpolation. The combination can be summarized in the following steps. Step1: establish the conversion mathematical model, offer trimming solution and obtain the conversion corridor. Step2: determine the parameter space, divide the space and prepare all the vertex system. Step3: decide the weighting coefficients by two-dimensional interpolation. Design of Robust and Self-Scheduled Controller With the nacelles and rotors tilting forward, the aircraft converts from the helicopter mode to the fixed wing mode, the control of rotor is replaced gradually by the control of fixed wing and the speed increases constantly. Thus the weights of the longitudinal cycle pitch and the elevator are changing at the same time. And the assignment of the weights is shown below, where Wδ is the weight of the longitudinal cycle pitch and Wδ is the weight of the elevator. l

e

1 − (u 75) 2 Wδl = 0

(u 75)2 (40 ≤ u < 75) , Wδe = (u ≥ 75) 1

(40 ≤ u < 75) (u ≥ 75)

(7)

By incorporating the convex combination system in Section 3 and the weights assignment mentioned above, the controller adjusts to the weights to maintain stability and reduce time-consuming procedures. In other words, the controller is self-scheduled, that is, automatically gain-scheduled with respect to the time-varying parameters. For system (2), the sufficient and necessary condition for the existence of a state feedback controller u = Kx which makes the closed loop stable and satisfying Gzw ∞ < γ is: there exist ε ( ε > 0 ) and P ( P = PT > 0 ) satisfying AT P + PA + γ −2 PB1 B1T P − ε PB2 B2T P + C1C1T < 0 (8)

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213

K can be constructed as K = − ε B2T P 2

(9) And MATLAB function hinfstruct in Robust Control Toolbox can be used to tune the parameters to meet H ∞ constraints. Simulation Result and Summary According to the design procedure mentioned above, simulation of the conversion mode was done and the results are as follows. The figure left shows changes of the velocity, the pitch angle and the angular during the process of transition and curves reflect good control effect. The figure right gives changes of forward distance and height. The height changes little while tilting. And controller can ensure a smooth transition and satisfactory flying quality. 600

1

60

x(m)

0 w(m/s)

u(m/s)

80

-1 -2

400 200

-3 40 0

5 time(s)

10

0

5 time(s)

0 0

10

2

4

6 time(s)

8

10

12

2

4

6 time(s)

8

10

12

0

-2

8

-1

6

-2 h(m)

q(deg/s)

θ(deg)

0

4

-4

-5

0 0

5 time(s)

10

-3 -4

2

0

5 time(s)

10

0

Fig.3 Changes of states while conversion. Fig.4 Changes of forward distance and height. References [1] Rolf. Rysdy k and Anthony J Calise Adaptive Model Inversion Flight Control for Tiltrotor Aircraft. AIAA-97-3758 [2] Ajay Verma and John L. Junkins Trajectory Generation for Transition from VTOL to Wing-borne Flight Using Inverse Dynamics AIAA-2000-0971 [3] Rolf. Rysdy k and Anthony J Calise Nonlinear Adaptive Control of Tiltrotor Aircraft Using Neural Networks AIAA-97-5613 [4] Wang S C. Helicopter Aerodynamic. Nanjing University of Aeronautics and Astronautics, 1993. [5] Xiong H Q, Liu C, Zheng B W. Aircraft flight dynamics. Beijing: Aviation Industry Press, 1990. [6] Ferguson S W. A mathematical model for real time flight simulation of a generic tilt rotor aircraft. NASA CR-166536, 1988 [7] Yanguo S, Huanjin W. Design of Flight Control System for a Small Unmanned Tilt Rotor Aircraft. Chinese Journal of Aeronautics, 2009, 22(3):85-89. [8] Muramatsu E, Ikeda M, Hoshii N. An interpolated controller for stabilization of a plant with variable operating conditions. Automatic Control, IEEE Transactions on, 1999, 44(1): 76-81

Applied Mechanics and Materials Vol. 721 (2015) pp 214-217 © (2015) Trans Tech Publications, Switzerland doi:10.4028/www.scientific.net/AMM.721.214

Submitted: 10.10.2014 Accepted: 21.10.2014

Mathematical Model of Obstacle Avoidance Shortest Time Path for Robot Luling Duana, Pin Wang and Ni Ruanb,* Department of Mathematics and Information Science, Guangxi College of Education, Nanning 530023, China a

b

[email protected], [email protected]

Keywords: Robot obstacle avoidance; Shortest time path; Model.

Abstract. In this paper, we study the mathematical model of obstacle avoidance shortest time path for robot. From origin to specified point, the robot shortest time path is divided into three cases, then we establish three mathematical models , through mathematical software, we get three required times, by comparison, we can get results of optimal path. Introduction In scientific exploration and emergency rescue , we often encounter some dangerous or human are not directly reachable region, these will need to use robots . The robot is the most basic function in complex terrain travel when automatic avoidance obstacle. So in the planar scene of obstacles in the robot starting point, planning by the arrival of the shortest time path of target points, and can greatly improve the working efficiency of the robot. The putting forward of the problem The following is a plane in the scene graph, the origin O(0,0) has a robot, it can only activities in the planar scene range. In the picture there are four different shapes of the obstacle is that a robot cannot collide with the obstacles, the mathematical description of the following Table 1: Table 1 The mathematical description of obstacle NO. Obstacle name

The bottom left vertex coordinates

Other characteristics description

1

Square

(80,60)

Length 150

2

Triangle

(60,300)

On the vertex coordinates(150,435),The lower right vertex coordinates(235,300)

3

Parallelogram

(360,240)

The bottom edge is 140,Vertex coordinates of the upper left(400,330)

4

Triangle

(280,100)

On the vertex coordinates(345,210),The lower right vertex coordinates(410, 100)

In the plane of the scenario in Fig. 1, the robot to walk path consisting of straight line segments and circular arc, reached the barriers and obstacles outside the specified distance of at least more than 10 units of the target point. The robot can't line turn, it turns circular arc path only. The turning path with a straight path by a circular arc tangent component, at the same time, it can also be composed of a circular arc path two or more tangentials, but each arc radius of minimum order is 10 units. In order not to collide with obstacles, also asked the robot walking route and obstacle the nearest distance of 10 units, or a collision will occur, if the collision occurred, robot is unable to complete the walking.

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The maximum speed of the robot to walk straight is v0 = 5 unit / sec. When the robot turns, maximum v0 turning speed is v = v ( ρ ) = , which ρ is turning radius. If more than the speed, the robot 10 − 0.1ρ 2 1+ e will rollover, unable to complete the walk. From the O(0,0) to A(300,300) , Our work is built the mathematical model [1] of the robot obstacle avoidance [2,3,4] in the shortest time path.

Fig.1 The plane of the scenario

Fig. 2 The shortest path

The establishment and solution of the model In one case: As everyone knows, a straight line is the shortest distance between two points. In order to realize the robot walking to A shortest path from the origin of this goal, we first consider the robot is walking to walk in a straight line segment based. Taking into account the need to turn and turn can only go circular route in the walking process, in order to make the total walking path shortest [5], as much as possible to reduce the arc also requires walking. In order to ensure that the robot can be carried out smoothly in the whole process of walking, the robots stay at least 10 units of distance and obstacles. The shortest path based on the target, the radius of the circular arc considered when turning to take minimum turning radius (10 units), in addition to assume that all arcs passing to corner point of the obstacles for the center of a circle. Easy to know from O to A shortest path in Fig. 2. Let l , l j , s i , ρ , d be the total length of the straight path, the j section of straight length, the i section of the arc length, turning radius, the shortest distance between any point on an obstacle and walking path. Because the path is composed of straight line segments and circular arc, we can be equipped with line segments, arcs, then the mathematical model can be established: n

m

i =1

j =1

min l = ∑ s i + ∑ l j

(1)

ρ ≥ 10 s.t. d ≥ 10

(2)

We can use MATLAB software, and draw the shortest path is 471.0372 from the origin O(0,0) to the target point of A , and the total time is 96.01764. According to the results of analysis, speed of the robot during cornering is a function of arc radius, arc radius is big, bigger, the robot turns faster, and in one case with minimum radius of circular arc to find the shortest path problem, it does not apply to solve the problem of the shortest time. So we do not specifically consider it. In two case: Obviously, radius size will affect the robot walking time, so that the robot to walk through the center of the circular arc turning obstacles point in line HP .

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Let Q ( x, y ) be the center of the circular arc, r a arc radius. we connected with the QO , QA respectively, where in the points of two tangencys on the circular arc are ( x1, y1) , ( x 2 , y 2 ) respectively, the lengths are r 1,r 2 respectively. The lengths of QO and QA are r 3,r 4 respectively. Let ( x 3 , y 3 ) be the midpoint coordinates arc chord, ∠α a half arc angle. The specific practices such as shown in Fig.3:

Fig. 3 In two case

Fig. 4 In three case

The objective function: min =

( r1+ r 2 ) 2r • ∠α + 2 v0 1 + e10−0.1r

(3)

The total distance of the robot walking from O to A : l = r 1+ r 2 +2r • ∠α

(4)

The linear HP equation: y − 60 210 − 60 = x − 230 80 − 230

(5)

The radius of the arc length: r = 10 + ( x − 80) 2 + ( y − 210) 2

(6)

The lengths of tangent r 1,r 2 ,QO, QA and r 3,r 4 : r = 1 r = 2 r 3 = r 4 =

x1 + y1 2

2

( x 2 −300) 2 + ( y 2 −300) 2 x2 + y2

(7)

( x − 300) 2 + ( y − 300) 2

The constraint conditions: Through the solution of LINGO [6], we can obtain the used time is 94.22830. In three case: Using the same method, we can assume the arc center of the robot around the corner of obstacle points in line HP , we can see Fig. 4. Through the solution of LINGO, the used time is 99.72198. The shortest time compared by results from the above three kinds of circumstances is 94.22830, that is the situation of robot walking time optimal path is the shortest.

Applied Mechanics and Materials Vol. 721

r 2 + r 2 = r 3 2 1 2 r + r2 2 = r4 2 2 2 ( x 1 − x ) + ( y 1 − y ) = r ( x −300)( x − x ) + ( y −300)( y − y ) = 0 2 2 2 2 ( x 2 − x ) 2 + ( y 2 − y ) 2 = r 2 x 1 ( x 1 − x) + y 1 ( y 1 − y ) = 0 x1 + x 2 x 3 = 2 y1+ y 2 y3= 2 r 5 = ( x 3 − x ) 2 + ( y 3 − y ) 2 r5 ∠α = arccos 5 80 < x < 230 x < 80 1 y 2 > 210

217

(8)

Acknowledgment This work is supported by the Key Project of Guangxi Social Sciences, China (No.gxsk201424), the Education Science fund of the Education Department of Guangxi, China (No.2014JGA268), and Guangxi Office for Education Sciences Planning, China (No.2013C108), and Characteristic Professional Project fund of the Education Department of Guangxi, China (No.GXTSZY277). References [1]

Q.Y.Jiang, J.X.Xie, J.Ye: Mathematical Model (Higher Education Publications, Beijing 2003) (In Chinese)

[2]

L.L.Duan: Journal of Guangxi College of Education, Vol.129 (2014) No.1, p.151-156. (In Chinese)

[3]

Z.M.Sun: Journal of Weifang engineering of Career Academy, Vol. 26 (2013) No. 1, p.89-93. (In Chinese)

[4]

S.C.Liu: Research on obstacle avoidance algorithm for mobile robot(MS.Northeast Petroleum University,China 2011), p.15.

[5]

X.Ch.Wang. p.54-56.

[6]

J.X.Xie, Y.Xue: Optimization modeling with LINDO/LINGO software (Tsinghua University Press, Beijing 2005) (In Chinese).

Journal of Wenzhou Vocational and Technical College,Vol.14(2014) No.1,

Applied Mechanics and Materials Vol. 721 (2015) pp 218-221 © (2015) Trans Tech Publications, Switzerland doi:10.4028/www.scientific.net/AMM.721.218

Submitted: 20.10.2014 Revised: 28.10.2014 Accepted: 01.11.2014

Synchronization control of chaotic systems with different orders Yafang Yanga School of Mathematic and statistics, Tianshui Normal University, Tianshui 741001, China a

[email protected]

Keywords: Uncertain parameters; scaling matrix; adaptive control.

Abstract. The mixed function projective synchronization is proposed in this paper, which includes the full synchronization and the anti-synchronization and so on. We design an effective controller and parameters identification strategy to study the synchronization phenomena between systems with different orders and uncertain parameters. The analytic results are complemented with numerical simulations for two chaotic systems which are the new integer-order system and the fractional-order Chen system, respectively. Several results show the effectiveness of the presented scheme. Introduction Synchronization and control of chaotic systems have been a hot topic research in different areas of science because of its potential applications in epidemic spreading control [1]. Different kinds of synchronization styles have been proposed in chaotic systems. Reseachers [2-3] discussed the modified projective synchronization of chaotic systems. In most studies, the authors discussed the modified projective synchronization of fractional-order chaotic systems via active sliding mode control and so forth [4-6]. Hence, it is necessary to study the synchronization between a response system that specifically has scaling factor matrix and a drive system with a scaling function matrix for different systems. Up to now, there are many studies on the synchronization of chaotic system. Also, many researchers has devoted to study the fractional calculus due to its application, furthermore, many fractional-order systems display chaotic behaviour, for instance, fractional-order Rossler oscillator, fractioanl-order hyperchaotic Lorenz system and fractioanl-order hyperchaotic Chen system and so forth. The synchronization of chaos with two strictly different systems is often encountered in many practical situations, such as in social and biological systems. As far as controlling method concerned, there exist various schemes, but the most commonly used one is feedback control method, of course, every method has its advantages and disadvantages, if we combine the feedback control with other schemes, the better effect will be received. That is to say, it is very interesting to study the mixed function projective synchronization between different systems with different orders. On the one hand, it is not always practical to assume that the system parameters are not fully known in reality situation, so adaptive control is an effective method, we can avoid waste the resources. On the other hand, from the point of view of application, additional unpredictable scaling factors can of course enhance the security [7-8]. Thus, we use the adaptive control advantages to present strategy to realize the mixed function projective synchronization between integer-order and fractional-order chaotic systems in this paper. We intend to demonstrate the validity of the above proposed methods for the mixed function projective synchronization on a integer-order system and the fractional-order Chen systems. Synchronization analysis Consider the following integer-order and fractional-order systems: x = f ( x )

D y = g ( y ) + U ( t , x, y ) , q *

(1) (2)

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where x = ( x1 ,x2 , ,xn )T , y = ( y1 , y2 , , yn )T ∈ R n are the state vectors of the integer-order system (1) and the fractional-order system (2), respectively, u(t, x, y) is contorl inputs. Then we can get the error dynamical system: D*q e1 = β1 D*q y1 − D*q (φ1 (t ) x1 ) q q q D* e2 = β 2 D* y2 − D* (φ2 (t ) x2 ) D q e = β D q y − D qφ (t ) x ), 3 * 3 * 3 3 * 3

(3)

It is said to achieve our main goal, if the following conditions hold: lim e = lim βy − M(t )x = 0, t →∞

t →∞

(4)

Lemma 1. For the fractional-order system (2) with the order as 0 < q ≤ 1 , if and only if there exists a real symmetric positive definite matrix Q such that the condition J = xT QD*q x ≤ 0 always holds for any states x(t ) = ( x1 (t ), x2 (t ),...., xn (t )) , then system (2) is asymptotically stable. Numerical Simulations We take the following new chaotic system as example, and is described by: x1 (t ) = a ( x2 − x1 ) x2 (t ) = x1 x3 − x2 x (t ) = b − x x − cx , 1 2 3 3

(5)

System (5) displays a chaotic attractors as the parameters a = 5, b = 16, c = 1 , is shown in Fig.1.

Fig.1. Three-dimensional plot of the trajectory of the new chaotic system (5) We suppose the parameters of the response system are uncertain. The system (5) is taken as the drive system, then the response system is chosen fractional-order Chen system and described as: D*q y1 (t ) = d ( y2 − y1 ) + u1 q D* y2 (t ) = ( f − d ) y1 − y1 y3 + fy2 + u2 D q y (t ) = y y − hy + u , 1 2 3 3 * 3

(6)

where u1 , u2 , u3 are the controller to be determined. The errors variables is defined as e1 = β1 y1 − φ1 (t ) x1 e2 = β 2 y2 − φ2 (t ) x2 e = β y − φ (t ) x , 3 3 3 3 3

(7)

220

Vehicle, Mechanical and Electrical Engineering

Then the fractional-order error dynamics system reads: D*q e1 = β1 D*q y1 − D*q (φ1 (t ) x1 ) q q q D* e2 = β 2 D* y2 − D* (φ2 (t ) x2 ) D q e = β D q y − D qφ (t ) x ), 3 * 3 * 3 3 * 3

(8)

Theorem 1 If the constant scaling factors β i (i = 1, 2,3) and the scaling functions ki (t), (i =1,2,3) are given, the new synchronization types between systems (5) and (6) will achieve using the following designed appropriate controllers: 1 q ˆ u1 = [ D* (φ1 (t ) x1 ) − e1 ] − d ( y2 − y1 ) β 1 1 q ˆ u2 = [ D* (φ2 (t ) x2 ) − e2 )] − ( fˆ − dˆ ) y1 +y1 y3 − fy 2 β 2 1 q ˆ . u3 = [ D* (φ3 (t ) x3 ) − e3 )] − y1 y2 + hy 3 β 3 ea = β1 ( y2 − y1 )e1 eb = β 3e3 e = − β y e , 3 3 3 c

(9)

(10)

Proof: According to Eqs.(5), (8)~(10), one obtain D *q e1 = β 1 ( d ( y 2 − y1 ) + u1 ) − D *q (φ1 ( t ) x1 ) q q D * e2 = β 2 (( f − d ) y1 − y1 y 3 + fy 2 + u 2 ) − D * (φ 2 ( t ) x 2 ) D q e = β ( y y − hy + u ) − D q (φ ( t ) x ), 3 1 2 3 3 * 3 3 * 3

(11)

Based on (9), (10) with (11), we have

J = e1

e2

e3

ed

ef

D*q e1 q D* e2 Dqe eh *q 3 D* ed Dqe *q f D* eh

(12)

= e1 D*q e1 + e2 D*q e2 + e3 D*q e3 + ed D*q ed + e f D*q e f + eh D*q eh

Thus we can get that e1 D *q e1 + e 2 D *q e 2 + e 3 D *q e 3 + e d D *q e d + e f D *q e f + e h D *q e h = e1 ( β 1 D *q y1 − D *q (φ1 ( t ) x1 )) + e 2 ( β 2 D *q y 2 − D *q (φ 2 ( t ) x 2 )) + e3 ( β 3 D *q y 3 − D *q (φ 3 ( t ) x 3 )) + e d ( β 1 ( y1 − y 2 ) e1 + β 2 y1 e 2 ) + β 2 e f ( y1 + y 2 ) e 2 + e h ( − β 3 y 3 e 3 ) = β 1 e d ( y 2 − y1 ) e1 − e12 + β 2 e 2 [ e f ( y1 + y 2 ) − e d y1 ] − e 22 + β 3 e h y 3 − e 32

(13)

+ e d ( β 1 ( y1 − y 2 ) e1 + β 2 y1 e 2 ) − β 2 e f ( y1 + y 2 ) e 2 + e h ( − β 3 y 3 e3 ) = − e12 − e 22 − e 32 = − e T Ie ≤ 0.

(a) The synchronization error with time evolution between the drive and response systems with scaling matrix β = diag(β1, β2 , β3 ) = diag(1, −1,1) and scaling function matrix M(t) = diag(0.5 + sin(π t ),0.5 + cos(t ), 2 + sin(t ))

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221

(b) The parameters estimation curve for system (6) with above scaling factor and function matrices Fig.2 Scaling factor and function matrix Conclusion This paper proposed a new synchronizaiton patterens, which is called the mixed function projective synchronization. In order to realize our goal, the appropriate controller and the parameters identification strategy are designed for chaotic systems using the fractional-order linear stability theory and the feedback control method. The effectiveness of strategy are validated by numerical simulations.

References [1] X.Wu, J. Li: Chaos control and synchronization of a three species food chain model via Holling functional response, Int. J. Comput. Math. Vol.87(2010),no.2 p.199-214 [2] Z. Li, G. Chen. Global synchronization and asymptotic stability of complex dynamical networks. IEEE Transactions on Circuits and Systems II: Express Briefs, Vol.53 (2006), No.1, p.28-33. [3] A. Yu, A. Pogromsky, partial synchronization theorem. Chaos, Vol.18 (2008) 037107. [4] C.D. Li, Q. Chen, T.W. Huang, Coexistence of anti-phase and complete synchronization in coupled chen system via a single variable, Chaos Solitons Fractals, Vol.38 (2008) No.3, p.461-464. [5] R.Mainieri, J. Rehacek, Projective synchronization in three-dimensional chaotic systems, Physic Review Letter, Vol. 82(1999) No.2, p.3042-3045. [6] C. Li, G.Chen, Synchronization in general complex dynamical networks with coupling delays. Physica A: Statistical Mechanics and its Applications, vol. 343(2004) No.2, p.263-78. [7] X.Y.Wang, X.P., Zhang, Modified projective synchronization of fractional-order chaotic systems via active sliding mode control, Nonlinear Dyn, Vol.69 (2012), p.511-517. [8] R.C. Wu, D.X. Cao, Function projective synchronization of chaotic system via nonlinear adaptive impulsive control, International Journal of Modern Physics C, Vol. 221(2011), p.281-291.

Applied Mechanics and Materials Vol. 721 (2015) pp 222-225 © (2015) Trans Tech Publications, Switzerland doi:10.4028/www.scientific.net/AMM.721.222

Submitted: 20.10.2014 Revised: 27.10.2014 Accepted: 28.10.2014

Research on Carrier-attacker Control before launching Air-ground Guidance Weapons Huiyong Zhang, Lei Zhang, Zeqian Liu*, Yiguo Jia, Weidong Zhang Air Force Aviation University, Changchun 130022, China a

[email protected]

Keywords: Carrier-attacker; motion control; modeling of control law; half material object simulation.

Abstract. Carrier-attacker controls are involved in navigation control and attitude stabilization control. Modes of navigation control and channels of the longitudinal and lateral-directional are analyzed to solve the problem of carrier-attacker controls before launching air-ground guiding weapons. The homing rules design and the control realization of course, pitch, altitude and velocity are studied. At last, the ways and means is approached to experiment and test them by half material object simulation on ground. Introduction In general, the gesture control of carrier-attacker is accomplished by longitudinal and transverse lateral two control channels, longitudinal channel can stabilize and control angle of pitch, height degree, and speed of carrier-attacker. Transverse lateral channel can stabilize and control course angle, angle of inclination and cross track error of carrier-attacker. The navigation control of carrier-attacker before launching air-ground guidance weapons and the stable control of carrier-attacker’ gesture (or altitude) when launching need to manual work anytime and anywhere, this ground-depth simulation is rare in the literatures, This article discusses the method of semi-physical simulation, The semi-physical tests are used in the navigation control of carrier-attacker before launching air-ground guidance weapons and the stable control of carrier-attacker’ gesture (or altitude) when launching, and the simulation results are discussed. Pitch attitude, airspeed and altitude control modeling of carrier-attacker before launching air-ground guidance weapons Maintain and control model of pitch attitude. When launching air-ground guidance weapons, in order to satisfy the normal launch, relaxed conditions and emergency conditions, pitch attitude needs to be stable in a certain range, this is accomplished mainly through to maintain and control the pitch attitude. In the time of pitch attitude maintain /control modeling, dynamics model of carrier-attacker can use the short cycle modal to approximate, Pitching angle velocity signal can obtain pitch attitude signal through an integrator. Maintain and control model of altitude control. The highly controlled carrier aircraft belongs to the center of gravity control. When the aircraft flights at a great circle route, in a complex navigation area or launching guided weapons, it need to maintain high stability that can not be accomplished by controlling the pitch angle which will produce highly drift in vertical air flow. The system including barometric altimeters, radio altimeters and air data sensors changes aircraft flight path by controlling the path angle of inclination basing on height error. Maintain and control model of mach number.When the carrier-attacker flying in the low dynamic pressure, if the speed of carrier-attacker increase ∆ν , in order to keep the carrier-attacker flight direction is constant, we must reduce the attack angle of carrier-attacker, make lift force increment is zero, that is the carrier-attacker must bend to generate negative angle increment, but it might lead to speed continues to increase, thus appeared speed instability phenomenon, using the

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speed control system can ensure that the aircraft flight in the low dynamic pressure situation, make carrier-attacker still with speed stability. In addition, carrier-attacker track control is actually implemented by controlling the angular motion of carrier-attacker, but this premise is that in the control angle motion of carrier-attacker, and flight speed is a constant, When in the low dynamic pressure or motor for a long time, can't guarantee this premise, general air-ground guidance weapons carrier-attacker in the flight phase are working under the low dynamic pressure, speed control system is necessary. Simulation implementation The carrier-attacker automatic navigation control simulation before launching. In the air-ground weapons carried on board, automatic navigation system is mainly composed of inertial navigation, autopilot, heading to contact equipment, etc. The function of inertial navigation is measurement and calculate in automatic navigation, It is mainly based on their own inertia of components constantly measure the aircraft's current location east, north to acceleration component, the actual track Angle and instant location, latitude, and then comparison with the flight plan route, Solution to calculate the drift distance, the plane's flight path Angle error, ground speed deviation from voyage route. To calculate a high-precision dc voltage control signal by the navigation equations, The signal is transferred to the signal transformer of the autopilot after control modulation, amplification of heading contact device, then γ ψ c and ψ c are output by transformer. They are input to the autopilot inclined channel and course channel to enlarge, and through the aileron steering engine and the direction s steering engine of the autopilot control aircraft route correction and rerouted around the corner, to achieve the goal of control aircraft autopilot. System control principle as shown in Fig. 1. γ - γc

γ

γ −Ψ c

Ψ Ψ

Fig. 1 Navigation control principle diagram Inertial automatic navigation use the autopilot control regulation law to control carrier-attacker routes stability and automatic change routes, This compared with the pilot controls the aircraft using the autopilot, it can get the same effect, but also reduces the autopilot stability and manipulate the state transformation, thus improve the reliability of the system and the degree of automation, reduce the workload of pilot. In the process of using the autopilot flight, although the airline's stability is automatic, but divert of the plane must operate by the pilot. In the process of automatic navigation, flight path stability and divert are all automatical.

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Vehicle, Mechanical and Electrical Engineering

Pitch attitudes maintain and control simulation of carrier-attacker before launching. The disturbance of pitch deviation is joined in system simulation computer to simulate carrier-attacker get a disturbance when flying, take carrier-attacker nose-up pitch as an example, in this time itching angle change of carrier-attacker is affected by the vertical gyro in simulation system, and output signal. The angular velocity gyroscope group feels around the horizontal shaft angular velocity and the output signal, At the same time, the signal is input in differential amplifier circuit, and output angular acceleration signals. The three signal added to the comprehensive input port phase of the Phase detector pre-amplifier l at the same time, after enlarge the drive elevator with the integrated signal is proportional to the speed turns down. Under the effect of torque produced by the elevator, the carrier-attacker’s pitching angle is restored to the initial state. In the process of recovery, the pitching angle deviation of carrier-attacker is reduced; the angular velocity and angular acceleration of the carrier-attacker are in the opposite direction with the carrier-attacker’s movement in the interference. The sum of these signals drives the rudder for the initial position. Choose the three parameters of the transmission ratio, can guarantee the stability of carrier-attacker have good performance, and elevator also restored to the initial position when pitching angle restore to its original posture. Stable principle is shown in Fig.2. θ

θ

θ

Fig.2 Pitching angle stability principle block diagram If the carrier-attacker flight by a disturbance, take carrier-attacker nose-up pitch as an example, in this time pitching angle change of carrier-attacker is affected by the vertical gyro in simulation system, and output signal. The angular velocity gyroscope group feels around the horizontal shaft angular velocity and the output signal, At the same time, the signal is input in differential amplifier circuit, and output angular acceleration signals. The three signal added to the comprehensive input port phase of the Phase detector pre-amplifier l at the same time, after enlarge the drive elevator with the integrated signal is proportional to the speed turns down. Under the effect of torque produced by the elevator, the carrier-attacker’s pitching angle is restored to the initial state. In the process of recovery, the pitching angle deviation of carrier-attacker is reduced; the angular velocity and angular acceleration of the carrier-attacker are in the opposite direction with the carrier-attacker’s movement in the interference. The sums of these signals drive the rudder for the initial position. Choose the three parameters of the transmission ratio, can guarantee the stability of carrier-attacker have good performance, and elevator also restored to the initial position when pitching angle restore to its original posture. Flight altitude stability simulation. The principle of stable flight altitude as shown in fig.3.The signal of attitude deviation is joined in system simulation computer to simulate carrier-attacker straight and level flight at schedule attitude, Due to some kind of interference, carrier-attacker deviated from the schedule attitude, if the attitude is reduced, the height difference sensor of simulation system will feel out of the carrier-attacker flight altitude speed deviation and the speed of the altitude change, the altitude signal proportional to the height deviation output by parity generator and lifting speed signal proportional to vertical speed output by tachometer generator are at the same time sent to integrated preamplifier for integrated amplifier, they are enlarged to drive the elevator up, and rotate with the rotate speed proportional to integrated signal, the look up torque is generated to make carrier-attacker upward flight. Due to the carrier-attacker raise its head, pitching angle signals, angular velocity and angular acceleration signals output by vertical gyro and the angular velocity

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gyroscope, these signals not only stop the elevator go to partial, and also try to make the elevator deflection down, make the carrier-attacker looked up slowly. When signal θ , θ , θ keep balance with ,this is U θ + U θ + U θ =U ∆H +U H the elevator back to the middle position ,then the ∆H , H carrier-attacker at a certain pitch continues to climb. When the altitude is about to reach a predetermined altitude, signal ∆H gradually tends to zero, integrated signal reverse (opposite in sign), elevator shift the helm. Then the angle of elevation of the carrier-attacker reduce gradually, when the carrier-attacker arrived in schedule altitude, the elevator back to the middle position, the carrier-attacker will level flying on the scheduled altitude. θ θ θ

∆H H

Fig. 3 The principle of stable flight altitude block diagram Conclusion The effective control for navigation, the pitch attitude /altitude maintained, speed stability of carrier-attacker is the necessary conditions of air-ground guided weapons to accurately hit targets, this paper just studies the control law of carrier-attacker before launching weapons, and the semi-physical simulation approach are discussed in this paper, specific application according to the specific situation, From the view point of engineering, concrete engineering implementation issues into consideration. References [1] Zhang Minlian: Flight control system [M], Beijing: Beijing aviation industry press, Vol.2 (1994). [2] Shen Anyu, Shen Xueren etc.: Automatic flight control system, Beijing: National defence industry press, (2003) p.42-160. [3] Li Xueguo: Active control technology application in carrier-attacker design, Beijing: Beijing aviation industry press, (1995). [4] Zhou Qi-huan: Area navigation overview. International aviation, Vol.10 (1992) p.42-160. [5] Brian L. Stevens, Fram K L. Levis: Aircraft Control and Simulation, Wiley Inter-Science, (1992). [6] Zhou Ziquan: Modern flight simulation technology. Beijing: National defense industry press, Vol.1 (1997).

Applied Mechanics and Materials Vol. 721 (2015) pp 226-229 © (2015) Trans Tech Publications, Switzerland doi:10.4028/www.scientific.net/AMM.721.226

Submitted: 22.10.2014 Accepted: 28.10.2014

Control of Channel Power Compensation Based on Genetic Algorithm to Impairment Aware in Optical Network Dongyan Zhao1, Keping Long1, Yichuan Zheng1, Zhiyuan Du1, Junfei Shi1, Shuo Cheng2 1

University of Science and Technology Beijing (USTB), Institute of Advanced Network Technology and New Services, Beijing 100083, China 2

College of electronic science, Northeast Petroleum University, Heilongjiang 163318, China

Keywords: impairment aware, ROADM, power adjustment, control strategy.

Abstract. In this paper, we address the problem to power impairment in amplified WDM communication networks with ROADM (Reconfigurable Optical Add-Drop Multiplexers), in order to improve the performance of communication systems. In dynamic transmission network, we propose a new control strategy that effectively adjusts signal power in the chain network. The results of simulation show that the proposed control strategy could effectively adjust better channel power and reduce the amount of adjustment to achieve transmission performance. Introduction Optical wavelength division multiplexed (WDM) networks are enabled by technological advances in optical devices such as reconfigurable optical add drop multiplexers (ROADM), erbium-doped optical fiber amplifier (EDFA) and wavelength selective switches (WSS). WDM networks are equipped with these devices for flexible provisioning and resource management based on ASON/WSON(Automatically Switched Optical Network/ Wavelength Switched Optical Network). Nowadays, physical impairment is a hot topic due to the evolution of optical networks to all-optical infrastructures and to the need to maintain or enhance Quality of Service (QoS) by the devices[1,2]. This paper researches optical transmission performance optimization with Genetic algorithm on nonlinear programming algorithm. The research on OSNR optimization and power impairment has been active, which is accomplished by maximizing the channel OSNR. A noncooperative game formulation for the OSNR optimization was presented by L.Pavel [3-5]. Genetic algorithm based on selection, crossover, mutation operator to search, and global search ability is strong, but local search ability is weak [6,7]. This paper combines the advantages of the two algorithms by genetic algorithm for global search in order to obtain the global optimal solution of the problem. Optical link model The optical adjustable device may include the pre-dispersion and dispersion compensator of the source node and the destination node. In the intermediate node, amplifiers can dynamically adjust the attenuation amount of any wavelength, thereby regulating the signal power. The effects of spontaneous emission noise (ASE) produced by optical amplifier can be reflected by OSNR. The OSNR of receiver depends on the optical signal power receiving into the amplifier. Accumulated nonlinear phase shift depends on the power of optical signal into the each fiber. Changing the gain or attenuation of each rode can adjust the optical signal power into each amplifier and the optical signal power into each optical fiber, and then adjust the end-to-end performance indicators (Q factor), which were joint decided by the OSNR of receiver and accumulated nonlinear phase shift. In the single channel or the WDM optical path which has the same transmission path and modulation method, since the factors such as PMD are unrelated to adjustment factors such as power and

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dispersion, the transmission performance indicators which were associated with the ASE, channel power, nonlinear, dispersion compensation and other factors could be expressed as: (1) is the OSNR must reached to ensure the Q factor reaches in back to back optical signal transmission. is the OSNR transmitted to the receiver under the current power configuration. expresses the corresponding OSNR penalty to ensure the Q factor reaches under the combined effects of dispersion, self-phase modulation, cross-phase modulation and four-wave mixing effect for back to back system. Optimization on genetic algorithm Genetic algorithm uses selection, crossover and mutation operator to search. This algorithm has strong global search capability, but the local search capability is weak. This paper combines the advantages of two algorithms. On the one hand the genetic algorithms can conduct the global search. On the other hand the nonlinear programming algorithm can conduct the local search, in order to get the global optimal solution in Fig.1. Population initialization

Fitness value calculation

Select

Cross

Variability

N

End

Y

Termination condition is met?

Evolution times Is a multiple of N?

Fig.1 Genetic algorithms Genetic algorithm is based on the simulation biological genetic and evolutionary process. It is a self-adaption algorithm whose core is to make the fittest survive. Select a certain individual and self-adaption function to calculate, and then decide which to survive according to the probability statistics. In the paper, we set the fitness function: = (2) And So, the purpose of this paper is to obtain a small amount of adjustment, therefore, the smaller the adjustment amount, the larger the fitness value, and the more excellent the individual, the greater probability of being selected. Simulation result A MATLAB simulation was used for the optimization algorithm with each optical amplifier with variable gain. We assume that the link dispersion is compensated at last, so we do not consider the influence of dispersion. All spans in a link have an equal length and all the amplifiers in a link have the same spectral shape and are operated in automatic power control mode. Each optical amplifier adjusts its power towards this goal in the presence of all other channels for a 10 Gbits/s system in the whole network through component specification (engineering rule). Adjustment range is [0,30]dB, the adjustment or compensation is 0.1dB, optical signal power range of the receiver is [-5,0]dBm, and is 10.572dB.

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Fig. 2 Optical transmission performance Figure 2 shows, the longer the transmission distance is, the more it is close to the optimal limit. By the genetic algorithm adjustment, optical transmission performance is better. But the genetic algorithm still need to adjust the on the change of to adapt the optical link transmission. So that adds another layer of complexity to the optimizational process. Conclusion This paper focuses on optimization algorithms in order to compensate power impairment. This paper combines the advantages of genetic algorithms conducting the global search to get the global optimal solution. The results of simulation show that the controller has better dynamic and effective performances for compensation of transmission power at the device nodes. There are several directions for future research. Another topic of research will to analyze other strategies of setting parameters of dynamic power compensation better meeting transmission performance. Acknowledgment The National Natural Science Foundation of China (No. 61302064), the China Postdoctoral Science Foundation (No. 2013M540862) and the youth fund of NEPU (No. 2012QNT04 ky120237). References [1] Asensio, A. Managing transfer-based datacenter connections, Optical Communications and Networking, IEEE/OSA Journal (Volume:6,Issue: 7), July. 2014 [2] Saengudomlert P. Power-aware logical topology selection for IP-over-WDM backbone networks based on per-lightpath power consumption model. Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), 2014 11th International Conference, pp 1-5, May. 2014 [3] Quanyan Zhu , Pavel L. Enabling differentiated services using generalized power control model in optical networks. Communications, IEEE Transactions, pp 2570-2575, September 2009

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[4] Alpcan, Tansu, Pavel L, Stefanovic N. A control theoretic approach to noncooperative game design, Decision and Control, 2009 held jointly with the 2009 28th Chinese Control Conference, pp 8575-8580, Dec. 2009 [5] Alpcan Tansu, Pavel L. Nash equilibrium design and optimization. pp 164-170, May 2009 [6] Giacobello D, Wung J, Pichevar R, Atkins J, Tuning methodology for speech enhancement algorithms using a simulated conversational database and perceptual objective measures, Hands-free Speech Communication and Microphone Arrays (HSCMA), 2014 4th Joint Workshop, pp62-65, May 2014 [7] Chuan Wang, Lei Yu, Jianguo Zhang, Chuan Wang. Study on optimization of radiological worker allocation problem based on nonlinear programming function-fmincon, Mechatronics and Automation (ICMA), 2014 IEEE International Conference., pp 1073-1078, Aug 2014

Applied Mechanics and Materials Vol. 721 (2015) pp 230-233 © (2015) Trans Tech Publications, Switzerland doi:10.4028/www.scientific.net/AMM.721.230

Submitted: 22.10.2014 Accepted: 28.10.2014

Study on Parametric Reverse Modeling Shuangxi Hua School of Mechano-Electronic Engineering, Hubei Radio and TV University, Wuhan 430074, China a

[email protected]

Keywords: Parameterization, Reversing modeling, Reconstruction.

Abstract. Methods about parametric reverse modeling have been studied based on products’ point clouds by comparing model rebuilding processes in different ways, such as collaborative reconstruction based on both reverse engineering software Geomagic and 3D modeling software, reverse and forward hybrid modeling based on Rapidform. It‘s concluded that reverse and forward hybrid modeling based on Rapidform takes more advantages in parametric reverse modeling, It is more rapid , accurate, and closer to the design intent. Introduction Reverse engineering is a kind of technique of establishing the digital models according to the data obtained by measurement of only the product model or physical model, no product definition or drawings. So that, these rebuilt models and features can be applied for process analysis, manufacturing production and processing of product. At present, it is more common way to use related software and technology to complete the reconstruction of 3D model with the point cloud data of products got by using 3D scanning equipment. Surface reconstruction is not only to realize the real sample shape reverse (namely the real original copy), but also need to reverse the intention of product design [1], these rebuilt models can be modified and further innovation designed. This is the new idea of reverse engineering parametric design [2]. Much software can be used to the reverse modeling, such as UG, Pro/E, Catia, Geomagic, Imageware, Rapidform etc. There are also many different surface reconstruction methods because of the different software platform. For example, the reverse engineering software, Geomagic can be used to complete model rebuilding fast and efficiently for art and the medical fields, shoes mould and other product reverse designs by the use of "based on the detection of curvature” or "based on a detection surface contour" overall fitting function , but these rebuilt curved surfaces cannot be modified because of non-parameters; forward modeling software such as UG, Pro/E can realize the parametric modeling, but not directly handle the huge amount of point cloud data, the point cloud data should be processed by other reverse software such Geomagic studio before designing; Rapidform and Catia are kinds of software combining positive and reverse functions. This article will compare several ideas and methods of reverse engineering in order to find out the faster and accurate way for parametric reconstruction. Ideas and Methods of Parametric Reverse Modeling The Products needed to be parametric rebuilt is usually composed of pure free surfaces and regular surfaces such as quadric surfaces and simple free-form surfaces. For these complex models the main rebuilding process is shown in Fig.1.

Fig.1 Parametric Model Rebuilding Process

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Firstly, processing point data should be done by professional reverse software such as Geomagic. It includes point cloud repairing (dark area), denoising, removing characteristics, aligning coordinate system, data sampling and simplifying, and the last and also key thing, encapsulating point cloud processed into triangle surfaces. Secondly, divide triangle surfaces into different regions reasonably according to the physical structure features by reverse software [3]. Thirdly, each region should be reconstructed into different main parametric surfaces. Then, Create connection surfaces between main surfaces rebuilt by last step by using the method of surface transition such as extension, cutting, bridging. These connecting surfaces should meet requirements of the continuity, accuracy, smooth connection between adjacent surfaces. Lastly, deviation analysis can be done in reverse software by comparing the final model with point cloud data or the original design model. Dividing fields and constructing main surfaces are the most complex and critical steps in the reconstruction process. Especially modeling often needs to use a variety of methods. The methods of modeling chosen will be directly related to the modeling efficiency and surface quality; it should need to do a proper balance between the two. Study on Comparison of Methods for Parametric Reconstruction There are two main methods for constructing parametric surfaces: reverse software and modeling software collaborative modeling, reverse and forward hybrid modeling. In this paper, several methods were studied on practice. Collaborative Reconstruction in Reverse Engineering Software and Modeling Software. Two approaches about it has been tried. (1). collaborative modeling with Pro/E and the reverse software Geomagic studio. First, process point cloud data and encapsulate them into triangle surfaces, then complete all surfaces fitting, cut and suture the fitting surfaces by “parametric surface” function. All these should be done with Geomagic studio. Then, import these surfaces into Pro/E to further improve the model and innovative design since Geomagic has the function to send parameters into Pro/E. (2).There is another way for rebuilding models. “Rapid parametric reverse modeling method based on the Geomagic and UG” [4]. Firstly, Geomagc is used to process point cloud data to get grid envelop curve. Triangle and polygon stage are mainly contained in this process. Secondly, the 3D molding software UG is used to construct parametric surface after the grid envelop curve is imported. By comparing the two ways, there both have many problems. For the first one, deviations of the fitting surfaces are large, and it is difficulty to prune and sew abnormal excess surfaces. It is heavy workload and time-consuming and accuracy is not high by 3D modeling software. For the second one, rebuilding is mainly finished by UG according the iges curves got by Geomagic. The process is also complicated and time-consuming for Grid curves are too many. More important problem is that rebuilt models by the above two methods are not definitely of high precision compared with the original designed model or physical models because the rebuilt surfaces are fitted from point cloud data. That depends on point cloud with very high precision, if the point cloud is not highly accurate, the models will have large deviation compared with the original designed models. Deviation mainly comes from three aspects: errors in the manufacturing process, errors in scanning process and models reconstruction process. For example, the two surfaces of the design models are parallel, but the two in actual product are not parallel for manufacture deformation, then the two surfaces fitted by point cloud scanned from the actual product will not parallel, either. Another example, the value of one fillet for the model’ side is constant, but it changes after the product is manufactured, so that also affects the point cloud, then the fillet surface will be fitted to pure free surface without the original parameters, which is different from the original design intention. Reverse and Forward Hybrid Modeling. RapidForm is chosen as the main software to reverse models. First of all, the point cloud data should be processed, and packaged into triangle patches, redundant features need to be removed .Then partition triangle patches based on features

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automatically by software, sometimes need to manually merge, partition. Different fields show different colors to distinguish in this software. If there are regular features in the product, such as cylindrical surface, conical surface, stretching, rotating surface, spherical, sweep surface, reconstruct each feature according to characteristic lines by the positive function of the software, the processing is simple and rapid. Taking the reconstruction of safety hammer model for example, for the elongated handle part safety hammer model, contour line can be obtained by the use of "patch sketch" function, and then use the arc and line to fit the contour lines to get smooth feature lines which can be stretched to the point cloud height into body. Ribs in the middle are rebuilt through "sweeping". Semicircular section line and path line are both obtained according the triangle surfaces and should be redrawed to get smooth line. For the hammer head part, rebuilding can be finished fast by function of "Spin Wizard" in RapidForm. The handle part reconstructed is shown in Fig.2. The above three parametric features can be rebuilt by forward design method fast and accurately, it doesn't need many lines, and can express original design intents.

Fig.2 Sweeping Feature and Stretching Feature Based on Typical Line

Fig. 3 Model Rebuilt by RapidForm To rebuild irregular pure free surfaces in the products, there are two methods. The first one is that we can reconstruct surfaces with the multi-line strings obtained by several cross sections truncating triangular facet model. The other one is using surface patch fitting function. These rebuilt surfaces are accurate smooth. The later method is fast but only used for open surfaces modeling rather than closed surfaces.

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After major surfaces have been done, import them into the 3D modeling software UG by parameter transfer function. Some surfaces should be rebuilt to connect the main surfaces smoothly. The reconstructing result is shown in Fig.3. Through this rebuilding process in the hybrid functional RapidForm, it is concluded that transition surfaces needed to connect main surfaces are considerably less than the previous two methods, so the reconstruction is highly efficient. Summary Through comparison between several methods of parametric model reconstruction, hybrid function reverse modeling can reach original design intent much nearer. The reverse modeling method is flexible; it can get design precision, high efficiency and finish reversing mixed models composed of different types of features. References [1] VARADY T, MARTIN R, COXT J: Reverse Engineering of Geometric Models—an introduction, Computer Aided Design, (1997), No29, p.255-268. [2] RapidForm and Reverse Engineering Concept, CAD/CAM and the Informationization of Manufacturing Industry, (2006), No.10, p.42-44. [3] Jianhong Du, Hongbing Zhang: Reverse Design of Physical Objects with Complex Surface, Machinery Design & Manufactur, (2005), No.12, p.18-19. [4] Enwei Din, Yong Cai, Jin Liang, Xiang Guo, Hao Hu: Rapid parametric reverse modeling based on Geomagie and UG, Mechanical Research & Application, (2012), No.3, p.173-175.

Applied Mechanics and Materials Vol. 721 (2015) pp 234-237 © (2015) Trans Tech Publications, Switzerland doi:10.4028/www.scientific.net/AMM.721.234

Submitted: 26.10.2014 Accepted: 06.11.2014

Chaos Synchronization of Systems via Accelerated Factors Jinping Jiaa School of Mathematics and Statistics, Tianshui Normal University, Tianshui 741001, China a

[email protected]

Keywords: Hyper-chaotic system; accelerated factor; function projective synchronization; the stability of Lyapunov theory.

Abstract. In this paper, chaos synchronization is investigated of a hyper-chaotic system with accelerated factors. An effectiveness controller is designed to realize the function projective synchronization for the hyper-chaotic system. Moreover, this paper improves the synchronization speed by adjusting accelerated factors. From the point of view of secure communication, more unpredictable factors can additionally enhance the security. Theory analysis and numerical simulations are presented to verify that the observer and controller are effective and feasible. Introduction Chaos plays a more and more important role in nonlinear science field. Chaos synchronization has received significant attention in the last few years due to its potential applications [1-4]. Since Pecora and Carroll [5] presented the chaos synchronization method to synchronize two identical chaotic systems with different initial conditions in 1990, many different methods have been reported to investigate chaos synchronization of some types of chaotic (hyperchaotic) attractors, such as linear feedback control method [6], nonlinear feedback control method [7] and so on. There exist many types of synchronization. Projective synchronization attracted lots of attention to study because of its proportionality between the synchronized dynamical states. However, scaling factors is a constant and the time of synchronization is long, so existing methods are somewhat limited. To the best of our knowledge, their researches seldom concerned adjust the speed of synchronization via accelerated factors. The definition of function projective synchronization The drive system and the response system are defined as below x = f ( x)

(1)

y = g ( y ) + u ( x, y )

(2)

Where X = ( x1 , x2 , , xn ) ∈ R are the state vectors, f , g : R → R are differentiable functions. u ( x, y ) is a controller. We define the error vector e = x − Λ (t ) y (3) Where Λ (t ) is a n order diagonal matrix of function, i.e. Λ (t ) = diag (α1 (t ), α 2 (t ),, α n (t ) , where α1 (t ) is a continuously differentiable function with bounded, and α i (t ) ≠ 0 for all t . Definition 1(FPS) for the drive system (1) and the response system (2), it is said that the system (1) and the system (2) are function projectiv synchronization (FPS) if there exists a scaling function e(t ) = 0 matrix Λ (t ) , such that lim t →∞ T

n

n

n

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Function projective synchronization of hyper-chaotic system In this section, a hyper-chaotic system is chosen to show the function projective synchronization behavior and to illustrate the effectiveness of the proposed scheme. Considering the hyper-chaotic system as follows, the governing equations are x = a ( y − x) y = bx + y − xz − h z = xy − cz h = kxz + d sin( wt ).

(4)

Where Y = ( x, y, z, h)T ∈ R 4×1 is the state variable, and a, b, c, k , d , w are parameters, when the parameters a = 10, b = 28, c = 8 / 3, k = 0.1, d = 1, w = 4.31 , the system exhibits hyper-chaotic behavior. For simplicity, we take the hyper-chaotic system (4) as drive system, is described by y1 = a( y2 − y1 ) y 2 = by1 + y2 − y1 y3 − y4 y3 = y1 y2 − cy3 y 4 = ky2 y3 + d sin( wt ).

(5)

Then the response system is given by x1 = a( x2 − x1 ) x = bx + x − x x − x 2 1 2 1 3 4 x3 = x1 x2 − cx3 x4 = kx2 x3 + d sin( wt )

(6)

Based on above analysis, the system (6) can be described by −a b x = AX + f ( X ) = 0 0

0 a 0 0 − x1 x3 1 0 −1 X + x1 x2 0 −c 0 0 0 0 kx1 x2 + d sin( wt )

(7)

We add a controller to sytem (7), so it can be rewritten as: −a b x = AX + f ( X ) + U = 0 0

0 a 0 0 u1 u − x1 x3 1 0 −1 + 2 X+ (8) u3 x1 x2 0 −c 0 0 0 0 kx2 x3 + d sin( wt ) u4 Then, we also add an adjusted accelerated factor σ i (i = 1, 2,3, 4) to system (8), then it can be

depicted as follows: −σ 1 b x = AX + f ( X ) + u = 0 0

a −σ 2 0 0

0 0 −σ 3 0

σ 1 x1 − ax1 0 u1 σ x + x2 − x1 x3 − x4 u2 −1 X + 2 2 + σ 3 x3 − cx3 + x1 x2 u3 0 −σ 4 σ 4 x4 + kx2 x3 + d sin( wt ) u4

(9)

In order to realize the function projective syschronization, the nonlinear controller is designed as follows: 0 −a b 0 u= 0 0 0 0

(t ) y −(σ 1 x1 − ax1 ) 0 σ 1Λ1 (t ) y1 + Λ1 (t ) y1 Λ 1 1 σ σ − ( x + x − x x ) Λ ( t ) y + Λ ( t ) y 0 −1 Λ ( t ) y 2 2 2 1 3 2 2 2 2 + 2 2 X + + 2 (t ) y −(σ 3 x3 − cx3 + x1 x2 ) σ 3 Λ 3 (t ) y3 + Λ 3 (t ) y3 Λ 0 0 3 3 σ σ − ( x + kx x + d sin( wt )) Λ ( t ) y + Λ ( t ) y 0 0 Λ ( t ) y 4 4 2 3 4 4 4 4 4 4 4 0

(10)

Theorem1 If the controller of (10) is proper and σ i > 0(i = 1, 2,3, 4) , then the function projective synchronization between two hyper-chaotic systems is realized via the above controller.

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Proof from the above definition of error signal, so e = X − Λ (t )Y , Then we can obtain that

(10)

(t )Y = AX + f ( X ) + u − Λ (t )Y − Λ (t )Y e = X − Λ (t )Y − Λ

(11)

According to designed controller (10), we can get the following result 0 0 0 −σ 1 σ 1Λ1 y1 + Λ1 y1 Λ1 y1 0 −σ σ Λ y + Λ y Λ y 0 0 2 2 2 e = X + 2 2 2 − 2 2 0 σ 3 Λ 3 y3 + Λ 3 y3 Λ 3 y3 0 −σ 3 0 0 0 −σ 4 0 σ 4 Λ 4 y4 + Λ 4 y 4 Λ 4 y 4 0 0 0 −σ 1 0 −σ 0 0 2 e = e = Pe 0 0 −σ 3 0 0 0 −σ 4 0 According to the linear system theory, if all eigenvalues of matrix P are negative.

(12)

(13)

Numerical simulations We would like to give the numerical simulations to verify effectiveness of the above-designed nonlinear controller. Fig.1 and Fig.2 presents the errors with evolving time t by taking the different accelerated factors p = 1 and p = 10 .

Fig.1 The synchronization errors with evolving time t with p = 1

Fig.2 The synchronization errors with evolving time t with p = 10

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Conclusions In this paper, the function projective synchronization of a hyper-chaotic system with accelerated factors is investigated. Effective controller is designed based on Lyapunov stability theorey. The effectiveness of the controller for the system is demonstrated by numerical simulations. References [1] G. W. Leibniz, Mathematics Schiften, Georg Olms Verlagsbuchhandlung, Hilesheim, 1962. [2] A.L. Chian, F.A. Zorotto, and E.L. Rempel, C. Rogers, “Attractor merging crisis in chaotic business cycles,” Chaos, Solitons and Fractals, Vol. 24 (2005) No.3, p. 869-875. [3] A.L. Chian, E.L. Rempel, C. Rogers, “Complex economic dynamics: chaotic saddle, crisis and intermittency,” Chaos, Solitons and Fractals, Vol. 29 (2006) No.5, p.1194-1218. [4] K. Sasakura: On the dynamic behavior of Schinas’s business cycle model, Journal of Macroeconomics, Vol.16 (1994) No.3, p. 423-444. [5] L.M. Perora, T.L. Carroll: Synchronization in chaotic systems, Phys Rev Lett, Vol.64 (1990), p.821-825. [6] A. Arenas, A. Diaz-Guilera , J. Kurths , Y. Moreno, C. Zhou: Synchronization in complex networks. Phys Rep, Vol.469 (2008) No.2, p.93-153. [7] J.H. Park: Controlling chaotic systems via nonlinear feedback control, Chaos Solitons & Fractals, Vol.23 (2005) No.3, p.1049-54.

Applied Mechanics and Materials Vol. 721 (2015) pp 238-243 © (2015) Trans Tech Publications, Switzerland doi:10.4028/www.scientific.net/AMM.721.238

Submitted: 27.10.2014 Accepted: 01.11.2014

Cooperative Output Regulation for a Class of Nonlinear Uncertain Multi-agent Systems Using the Backstepping Method Dingcai Huanga, Xiangke Wangb, Yifeng Niuc College of Mechatronic Engineering and Automation, National University of Defense Technology, Changsha 410073, China a

[email protected], [email protected], [email protected]

Keywords: Multi-agent systems, output regulation, internal model, backstepping.

Abstract. The cooperative output regulation problem for a class of nonlinear uncertain multi-agent systems is considered. Based on the distributed internal model, the problem is firstly transformed into a global stabilization problem of the augmented system. Then, using the backstepping design method, a distributed control law with its stability analysis is proposed to solve the global stabilization problem of the augmented system. Finally, a numerical simulation is made to show the efficacy of the analytic results. Introduction In recent years, the cooperative control of multi-agent systems has emerged as a hot topic of the current control area, including consensus, formation, flocking, coverage and leader-following coordination [1-5]. The cooperative control has lots of advantages such as greater efficiency and operational capability. On the other hand, the output regulation theory has been widely studied and extended to the large-scale systems and networked systems. For example, Su defined a general formulation about the cooperative output regulation problem of linear multi-agent systems[6]. Wang considered the cooperative output regulation problem of switched linear multi-agent systems with saturation input [7]. However, in most of the existing results, its assumption that all the followers can access to the information of the leader is unrealistic in the practical world. Moreover, the dynamics of multi-agent systems are considered as the first or second-order linear systems. In reality, the systems should be modeled as more complex nonlinear dynamics. Motivated by these, this paper investigates the cooperative output regulation problem of nonlinear uncertain multi-agent systems based on the relative information between the neighbors. Preliminaries and problem statements Directed graph. A directed graph G is defined as G = (N, ε ) ,consisting of a node set N = {0,1, 2,..., N} and a set of edges ε ⊆ N × N [8]. An edge (i, j ) ∈ ε implies that node j can obtain information from node i . The neighbors of node i is defined as Ν i = { j ∈ N | ( j , i ) ∈ ε } . The N graph Laplacian L = [lij ]i , j =1 can be defined as lii = ∑ j =0 α ij , and lij = −aij , for i ≠ j . N

In our multi-agent systems, the dynamic of agent i (i = 1,..., N ) is described by: xi = f i ( xi , yi , v, w), y i = g i ( xi , yi , v, w) + bi ( w)ui , i = 1,..., N ,

(1)

where xi ∈ R and yi , ui ∈R are the states, output and input of the i th agent, respectively; w ∈ R nw is an uncertain constant vector; and v ∈ R nv is the reference input to be tracked or the disturbance to be rejected, which is produced by the leader: v = S ( ρ )v, (2) nxi

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where ρ ∈ R nρ is an constant vector. For i = 1,..., N , the regulated error ei is defined as ei = yi − q(v) , where q (v) is the output of the leader. In practice, only a part of the follower agents are able to access the information of the leader due to the communication constraints, whereas the others cannot. Thus we introduce a virtual regulated error evi , which is defined as

evi = ∑ j =0, j∈N α ij ( yi − y j ) . N

i

Thus, we will consider a distributed control law in the following form: ui = ki (ς i , evi ), ςi = hi (ς i , evi ). (3) Problem definition. Having defined the notations above, the cooperative output regulation problem of nonlinear uncertain multi-agent systems can be stated as follows: Definition 1: For each agent i = 1,..., N , find a distributed control law with form (3) such that the closed-loop composite system (1) and (2) satisfies two properties as follows: Property 1: For all sufficiently small x (0) , v(0) and w , the trajectory col ( x(t ), v(t )) of closed-loop composite system (1) and (2) exists and is bounded for all t ≥ 0 ; Property 2: For all sufficiently small x (0) , v(0) and w , the trajectory col ( x(t ), v(t )) of closed-loop composite system (1) and (2) satisfies: lim ei (t ) = 0. (4) t →∞ To solve the problem, firstly we list some standard assumptions. Assumption 1: There exist sufficiently smooth functions xi (v, w, ρ ) , y i (v, w, ρ ) and u i (v, w, ρ ) satisfying xi (0, 0, 0) = 0 , y i (0, 0, 0) = 0 , ui (0, 0, 0) = 0 , such that for arbitrary v ∈ R nv and col ( w, ρ ) ∈ W × S , ∂xi (v, w, ρ ) S ( ρ ) = fi ( xi (v, w, ρ ), y i (v, w, ρ ), v, w ) , ∂v ∂y i (v, w, ρ ) (5) S ( ρ ) = gi (xi (v, w, ρ ), y i (v, w, ρ ), v, w) + bi ( w)ui ( v, w ) , ∂v 0 = y i (v, w, ρ ) − q (v). Assumption 2: All the eigenvalues of S ( ρ ) are simple with zero real parts for all ρ ∈ S . Assumption 3: The solutions u i (v, w, ρ ), i = 1,..., N are polynomials in v . Assumption 4: Let G is a directed graph which contains a directed spanning tree with node 0 being the root. The subgraph G containing all the followers is bidirected. Remark 1: According to Theorem 3.8 in Ref. [9], Assumption 1 guarantees the output regulation problem of nonlinear uncertain systems can be solvable. Assumptions 2 and 3 guarantee a distributed internal model can be designed for the leader. With Assumption 4, we have lim t →∞ ei (t ) = 0 ⇔ lim t →∞ eiv (t ) = 0 . Moreover, L is positive definite and satisfies Le = LT e = ev . The main results In this section, we present our main results based on the distributed internal model, and construct a distributed control law by using the backstepping method. Distributed internal model. Under Assumption 3, there exists integers ni ( i = 1,..., N ), such that for all col ( w, ρ ) ∈ W × S , u i (v, w, ρ ) satisfies

d ni ui (v, w, ρ ) dui (v, w, ρ ) d ( ni −1)ui (v, w, ρ ) = a ( ρ ) u ( v , w , ρ ) + a ( ρ ) + + a ( ρ ) . 1i i 2i ni i dt ni dt dt ( ni −1)

(6)

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du i (v, w, ρ ) d ( ni −1)u i (v, w, ρ ) ,..., Let τ i (v, w, ρ ) = col ui (v, w, ρ ), , and (Φ i , Γi ) satisfies dt dt ( ni −1) dτ i (v, w, ρ ) = Φ iτ i (v, w, ρ ) and ui (v, w, ρ ) = Γiτ i (v, w, ρ ) . Then we introduce an internal model for dt each subsystem of system (1) as follows: ηi = M iηi + Ni ui , (7) Where ( M i , Ni ), i = 1,..., N is any controllable pair. Thus system (1) and internal model (7) constitute an augmented system. There exists a nonsingular matrix Ti ( ρ ) satisfying the Sylvester equation Ti ( ρ )Φi − M iTi ( ρ ) = Ni Γi . (8) Let θi (v, w, ρ ) = Ti ( ρ )τ i (v, w, ρ ) , then by the following coordinate transformation xi = xi − xi (v, w, ρ ), ηi = ηi − θi (v, w, ρ ) − bi−1 ( w) N i ei , The augmented system can be converted into the following form: xi = fi ( xi , ei , µ ), η = M η + ϕ ( x , e , µ ), i

i i

i

i

i

(9)

(10)

ρ

ei = gi ( xi ,ηi , ei , µ ) + bi ( w)(ui − Ψi ηi ). Lemma 1[9]: If there exists a distributed control law of the form (3) stabilizing the augmented system (10), Then the cooperative output regulation problem can be solved by ui = ki (ς i ,ηi , evi ), ςi = χ i (ς i ,ηi , evi ), (11) ηi = M iηi + N iui , i = 1,..., N . Remark 2: It is noted that the cooperative output regulation problem of system (1) can be converted into the global stabilization problem of the augmented system (10), which can be solved by the distributed control law (11). Denote X i = col ( xi ,ηi ) and X = col ( X 1 ,..., X N ) . According to Ref. [9], there exist some real constant c1i > 0 , c2i > 0 , and some smooth positive functions π1i ( xi ) , π 2i (ei ) , π 3i ( X i ) and π 4i (ei ) , such that, for all µ ∈ Σ , || ϕi ||≤ c1i [π 1i ( xi ) || xi || +π 2 i (ei ) || ei ||], (12)

|| gi ( X i , ei , µ ) ||2 ≤ c2i [π 3i ( X i ) || X i ||2 +π 4i (ei )ei2 ], N N || g ( X , e, µ ) ||2 ≤ c ∑ π 3i ( X i ) || X i ||2 + ∑ π 4i (ei )ei2 , c = max{c21 ,..., c2 N }. i =1 i =1 In addition, there exists a unique, nonsingular matrix P satisfying M iT Pi + PM . i i = −I

(13) (14)

Controller design. In what follows, we will adopt the backstepping method to design a distributed control law, only by using the relative information of the outputs between neighboring agents. Theorem 1: Under Assumptions 1-5, if there exist some positive function ∆ i ( xi ) and some

positive constants li and ri , such that the following conditions hold, (1) ∆i ( xi ) ≥ 1 + π12 ( xi ) + π 22 (ei )φ12 ( xi ) ; (2) li = max{1,8c12i || Pi ||2 } ; (3) r ≥ π 3i ( X i ) + 1 .

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Then the cooperative output regulation problem of system (1) can be solved by 1 c 1 ui = − (π 4 i (ei ) + 1) evi + Ψ iρηi . + 2 bi ( w) 4 λmin ( L )

241

(15)

Proof: Firstly, let ui′ = ui − Ψ iρηi . Then we adopt the backstepping method as follows. Step1: Consider the xi -subsystem of (10) and treat the variable ei as a virtual control input. Assume that there exists a feedback control law ei = φ1 ( xi ) xi stabilizing the xi -subsystem, and a smooth and positive definite Lyapunov function V1i satisfying

∂V V1i = 1 f i ≤ −∆ i ( xi ) || xi ||2 . (16) ∂xi Step 2: Consider the ( xi ,ηi ) -subsystem and treat the variable ei as a virtual control input. Choose a smooth and positive definite function V2i ( xi ,ηi ) as a candidate Lyapunov function, which is defined as V2i ( xi ,ηi ) = liV1i ( xi ) + 2ηiT Piηi . Then the derivation of V2i is V = lV ( x ) + 2η T ( M T P + P M )η + 2ϕ T Pη + 2ηT Pϕ 2i

i 1i

i

i

i

i

i

i

i

i

i i

i

i

i

≤ −li ∆ i ( xi ) || xi ||2 −2 || ηi ||2 +8c12i || Pi ||2 π 12i || xi ||2 +π 22i ei 2 + ηi2 ≤ −{li ∆ i ( xi ) − 8c12i || Pi ||2 π 12i − 8c12i || Pi ||2 π 22iφ12 ( xi )} || xi ||2 − || ηi ||2 .

(17)

If we choose ∆i ( xi ) ≥ 1 + π 12 + π 22φ12 ( xi ) and li = max{1,8c12i || Pi ||2 } , then

(18) V2i ≤ − || xi ||2 − || ηi ||2 = − || X i ||2 . Consider the X -subsystem, and choose the smooth and positive definite function V ( X ) as the N

candidate Lyapunov function, which is defined as V ( X ) = ∑ V2i ( X i ) . Then i =1

N

N

i =1

i =1

V ( X ) = ∑ V2i ( X i ) ≤ − ∑ || X i ||2 = − || X ||2 .

(19)

Step3: Consider the augmented system (10) and treat the variable u ′ as a control input. Now choose a smooth and positive definite function V ( X , e) as a candidate Lyapunov function, which is defined as V ( X , e) = rV2 ( X ) +

1 T e Le . Then the derivation of V ( X , e) is 2

N

V = rV2 + evT g + ∑ biui′evi i =1

N c 1 ≤ rV2 + ev 2 + || g ( X , e, µ ) ||2 + ∑ biui′evi 4 c i =1 N

N

N N N c ≤ ∑ r || X i ||2 + ∑ evi 2 + ∑ π 3i || X i ||2 + ∑ π 4i ei2 + ∑ biui′evi i =1 i =1 4 i =1 i =1 i =1 N N N N c = ∑ [r − π 3i ] || X i ||2 + ∑ evi 2 + ∑ π 4i ei2 + ∑ biui′evi . i =1 i =1 4 i =1 i =1

Now if we choose r ≥ π 3i ( X i ) + 1 , ui′ = −

1 c π 4i (ei ) + 1 + evi , then we can have bi ( w) 4 λmin ( L2 )

(20)

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N N N N c π +1 c V ≤ −∑ || X i ||2 + ∑ evi2 + ∑ π 4i ei2 − ∑ + 4i 2 evi2 λmin ( L ) i =1 i =1 4 i =1 i =1 4 N

N

N

= −∑ || X i ||2 + ∑ π 4i ei2 − ∑ i =1

i =1

N

N

i =1

i =1

i =1

1 (π 4i + 1)evi2 2 λmin ( L )

(21)

≤ −∑ || X i ||2 − ∑ ei2 = − || X ||2 − || e ||2 Thus the proof is completed. Simulation results In this section, we provide an example to illustrate our design. Consider a group of five van der Pol oscillators as follows[10]: x1i = x2i

x2i = − a1i x1i + a2i x2i − a2 i x12i x2i + bi ui

(22)

yi = x2i

ei = yi − v1 , i = 1,...,5 Where a1i > 0 , a2i > 0 and bi > 0 are some constant parameters, v1 is generated by the leader system v1 = ρ v2 , v2 = − ρ v1 . Let ai col ( a1i , a2i , bi ) = ai + ∆ai , where ai and ∆ai denote the nominal value and the perturbed values of ai , respectively. The topology graph is shown in Fig. 1.

Fig. 1 The network topology The unique solution of the Sylvester equation (8) is given by Ψ iρ = [4 − 9 ρ 4 12 13 − 10 ρ 2 6] . Assume that ai = [1,1,1]T , ρ = 0.8 , v(0) = [0, 2]T , ∆a1 = [0.2, −0.1, 0.01]T , ∆a2 = [0.1, 0.1, −0.02]T , ∆a3 = [ −0.03, −0.05, 0]T , ∆a4 = [ −0.05, 0.08, 0.05]T and ∆a5 = [0.02, 0.1, −0.06]T . We can design the

distributed control law of the form (15) with λmin ( L2 ) = 0.1073 , c = 1 , and π i (evi ) = 10(evi4 + 1) . 3 leader 0 agent 1 agent 2 agent 3 agent 4 agent 5

2

output

1 0 -1 -2 -3 0

10

20

30 time(s)

40

Fig. 2 Tracking performance of yi

50

60

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2 agent 1 agent 2 agent 3 agent 4 agent 5

regulated error

1

0

-1

-2 0

20

40

60

80

100

time(s)

Fig. 3 The regulated error ei (t ) Conclusion In this paper, the cooperative output regulation problem of nonlinear uncertain multi-agent systems has been considered. Based on the internal model, a distributed control law has been proposed by using the backstepping technique for tracking trajectory and rejecting disturbance. In future work, our design scheme will be extended to the case when there exist some unknown parameters in the leader. Acknowledgments This work is supported in part by the Research Project of National University of Defense Technology. References [1] X. Wang, T. Liu and J. Qin, Second-order Consensus with Unknown Dynamics via Cyclic-small-gain Method, IET Control Theory Application, 2012, 6(18), 2748-2756. [2] X. Wang, C. Yu and Z. Lin, A Dual Quaternion Solution to Attitude and Position Control for Rigid-body Coordination, IEEE Transactions on Robotics, 2012, 28(5), 1162-1170. [3] H. Shi, L. Wang and T. Chu, Flocking of multi-agent systems with a dynamic virtual leader, International Journal of Control, 2009, 82(1), 43-58. [4] Y. Stergiopoulos and A. Tzes, Spatially distributed area coverage optimisation in mobile robotic networks with arbitrary convex anisotropic patterns, Automatica, 2013, 49(1), 232-237. [5] X. Wang, J. Qin and C. Yu, ISS Method for Coordination Control of Nonlinear Dynamical Agents under Directed Topology, IEEE Transactions on Cybernetics, 2014, 44(10), 1832-1845. [6] Y. Su and J. Huang, Cooperative output regulation of a linear multi-agent system, 30th IASTED Conference on Modelling, Identification, and Control, AsiaMIC 2010, November 24, 2010 November 26, 2010, Phuket, Thailand, Year, 228-233. [7] X. Wang, W. Ni and J. Yang, Distributed output regulation of switching multi-agent systems subject to input saturation, 10th World Congress on Intelligent Control and Automation, WCICA 2012, July 6, 2012 - July 8, 2012, Beijing, China, Year, 840-845. [8] C. Godsil and G. Royle, Algebraic graph theory: Graduate Texts in Mathematics, New York, Springer, 2001. [9] J. Huang, Nonlinear Output Regulation: Theory and Applications, Philadelphia, USA, SIAM, 2004. [10] M. A. Barron and S. Mihir, Synchronization of Four Coupled van de Pol Oscillators, Nonlinear Dynamics, 2009, 56(4), 357-367.

Applied Mechanics and Materials Vol. 721 (2015) pp 244-248 © (2015) Trans Tech Publications, Switzerland doi:10.4028/www.scientific.net/AMM.721.244

Submitted: 27.10.2014 Accepted: 01.11.2014

Simulation of Valve Position Feedback Mechanism Based on Pro/E and ADAMS Youjun Fan a, Fei Li b and Huatian Zhao c School of Mechanical Engineering, Shanghai Institute of Technology, Shanghai 201418, China. a

[email protected], [email protected], [email protected]

Keywords: Feedback Mechanism, Pro/E, ADAMS, Simulation.

Abstract. In traditional valve position feedback mechanism design, it is tested repeatedly and improvement after processing prototype, the process is complex and workload. Using Pro/E and ADAMS, the overall mechanical structure of the valve position feedback mechanism for joint simulation, and an analysis of the kinematics and dynamics model, simplified the design process of the repeated calculation, get the relationship of stem displacement- angle between gear, gear meshing force and motion state of the stem, the simulation value compared with the theoretical value, tallies with the data and shows that the simulation is reasonable. Introduction In the industrial flow control process, the valve positioning control indices such as accuracy, real-time, speed and flexibility of the higher and higher demands are proposed, and the demand for intelligent valve positioner is more urgent [1]. Valve position feedback mechanism is a smart valve positioner major component, which is an effective way to achieve precise control of gas flow and determines the accuracy of the smart valve positioner machine control precision. As the valve position feedback mechanism is a complex electromechanical system, if you follow the traditional design patterns, which is tested and improved repeatedly by working prototype. It will be not only difficult to effectively improve the performance of the valve position feedback mechanism, but also spend a lot of time and materials. So it is necessary to make use of virtual technology. Before physical prototypes, using Pro / E and Adams software to establish a co-simulation system at first, it can simulate and analyze on the kinematics and dynamics of the valve position feedback mechanism, and also improve the working process of the valve position feedback mechanism. Working principle Fig.1 shows the valve position feedback mechanism driving schematic, and the working principle can be stated as follows: When the valve pneumatic actuator 12 works, the valve stem 9 will move under the driving gas pressure, while under the influence of the clamp assembly 11, the detection lever 10 moves together with the stem 9. The intelligent valve positioner 6 is fastened to the bracket of pneumatic actuators, when detecting lever 10 is moved, it will drive the roller 8 moving vertically in the detection lever10, the roller 8 is fixed to the feedback lever 7, the other end fixed to the axle of the big gear 4.The roller moving on the detection lever will drive the feedback lever rotated relative to the positioner, then the big gear case is deflected, causing the pinion gear 3 rotated, so that the deflection is amplified. The angular displacement sensor 5 of the valve position measures accurately the deflection and reconverts to an electric signal transmitted to the control unit 2 to complete the rotation of the valve stem moving to potentiometer conversion.

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Fig.1 Valve position feedback mechanism drive schematics

245

Fig.2 Valve position feedback mechanism model on ADAMS

Modeling of 3D solid on Pro/E Using Pro / E parametric modeling function, the various parts of the feedback linkage are modeled and assembled. After the completion of modeling using Pro/E interference analysis function for interference check. It avoids that when mechanism has some problem after model into ADAMS, it must be returned to Pro / E revising duplication of work parameters. Modeling of simulation on ADAMS Due to the use of Pro / E and ADAMS software version is higher, using conventional dedicated module interface software Mechanism / Pro cannot be fully converted data, and therefore it can be used to generate a solid model file format to create a universal model ADAMS. That is generated parasolid format solid model files, which is an extension of the parabolic (*.x_t). Modifying imported models (1) Integrity test model When 3D model into ADAMS, due to a gap in the data for each software conversion process, resulting in a Pro / E as an integral component after importing into several separate components, hence the Boolean operations are needed to reconstruct a member. Meanwhile, in order to improve the ADAMS simulation results and computational efficiency, it’s need to remove some parts which does not affect or less affect for the simulation, such as locating housing, actuating cylinder, bolts and so on. (2) Material selection In this mechanism, the stent of pneumatic actuator is defined as cast (Cast-iron). Since the friction state detecting lever is long, the detection lever and the roller are defined as stainless steel (Stainless), the other part is defined as the steel material (Steel). Meanwhile, in order to facilitate the calculation, assuming that all the parts are rigid, it will not consider the actual precision of the parts and assembly errors, and the link mechanism ADAMS finished model is shown in Fig.2. Adding constraints and load (1) Determine the constraint relations According to the above works kinematic relations, it is defined as the relationship between the various constraints of the system, the specific constraint relationships as shown in Tab.1. Tab.1 Constraint relations of valve position feedback Joints Body1 Body2 Constraint relations Joint_1 Big Gear Feedback Lever Fixed Joint Joint_2 Feedback Lever Roller Fixed Joint Joint_3 Clamp Assembly 1 Detection Lever Fixed Joint Joint_4 Detection Lever Clamp Assembly 2 Fixed Joint Joint_5 Clamp Assembly 2 Valve Stem Fixed Joint Joint_6 Pneumatic Actuators Ground Fixed Joint Joint_7 Big Gear Ground Revolute Joint Joint_8 Valve Stem Pneumatic Actuators Translation Joint

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Joint_9 Roller Detection Lever Inline Primitive Joint Joint_10 Pinion Gear Ground Revolute Joint Joint_11 Pinion Gear Potentiometer Fixed Joint Gear_1 Big Gear Pinion Gear Gear (2) Gears contact force parameter selection There are two method mainly of calculating the contact force in ADAMS / View: one is restitution; the other is the impact. Calculating from the principles of the two contact force, the relevant parameters set of restitution method is difficult, so we use the impact method to calculate the contact force in simulation. Contact force is defined as in MSC.ADAMS [3]: (1) Where q0 is the initial distance between two objects, q is the actual distance in object collision course; q0-q is the deformation during collisions. The formula, when q≥q0, the two objects do not collide, their collision force is zero; When q L ⋅ n 0 n In the type, T ( L, n) represents the demand torque, L is torque load coefficient, Tmax is motor peak torque, n0 is motor rated speed, n is current speed of the motor. From the above, the slope starting torque Tt is equal to the anti-slip torque Ts and the demand torque corresponding to the accelerator aperture T ( L, n) , such as equation (17):

Tt = Ts + T ( L, n )

(17)

Offline Simulation According to the slope starting control process, the simulation model has built using MATLAB /Simulink to verify control strategy by offline simulation. Firstly, it is the simulation of slipping in a slope. For the simulation of slipping in a slope, the accelerator pedal and the brake pedal are fully released. Slope selects 8% and 15% two slopes representing a small slope and a big slope. Figure 4 and Figure 5 respectively represent the simulation results of slipping in 8% and 15% slopes.

Fig.4 The results in the 8% slope Fig.5 The results in the 15% slope The above results show that: In the 8% slope road, vehicle controller predicts the current slope is 8% according to the slipping acceleration and other parameters, and there is little error for the reason that the prediction model is based on theoretical calculation. After slope forecasting, the vehicle controller sends anti-slip torque command using PID control in order to keep the vehicle speed 0km/h in 8% slope. In the 15% slope road, the control process is similar. Finally, the anti-slip control keep the vehicle slipping speed at around 4km/h, and achieves the control effect slipping at a low speed in a big slope, ensures driving safety when slipping. Secondly, it is the simulation of starting in a slope. For the simulation of slipping in a slope, the accelerator pedal and the brake pedal are fully released and the function of anti-slip is shut off at first.

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And then turn on the function of anti-slip in the third second, finally operate the accelerator pedal In the ten second. Figure 6 represents the simulation results of starting in a 15% slope.

Fig.6 Simulation results of starting in a 15% slope The above results show that: At first, the slipping speed is increased to 17km/h for the reason that the anti-slip function is shut off. And then the slipping speed is reduced to 4km/h and maintained when the anti-slip function is turn on. In the ten second, the accelerator pedal is operated, the anti-slip torque is 78Nm, which is equal to anti-slip torque and demand torque corresponding to accelerator aperture. In sum, the control effect is verified. Conclusion This slope starting strategy not only can quickly and accurately predict the current road slope, also can control the anti-slip torque to ensure the traffic safety, and keep the driving feeling the same as the flat road starting. References [1] S.J. Chen, D.T. Qin, M.H. Hu, H.B. Wei: Journal of Chongqing University, Vol. 35 (2012) No.9, p.1. [2] R. Chen, D.Y. Sun: Proceedings of the Second International Symposium on Intelligent Information Technology Application (Shanghai, China, Dec. 20-22, 2008). Vol. 2, p.712. [3] K.H. Ang, G. Chong, Y. Li: IEEE Transactions on Control Systems Technology, Vol. 13 (2005) No.4, p.559. [4] L.F. Wang, J.Z. Zhang, X.H. Wen: Seoul 2000 FISITA World Automotive Congress (Seoul, Korea, June 12-15, 2000).

Applied Mechanics and Materials Vol. 721 (2015) pp 322-325 © (2015) Trans Tech Publications, Switzerland doi:10.4028/www.scientific.net/AMM.721.322

Submitted: 22.10.2014 Accepted: 28.10.2014

Research on the Design and Manufacture of Steam Cleaning Machine based on PLC Demin Zhang1, a, Shibo Li2, b 1

School of Electrical Engineering Tianjin University of Technology, Tianjin 300384, China

2

Tianjin Key Laboratory for Control Theory & Applications in Complicated System, Tianjin 300384, China a

b

[email protected], [email protected]

Keywords: PLC, steam cleaning machine, automation equipment

Abstract. In this paper, the programmable logic controller (PLC) produced the steam cleaning machine controller based on PLC and foundation design, and steam cleaning device controlled the process of the realization .The programmable logic controller PLC programming and logic design, system realizes according to different artifact types, complete cleaning, blowing, drying the corresponding action. This system after careful design, manufacturing and detailed inspection, and after the running test, the experimental results shows that: as long as the proper maintenance and proper use of equipment, the project can improve the efficiency to a great extent. Introduction SCP technology process SCP is abbreviation of Steam Cleaning Process, mean the steam cleaning process. Existing parts cleaning technology are mainly manual cleaning, chemical cleaning, mechanical cleaning and so on, poor security, inefficiency, high-temperature steam cleaning have beneficial to cleaning and reduce greasy dirt oil adhesion.[1] The main factors influencing the cleaning efficiency is that: F mean Nozzle shot force; ρω mean Water density; Q mean clean water flow; Pd mean Nozzle outlet pressure: F = Pd × 2 / ρω × ρω × Q (1) From the type (1) is considerable, Water jet cleaning spray force is proportional to the flow. Steam valve flow to choose the proper or not will affect the cleaning of the fast speed, Thus influence on the cleaning efficiency.[2] Generally think of shock pressure is in 500 KPA to 800 KPA, scour water yield in30~150L/㎡Can achieve a better cleaning effect. SCP control plan. PLC Widely used in automatic control of various kinds of mechanical equipment, to efficient production gradually replace manual operation. In most parts of manufacturing enterprise, parts or the work piece cleaning production is indispensable in the technology process.[3] But many factories still continue manual cleaning, the production efficiency is low and cleaning efficiency is poor. This article is based on PLC was designed automatic SCP steam cleaning equipment, Can be realized according to the different kinds of artifacts, To complete the function like corresponding cleaning, blowing, baking and so on. The function of the steam cleaner and structure

Steam cleaning machine with PLC as control core, analysis, design, manufacturing a full set of steam cleaning machine equipment, integrates, including electrical control and mechanical control, a variety of control mode. [4] Will edit debugging good input of PLC control program, can complete the logic control of input and output of the PLC memory, using the powerful logic control function of PLC to realize the corresponding cleaning, drying, blowing, etc. Figure 1 as the structure diagram of the control system, Send instruction to PLC control panel, By PLC output signal to the rotating

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mechanism, display institutions, Shielding gate institutions, cleaning institutions, Blowing institutions and dry institutions. [5] On the other hand, the advantages of various detecting element signal back to the PLC, output to the control panel is Convenient to man-machine interaction.

Fig.1 The control system structure diagram Among them, the rotating mechanism is controlled by PLC output signal of the motor rotation, and then, drive into the work piece rotation. Its purpose is through rotating wash in order to achieve better effect. Posing mechanism and the platform screen door is controlled by PLC output of solenoid valve, Powered by cylinder pneumatic device. By PLC, cleaning mechanism output signal to control the steam valve opening to regulate the flow of steam. [6] By adjusting the opening of the air valve, blowing institutions to control the flow of air. Drying mechanism is controlled by PLC output signal valve signal to control the switch of hot air drying. Steam cleaning machine working principle as shown in figure 2, By PLC control air and steam flow, The box to complete the cleaning after rotation, blowing air, dry clean again by exhaust all the institutions and waste discharge process. [7]System has four groups of motor, respectively for the pump motor, rotating machine, hot air and exhaust fan motor, through the intermediate relay connected to the control circuit as the output link, Form a complete set of drive system, the output signal to PLC to control the motor start-stop control relay for the FRP. Another five electromagnetic valve respectively open closed solenoid valve, solenoid valve, posing as a pendulum back steam solenoid valve solenoid valve, solenoid valve, air dry, respectively in the control system of pneumatic parts.

Fig.2 Steam cleaning machine working principle diagram

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The hardware design of a steam cleaner We select MITSUMISHI A1SJHCPU type PLC CPU as the system core, A1SX42 - S2 as input of PLC module, A1SY42P as PLC output module, Q62P for its power supply, the system fully meet all the requirements of the steam cleaner, And has good expansibility. The steam cleaning machines of the PLC have 47 inputs X and 41 output Y. External button control cabinet design According to the requirement of the system we designed a manual button on the control cabinet for easy user operations, the main button and its significance is as follows: Power: 220 v lights Revolve prepare: Press after running to the light, cleaning machine operation ready. Full reset: The cleaning machine all actions are in situ place, Prepare into the work piece. Buzzer: When there is an error, Buzzer will alarm, such fault as a rotary motor not turn etc Manual/automatic: when manually, can operate cleaner single-step action, Such as open or close doors, etc. Run automatically: Automatic state, Press the run automatically, cleaning machine into the automatic state. Our system control can be divided into two types of manual and automatic way to work. Manual mode: According to the full reset all at once, three color light yellow light flashing, until the restoration is completed, all state back to the initial value. If all goes well, after the full reset, the green light will be flashing.[8] If the yellow light continues flashing, that is still at the reset process. Industrial control box on the appropriate switches buttons, cleaning machine will make the corresponding action, used to checking the washing machine to perform any action. Automatic mode: Under the current condition, Press the reset, perform the reset function, Yellow light flashing, Reset at the end of the yellow light to stop flashing. [9]After Toggle to the automatic position (prepare lights if you don't, please click run to prepare), click on run automatically. To automatic switching status: The manual/automatic button in the automatic state to prepare for the light (if there is no light to button), then click the automatic start, the corresponding start the light automatically. Get ready to put into the pressure machine and complete good work piece. After the work piece in place, to start the toggle switch, Cleaning machine, automatically steam cleaning, blowing, baking, Time arrived, pneumatic door open, and the pose of work piece, remove the artifacts, System to complete a cycle. Then wait for the next process of work piece. The advantages of the software design of steam According to the need, we can design a state of chain and chain. State of chain used in the loop of the system to keep the system has been running [10]. The chain is under the condition of the system has not yet been applied to action at the beginning of the run. When start the switches are fed into the work piece cleaning box and automatically shut down the box door, and then steam cleaning artifacts, According to the different types of work piece cleaning requirements have been in the program input parameters, the corresponding steam time Blowing time parameter and hot air drying time parameters. According to those above of the steam cleaner process. Write the corresponding PLC logic control program, Written after the input to the PLC logic control method of the internal can complete we need [11]. SCP steam cleaner using sequential control program to a great extent improve the quickness and accuracy of the system [12].

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Conclusion: This article discussed the steam cleaning machine based on PLC of design and manufacture, and the control circuit and defines the input and output of logic controller made in-depth analysis and summary, especially specific cleaning machine of the control cabinet panel design are introduced. This paper discusses the steam cleaning machine based on PLC of design concept and the basic composition. Reflects the use of PLC is convenient, simple programming, Strong function, high cost performance, Hardware complete, Strong adaptability, high reliability, strong anti-interference ability, The system design, installation, debugging, less workload, Maintenance is convenient wait for a characteristic and advantage. References [1] XU Liang-xiong, “The Electrical Control System PLC Transformation of the XA6132 Milling,” International Journal of Plant Engineering and Management, Vol.18 (2013) No.4, pp.249. [2] ZHU Shao-ying, XU Yu, “The PLC Control System of Vacuum Resin Shot Dosing Equipment,” International Journal of Plant Engineering and Management, Vol.8 (2003) No.3, pp.149-153. [3] C.X.Li and B.Q.Li, “The application of PLC to motor of pendent an assembly line”. China Mechanical Engineering. Vol.5 (1994) No.5, pp.38-40.(In Chinese) [4] Aijun Xu, “Principle and design of intelligent measuring control instrument,” Beijing University of aeronautics and astronautics press, Vol.127 (2004). [5] W.Cai and Y.F.Ju, “PLC distributed control system,” Journal of Xi’an Highway University, Vol.16 (2006) No.3, pp.140-143. (In Chinese) [6] Kambezidis H D, Vera D-P, Adamopoulos A D, “Radiative transfer.Ⅰ. Atmospheric transmission monitoring with modeling and ground-based multispectral measurements,” App Opt, Vol.36 (1997) No.27, pp.6976-6982. [7] Kindel B C, Qu Z, Goetz A F H, “Direct solar spectral irradiance and transmittance measurements from 350 to 2500 nm,” App Opt, Vol.40(2001) No.21, pp.3483-3494. [8] Yifei Wu, Sheng Li, Hua Cai, “Design and implementation of pan- tilt control system based on MSP430 MCU,” Microcomputer Information, Vol.22(2006) No.7, pp.90-93. [9] Chien, Min Lee, “Power-efficient coded modulation for wireless infrared communication,” University Of California, (1998). [10] Ying Ding, “Study and implement of the digital GFSK modulation and demodulation,” Electronic Test (2010) No.10, pp.52-55. [11] G.Z.Xu. J.H.Zhou and Q.G.Li, “Automatic substation monitoring system based on PLC,” Journal of Tsinghua University (Sei&Tech), Vol.38 (1998) No.4, pp.82-85. [12] C.Q.Qi, “PLC technology and application,”Beijing: Mechanical Industrial Press, (2000) (In Chinese).

Applied Mechanics and Materials Vol. 721 (2015) pp 326-329 © (2015) Trans Tech Publications, Switzerland doi:10.4028/www.scientific.net/AMM.721.326

Submitted: 27.10.2014 Accepted: 01.11.2014

The Design Method for Sequence Function Chart in Drilling Machine Control System Sha Jina Liaoning Petrochemical College of Vocational and Technology, Jinzhou 121001, China a

[email protected]

Keywords: Sequence function char, Programmable controller, the initial activation.

Abstract. The design method of sequence function char is used in sequential control. Programmable controller also set Sequential control instruction for programming. It is expounded in this article about adopt the method of sequence function char in programming and skill. As a case study of deep hole drilling machine automatic processing, Sequence function char describes the control is mainly introduced the initial activation, stop, memory and processing method of overload protection, such as. Introduction Programmable controller using sequence function chart for programming. First according to the request of electricity control in a sequence function chart describe the working. Second sequence function diagram can be converted to standard ladder diagram and added the necessary control instruction according to control needs. Last write complete control program. The writing is specification, although the design is complex. Sequence function chart according to the steps describe control with intuitive, accurate and readable. Engineering practice in sequence function chart into control program, eventually to achieve a safe and reliable control, also need to science set up the diagram, reasonable selection units, and to supplement. These are the difficulties for designer in using sequence function chart programming . Sequence function chart to Program design Sequence function chart consists of a step, directed connection, transformation, conditions and tasks. It described control and each perform specific tasks by graphic and arrow. Tasks can work when step at activated. The activation state as a token run in the sequence function chart. The token reaches step it to the matching task is executed. When the next step is activated, the step on the closure and it matching task has been terminated. The structure of the sequence function chart has a single sequence, selection and parallel sequences. Sequence function chart describes the control using a single or composite structures. Sequence function chart describes the control. The focus of the program design is sequence function chart correctly describe workflow, task and control needs. Here the deep hole drilling machine as the control object, Siemens S7-200 Programmable controller as the controller. Introduce the focus of the sequential function chart design and the processing method. The structure of the deep hole drilling machine. Deep hole drilling machine working schematic diagram is shown in figure 1. SQ1 is origin of proximity switch.SQ2 is work into proximity switch. SQ3 is fast into proximity switch. SQ4 is destination proximity switch. SQ5 is reset proximity switch. A,B,C,D,E is for block iron.

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Fig. 1 Deep hole drilling machine working schematic diagram The deep hole drilling machine working. The power head fast forward after boot device. When B for SQ2 close to drill into processing. When machining certain depth drill bit quick exit. When C and SQ3 near bit too fast forward again. After close to B for SQ2 began to the second paragraph. When the depth of processing to drill out quickly. After multiple loop processing, Bit fast rewind when D near SQ4. Electromagnet YA get electricity at the same time, it makes B back in situ. Electromagnet YA lose electricity when SQ5 and E near. Then power head back in situ, the processing is over. The Programmable controller I/O address allocation table as shown below. Table 1 I/O address allocation table Input SB2 SB1 SQ1 SQ2 SQ3 SQ4 SQ5 FR1

comment start button emergency button In situ work-feeding fast forward end of processing reset switch M1 thermal relay

Input Address I0.0 I0.1 I0.2 I0.3 I0.4 I0.5 I0.6 I0.7

Output KM1 KM2 KM3 YA1

comment M2 Forward M2 backward M1 contactor electromagnet

Output Address Q0.0 Q0.1 Q0.2 Q0.3

Sequence function chart. According to the deep hole drilling machine working and control needs, the sequence function chart is drawn. It is for single sequence framework. It contains two loops and a choice structure. As shown in figure 2. Improved sequential function chart According to the standard format, Sequence function chart convert into ladder diagram program. Then the programming to the programmable controller, it can carry out control. These cannot meet the needs in the complicated industrial environment. Such as: Prevent wrong and emergency stop, after power recovery, running protection. To add the necessary control procedures and instructions to adapt to the industrial control needs. Complete the control task need to experience design method combined with diagram design. Figure 2 as an example, combining with the experience design method for sequence function chart to supplement.

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Vehicle, Mechanical and Electrical Engineering

Fig. 2 Sequence function chart of deep hole drilling machine The initial activation. S7-200 the state components in the initial state is low level. Sequence diagram is not working. Need to be the initial state of the step S0.0 activation. The initial step S0.0 usually activated by SM0.1. As shown in figure 3, after the initial activation equipment begin to work. When the programmable controller from run to stop to run state, two or more Step activated in sequence function chart. Equipment failures caused by error control. To avoid the error of the initial state is activated many times, using the initial activation method as shown in figure 4. Whether all state components to zero will be the initial step S0.0 activation. To avoid the initial step S0.0 repeatedly activated in the working status.

Figure. 3 Using SM0.1 activation

Figure. 4 Using button to activate

Accurate stop and Emergency stop. When the processing is complete, the bit will automatically return to the origin wait for the next processing. This is an accurate stop. But the sequence function chart cannot be achieved for the accident emergency stop. There are two ways of emergency stop: One is to set the stop button. Emergency stop by manual will return to the origin bit after went back to work. As shown in figure 5. Another is to continue to complete the rest of the task after troubleshooting. First choose timer has the memory function as TONR type timer. The second place M10.0 in sequence function chart of all the transition conditions and each step task. At last the button control M10.0 to stop running. As shown in figure 6. After the emergency stop, the controller output is zero to stop running. Step activation can't transfer but keep working state. When emergency stop keep cancel, Press the start button will continue to work in the stop position.

Figure 5. Stop by manual

Figure 6. Stop by memory

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To restore working state after Power on. S7-200 set related the status of the components, timer, auxiliary relay setting for memory. This is a potential safety hazard, because memory components makes the equipment run immediately after power recovery. Therefore, we need to design can continue to work after troubleshooting. Reference is shown in figure 6. When restore power, the drilling machine need to press the button I0.0 can continue to work. Ensure the safety after the power recovery. Summary Sequence function chart design method used in the deep hole drilling machine control. Through the initial activation and Emergency stop and power restore Settings. Overall improvement shortages in the design of sequence function chart. To cut out the safety hidden trouble and waste products. It is a good reference for the sequence function chart design method. According to the needs of safety first, the possible problems in control must to be prevent and inspection. When the design method cannot satisfy the needs of control. References [1] Chao Wang, Talk about Some Tips of Programmable Controller System Design, J. Computer CD Software and Applications, 2011, (12). [2] YuanFang Pei, Ping Feng, Jichang Kang, Idea of Description on Ladder Diagram's Data Structure for PLC, J. Computer Engineering and Science,2009, 31(12). [3] Yuanbo Liu, Lixian Xue, Yi Zheng, Discussion on program design of PLC controlling system, J. Gold, 2011, 32(4). [4] Zongren Guo, Zhikai Wang, Yan Li, The Application and Realization of PLC Hierarchical Intelligent Control System, J. Acta Electronica Sinica, 2002, 30(4). [5] Sha Jin, Jingtao Geng, PLC Application Technology, China electric power press , Bei Jin, 2010.

Applied Mechanics and Materials Vol. 721 (2015) pp 330-333 © (2015) Trans Tech Publications, Switzerland doi:10.4028/www.scientific.net/AMM.721.330

Submitted: 28.10.2014 Accepted: 01.11.2014

The Control Design of the Chain Magazine Test Bench Mingze Raoa School of College of Engineering and Technology, Southwest University, Chongqing 400715, China a

[email protected]

Keywords: Magazine Test Bench; control system; PLC; PC.

Abstract. The stable operation of the chain magazine which is important functional parts of the machining centers affects the processing efficiency directly, requiring test bench and related experiments in progress for the chain magazine performance parameters. We design the hardware and software and complete PLC chain magazine test bench based on the control and experimental testing requirements in the analysis of specific institutions and chain magazine movement principle. PLC is the core of the test bench which can achieve the operation of the magazine and the control automatic tool changer. The PLC program according to the experimental requirements can control tool magazine and automatic tool changer different motion process for related test. PC can achieve magazine movement simulation, monitoring the process of the tool changing, as well as recording and analysis of test data. The Anti-interference cabinet of the test bench ensure the stable operation of control system. The tests proved that PLC-based test bench able to meets the test requirements and complete the default action for a series of related tests with simple control, stable operation and scalability. Introduction The magazine and automatic tool changer are composed mainly of magazine and manipulator as important parts of the machining center. The magazine performs the tool selection which storages in the magazine, manipulator changes the tool with the spindle of the machining centers, which can complete the multiple processes after a single setup of the work piece, so the performance and parameters of the magazine and automatic tool changer affect the performance machining centers directly. It has put forward higher requirements for magazine and automatic tool changer with the development of machining centers. The chain magazine and automatic tool changer with the advantages of large capacity are used widely in a number of large and medium sized machining centers which need to be configured a lot of different type’s tool [1]. A higher degree of automation of the chain magazine and automatic tool changer, motor coordination of mechanism is relatively complicated, especially prone to failure during the process of tool changes. Therefore, we design the control system of chain magazine test bench, test the performance of magazine and record the fault data. We can master the performance and parameters of the magazine through the design the control system of the test bench, which provide an important basis for the improvement and perfection of the magazine and automatic tool changer. The overall design of the magazine test bench The control system of the chain magazine test bench is designed in the basis of studying the requirements of test bench and researching the abroad design of magazine adequately, and the overall control program of test bench is designed in this article. We have developed the control system which the core is PLC and the detection system based VC of test bench. The main structural of test bench are support units, chain magazine, virtual spindle, control systems and detection systems. The control system of test bench realizes the position of the tool magazine during the operation, the flip of pocket and tool changing by manipulator [2]. We design the bracket and install the sensor according to the features of the flip operation of magazine pocket, develop detection system based VC, complete the

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acquisition and analysis of the test data in real-time, and preserve the test results by VC program. The using separated design for control system and detection system of the test bench, the operation of control system is only for magazine and manipulator, and the detection is independent of control system. The separated design for the control systems and detection systems can not only ensure the stable operation of the control system to simplify the entire operation of tool changer, but also improve the reliability of the entire detection system to ensure the test results are accurate and effective. The overall design of the chain magazine test bench diagram as follows:

Fig.1 The overall design diagram of Test Bench

Fig.2 The block diagram of Test Bench control system

The design of control system We design the control scheme of test bench, when the action timing of tool magazine and automatic tool changer are analyzed. The design of the test bench control systems is mainly to the performance and indicators of the magazine is detected and validated, so we decide that the programmable logic controller (PLC) is the core of control system of test bench, the use of PLC can control servo drives and servo motors complete the precise positioning of the tool selection. The movement of opening and closing of two five-way valve which is controlled by PLC driven the cylinder to achieve tool pocket flip and rotate. Meanwhile, PLC control inverter and AC motor, and cam box achieve the action of tool changing by manipulator. It is achieved that automatic and manual tool election and changer are controlled by PLC program, which be able to complete requested action for detection system. The figure above is block diagram of test bench control system:

Fig.3 The diagram of input and output of PLC

Fig.4 The flow chart of nearest tool election and tool changer of the magazine

332

Vehicle, Mechanical and Electrical Engineering

The hardware design of control system The selection of PLC. It determines the I/O points of the controller according to the requirements of magazine test bench, and increases of spare capacity 10% to 15%. PLC program storage capacity must be less than the PLC design capacity to make the program run successfully. Program storage capacity is determined by the length of the program, which estimates: Storage capacity = switch I/O total point × 10 + analog channel number × 100 [4]. In summary, in order to meet the above controlled conditions, we select Mitsubishi FX1N-40MT PLC in the control system, which is used previously, and the programming software is simple, communicational interface is rich, skilled application of Mitsubishi PLC and high cost performance. According to the tool changing process of the magazine and automatic tool changer and the analysis of action sequence, it determines the PLC which requires the following input and output signals: The selection of motor The magazine is driven by servo motor drive to achieve precise control and positioning. PLC programs can easily set different speed of servo motor depend on the weight of the tool, set the appropriate speed and shorten the change time possibly [3]. Servo motor driven cylindrical indexing cam and indexing plate rotates, thus stimulate the movement of chain fitted tool pockets. Closed-loop control can be achieved by servo motors with encoders, so that the tool can be positioned in the tool changing position accurately. It need calculate the servo motor speed. According to the design parameters of the chain magazine test bench, the transmission speed of the chain is 15m/min. 15 = 6 .8 r/m in 2π R 2 × 3 .1 4 × 0 .3 5 n n nV The speed of the motor: ω = 1 2 3 m a x =1 2 × 1 × 1 5 × 6 .8 =1 2 2 4 r/m in 2π R

The speed of sprockets: ω 1 =

V

=

The gear ratio of cylindrical indexing cam and sprocket is that n1 = 12:1. The gear ratio of Cylindrical indexing cam gear and reducer output gear is that n2 = 1:1, which of servo motor and reducer is n3 = 15:1. It is calculated that ω = 1224r/min. Therefore, the choice of the servo motor speed should be 1500r/min. Total motor power: W = M × dθ＝W0＋W f ＝M 0 × dθ 0＋M f × dθ 0 Wherein the suffered friction of the shaft rotated in the rotation is far less than the resistance of the sliding friction generated between the chain and the guide rails, so it is negligible, only consider the chain sliding friction. It can be obtained that the maximum power of the motor is 1.12kw by calculated. According to the requirements of motor selection, select Yaskawa servo motors and the motor power of 1.3kw SGMGV-13ADC61, servo drives SGDV-120A01A. The software design of control system The PLC programs are edited based on the control requirements of the test bench, in order to realize the function of tool selection and the nearest automatic tool change. The automatic control program prepared by PLC has the following features: generates the number of target tool randomly, achieve the selection of the nearest tool and automatic tool change by the PLC internal logic operations. The timing of the nearest and fast tool changer In the factor of the efficiency of machining centers, tool changing time is the one of measures; therefore, the recent magazine tool change is the principle that achieves the position of the tool by shortest path. And it need improve the reliability of the software programs as far as possible, so that the action of ATC can be performed smoothly to reduce the factors which cause instability [5]. PLC

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control the action of solenoid valve to achieve the control of the flip of tool pocket, the manipulator performs the action of tool changer only in the case that determines the completion of the flip of tool pockets. PLC determines the next action through received feedback signal, which the completion of previous action is the condition of next action begins. The entire ATC actions must be clear and orderly. To meet the above requirements, we design flow chart of the nearest tool selection and tool changer shown in Figure 4: The HMI of PC

Fig.5 The human machine interface of control system The design of control system HMI for the test bench is used software of configuration; PC communicates with PLC through an agreement, simulates the current operating status of the ATC and sets parameter. HMI interface is shown below: Conclusion This paper describes the overall design of control systems design concept and process from aspects of the design of hardware and software of the magazine test bench, describes the control logic of the magazine test bench. This control system of test bench can perform real-time and control the action accurately by designed program Simple and reliable control design of the test bench ensure stable operation of the magazine, achieves a series of performance of the detection; The magazine test bench conducts an independent of the control and detection systems, which design can improve the reliability of the control system of the magazine; Through the control design, the magazine can commence performance testing, the data of detection supports research to improve the reliability of magazine. References [1] Li J M. Modular Machine Tool & Automatic Manufacturing Technique, 2012, (10), p.103-105. [2] Zhang Y M, Deng W P, Guan W, et al.: Modular Machine Tool & Automatic Manufacturing Technique, 2012, (9), p.56-58. [3] Tan G H: Mechanical Engineer, 2010, (7), p.40-41. [4] Huang J: Electrical control and programmable logic controller. Beijing: Mechanical Industry Press, 2004, p.195. [5] Zhu Z H, Han J: Machinery Manufacturing, 2011, (9), p.28-31. [6] The User's Manual of Yaskawa AC servo drive Σ-V Series. Yaskawa Electric Co., Ltd., 2007. [7] Guan W: The Research on the Detection and Control System of the Chain Magazine and Automatic Tool Changer, Beijing University of Technology, 2012.

Applied Mechanics and Materials Vol. 721 (2015) pp 334-337 © (2015) Trans Tech Publications, Switzerland doi:10.4028/www.scientific.net/AMM.721.334

Submitted: 28.10.2014 Accepted: 01.11.2014

Design of Foil-making Machine Based on Motion Controller Dehui Zhang a, Xiaoqiang Wu b College of Mechanical Engineering, Inner Mongolia University for the Nationalities, TongLiao, China a

b

[email protected], [email protected]

Keywords: Motion controller; Foil making; Control system.

Abstract. The work of traditional foil making machine is a manual or semiautomatic process, the work efficiency is low, and the security risks exist. In this paper, a kind of foil making machine is designed based on motion controller, the automation of foil process is realized, the work efficiency is improved, the production cost is reduced, and safety of the operator is guaranteed. It makes a certain contribution for the development of foil making machine. Introduction Foil making machine is a kind of machine that can forge the metal sheet into such a metal foil as thin as wafer through machine forging, it is widely used in the application of gold foil forging[1]. Before invention of foil making machine, the foil making work was done by hand, the amount of labor large, the efficiency is not high. The existence of machine instead of man, it greatly reduces the amount of labor of human [2]. The essence of foil machine is the use of ductility of a metal sheet, it will continue to forge a metal sheet into a foil. Foil making machine has two main parts, respectively they are heading and workbench. When it works, the heading driven by the power device moves up and down, the sheet metal on the workbench is forged into the foil. The foil making machine always uses semiautomatic structure, the metal sheet need to be hand-held to move on the workbench to be forged on different parts by the forge heading, but this way of working is very dangerous, accident often occurs in forging [3]. In recent years, people begin to study automatic foil making machine control system, but the systems are generally based on single chip or PLC, the system has poor universality, low reliability. The motion controller with its high control accuracy, good reliability, and good expansibility is widely in industrial control in the present time [4]. In order to realize the full automation of foil making machine, reduce the amount of labor, improve labor efficiency and guarantee the safety of staff, in this paper, a kind of foil machine design based on the motion controller is introduced. The working principle and structure design The working principle. Foil making machine’s work mainly relies on the ductility of the metal sheet, through continuous reciprocating motion of forging heading, the metal sheet will be by constantly forged to be thinner and thinner. In the process of the work, it need to control the speed of forging heading to meet the different requirements of metal sheet. In order to make the metal foil more uniform, the metal sheet must be forged on different location, the workbench must be able to move to any location according to the system requirements. The process of foil is realized by the reciprocating movement of the moving worktable and forging heading. Structure design. Foil machine is mainly composed of two parts, respectively they are heading and workbench, the introduction will be separately carried on in the following. The foil making machine coordinate system is defined as shown in figure1. Forge heading: The main function of forge heading is to forge metal sheet, instead of manual operation. Movement of forge heading is to do the up and down reciprocating motion along the Z direction. The structure used in foil making machine must have good reliability, and can adapt to the high intensity of impact. In this paper, heading of foil making machine is realized through the slider

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crank mechanism as shown in Figure 2, the crank slider mechanism is widely used in various pressure machine, and it has the advantages of simple structure, convenient use and good reliability. The crank is driven by motor to rotate to drive the slider block to do the reciprocating motion, forging head is connected on the sliding block, the forged heading reciprocating motion on the Z direction is realized, the foil will be forged, it also can control the rotation speed of the servo motor to control forging head reciprocating frequency, in order to adapt to the different metal.

Fig .1 The foil making machine coordinate system Fig.2 The slider crank mechanism Workbench: The workbench is the carrier of sheet metal, in the process of working, workbench moves along X and Y direction in the XOY plane. Workbench adopts the servo motor to drive which is always used in machine tool. In order to achieve the required motion, rotation of servo motor must be transformed into the linear motion of the workbench, in the foil making machine transmission mode of the screw nut is adopted to transform the rotary motion of the motor into linear motion of workbench. In order to make the two vertical motions of X and Y two directions do not interfere with each other, workbench is designed with two layers, as shown in figure 3. Each layer consists of a servo motor through a screw, the workbench is driven by the servo motor to respectively move in X and Y two directions. The workbench movement speed can be controlled by the speed of servo motor, the workbench will move corresponding path according to the control of system.

Fig.3 Workbench design

Fig. 4 System control principle

Foil making machine control system design The principle of control system. The motion controller has the advantages of high control precision, fast response, good reliability, it has been widely used in recent years. The core of control system of foil making machine in this paper is the motion controller, motor controller will control the movement of X, Y, Z three directions, detection device will feedback motor motion to the system, and the motion will be adjusted according to the feedback information. Motor on Z direction is mainly used to drive the forging heading, force and frequency of the heading are controlled by motor speed in working process, and power detection device is installed in the Z direction. The motor on X and Y direction is mainly to drive metal sheet, limit switches are respectively installed in X and Y direction to avoid danger occur. System control principle is shown in figure 4.

336

Vehicle, Mechanical and Electrical Engineering

Control system design. Control system operation flow chart is shown in figure 5. First of all, the system initializes, after the initialization, do the model selection and parameter setting. Foil making machine is divided into automatic mode and manual mode. In the automatic mode, the workbench will according to the control system program do the corresponding trajectory; in manual mode, the workbench is controlled by hand. Z axis motor is opened after the completion of parameter setting, then the X and Y axis motor are opened. I/O distribution of system is shown in Table 1, system programming software using the motion controller own software, motion controller programming language is based on Visual Basic, it has the advantages of simple structure, easy programming. Do the modular programming according to the above principle, finally the design of control system is completed. Table 1. System I/O distribution IN0 IN1 IN2 IN3 IN4 IN5 IN6 IN7

Input X-axis limit switch 1 X-axis limit switch 2 Y-axis limit switch 1 Y-axis limit switch 2 Z-axis limit switch 1 Z-axis limit switch 2 Force sensor Temperature sensor

OUT1 OUT2 OUT3

Fig.5 Control operation flow chart

Output X-axis servo motor Y-axis servo motor Z-axis servo motor

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Conclusion According to the existing problems of traditional foil making machine, in this paper, a kind of full automatic foil making machine is designed based on motion controller, it can liberate human, improve the work efficiency, reduce the labor cost, and ensure the safety of the operators. Motion controller is a kind of new control elements, this paper has a certain guiding sense on the development of foil making machine. References [1] Yang Haibo, Zheng Chensheng, Yin Jianguo. The developing process of automatic gold foil machine [J]. Journal of Nanjing Institute of Industry Technology.2013, 13(4). [2] Wang Yuansun. Forging patent reports [J].Forging Technology, 2005, (2):p.28. [3] Yang Haibo, Yang Xinchun. Control and realization of biaaxial work bench based on PLC [J]. Journal of Chinese Agriculture Mechnaization, 2014, 35(4):p.288-230. [4] Wu Hong, Jiang Shilong, Gong Xiaoyun. Current situation and development of motion controller of [J]. Manufacturing Technology and Machine Tool, 2004, (1):p. 24-27.

Applied Mechanics and Materials Vol. 721 (2015) pp 338-341 © (2015) Trans Tech Publications, Switzerland doi:10.4028/www.scientific.net/AMM.721.338

Submitted: 04.11.2014 Accepted: 10.11.2014

Optimization Research on the Circuit Breaker Instantaneous Characteristic Experiment Based on Neural Network Xingjian Jiaa School of mechanical and electrical engineering, Nanchang Institute of technology, Nanchang 330099, China a

[email protected]

Keywords: LabVIEW, Neural Network, circuit breaker check, Matlab.

Abstract. The circuit breaker’s instantaneous characteristic test equipment controlled by computer is introduced in this paper. There are still some problems in the design. To address those issues, compensation coefficient is presented in order to improve the accuracy of instantaneous current. Based on analyzing the influencing factors of instantaneous current and establishing equivalent model, neural network control method is adopted. Under LabVIEW software environment, neural network which has been trained by MATLAB script node is called to compute the compensation coefficient. In order to enhance system adaptability, the neural network online adjustment is achieved by changing training samples. The accuracy of instantaneous current is further improved. Introduction According to the regulations of the molded case circuit breaker reliability test method, when the test current reaches 80% of the set values for the short-circuit current, that is eight times the rated current(8IN), the circuit breaker should not tripping in the specified time(0.2s).When the test current reaches 120% of the set values for the short-circuit current, that is twelve times the rated current(12IN), the circuit breaker should tripping in the specified time(0.2s).Debugging of circuit breaker transient dynamic characteristics is carried out in accordance with this standard[1-2].

Fig.1 Main circuit of instantaneous debug equipment Rated current is generated by electric voltage regulation mode and open circuit voltage (V1)is detected. Large current transformer primary side switch and electric voltage regulation are used to guarantee the open circuit voltage. Schematic diagram is shown in figure1. In the experiment of testing instantaneous characteristics for circuit breaker, the transient current exists in the experimental circuit because of the closing making angle, which has serious influence on the testing precision. Meanwhile, in the short circuit experiment of switch apparatus, in order to eliminate transient current, it is necessary to make the voltage angle equal to power factor[3]. Another problem is that the closing making angle is same when the rated current is different. Therefore, if the AC breaker is closed in the

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same closing making angle when the testing current is various levels, the transient current will exist in the experimental circuit, and the test precision is inaccurate. To address this issue, this paper improves the accuracy of current by referring compensation coefficient in the way of instantaneous calibration current control. Control system general scheme In order to ensure the accuracy of instantaneous debugging current, compensation coefficient k8 is a key factor.Eexpected current, power factor angle and rated current corresponding the open circuit voltage V1 are key parameters to affect compensation coefficient. These parameters are related and influencedeach other, it is a multiple data fusion system.The neural network is most suited to solve such problems. BP network is currently the most widely used neural network model, it has strong nonlinear mapping ability and fault tolerance. Its learning rule is that through back propagation to adjust the network weights and threshold to minimize the network error sum of squares.Compensation coefficient adopts the following control structure that is shown in figure 2. The control system consists of neural network controller, network training institutions, generation currents institutions. Neural network controller calculates voltage compensation coefficient according to expected current,power factor angle and rated current corresponding the open circuit voltageV1. According to the error of actual current and expected current,network training institutions adjust the training sample data and online training network controller.

Fig.2 Control structure diagram of the compensation coefficient Neural network controller The notable characteristic of the neural network is that it can acquire knowledge and experience through learning from sample. In considerable degree, Samples decide the intelligent level of the neural network. So the selection of sample in the study of neural network will play a key role, it directly relates to the success or failure of network training and using. Based on the analysis of the detection circuit , we find that expected current,power factor angle and rated current corresponding the open circuit voltage V1 are key parameters to affect the output. The three parameters in the system are collected by the sampling card PCL818HG. The output is the expected target output of the neural network. In order to ensure the accuracy of instantaneous debugging current, compensation coefficient k8 is a key factor. Compensation coefficient is selected as the output of the network. Through running the program on site, we adjust parameters step by step to get the most appropriate compensation coefficient as the network output. Training of neural network controller is divided into two stages: off-line training and on-line adjustment. The off-line training method of neural network controller: P is the input sample of network, k8 is the target vector data of network. The program of LabVIEW call Matlab is shown in figure 3. The program create a neural network.

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Fig.3 Establish neural network We first list the input sample and the target vector. Seventy two groups of data are selected. A BP network is created. Number of training and training error are defined, and finally use function "train" to train the network. It will train neural networks when the test procedure start running. At the same time, the mean square error curve of the BP neural network will be pop up. At the end of the neural network training, a function is determined which can express the relationship among input expected current,power factor angle , rated current corresponding to open circuit voltage V1 and the output compensation coefficient. This function is a neural network structure in Matlab environment ,it can get the function output using function sim in Matlab environment. LabVIEW provides a Matlab Script node, including executive Matlab command, using feature-rich toolkit, such as neural network toolbox.After completion of training neural network controller, voltage compensation coefficient can be calculated using the method of Fig.4.

Fig.4 Calculation method of compensation coefficient The experiment shows that neural network algorithm is very effective.Along with problems such as equipment aging, some parameters will change. If training network always uses the invariable samples and target vectors in training network,it will produce large errors.Input samples and target vectors are always changed through by online adjustment method, making them constantly close to the real value of equipment. The core of online training institutions is to adjust training data samples, The training data samples are replaced by new data according to the error of the actual current and expected current. According to above principles, it can be adjusted by the program. Actual collection of input vector replace the most close to a list of input vector P,the expected current multiply the last calculation of compensation coefficient,and then divide by the actual current,the results replace the output vector corresponding to this set of input vector. If training samples change, the network should train again. Voltage compensation coefficient is calculated and then instantaneous debugging current is produced by generation currents institutions. Compared with the deviation between system actual current and expected current,directly control action is generated to eliminate the deviation. The whole system form a closed loop. Results The expected current of the simulation input data is selected in different grades. Table 1 lists three cases of simulation results such as the fixed compensation coefficient, fixed training sample and

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online adjustment. Take expected current (1040A) for example, when the compensation coefficient is fixed to 1, the actual current is 1025.83A, the error of instantaneous current and expected current is 14.17A. The error is 4.51A through by fixed training sample of neural network,the actual current is1035.49A. In the process of neural network online adjustment, network training institutions online adjust the training data sample according to the error of actual current and expected current, recalculate the compensation coefficient, and eventually generate instantaneous debugging current. The error is only 0.32A through by online adjustment of neural network. A large number of simulation results show that after neural network training the error value between instantaneous current and expected current is much smaller than the error that in the case of fixed compensation coefficient, and the error value is smallest after the neural network online adjustment. Table 1 Comparison of various simulation results expected current (A) 800 1040 1160 1280 1400 1800

test sample impedance (Ω) 0.0035 0.0045 0.005 0.0035 0.004 0.005

actual current (fixed compensation coefficient) (A) 787.172 1025.83 1151.31 1262.72 1379.7 1786.35

actual current (fixed training sample) (A) 800.969 1035.49 1160.96 1273.67 1400.34 1795.94

actual current (online adjustment) (A) 800.969 1040.32 1160.96 1280.58 1400.34 1800.74

Conclusion The control model of instantaneous calibration current based on neural network is presented in this paper. The method makes full use of the nonlinear characteristic of the neural network, adaptive ability and learning ability. According to training study, it can close to the input and output characteristics of circuit breaker testing equipment. According to the error of actual current and expected current, network training institutions adjust the training sample, online train network controller, calculate the voltage compensation coefficient. The simulation results show that neural network online adjustment model can produce more precise 8 times and 12 times instantaneous debugging current, and the circuit breaker instantaneous characteristics can be accurately detected. Acknowledgment Youth fund of Nanchang institute of technology under Grant No. 2012KJ011 sponsored this paper. The authors deeply appreciate the supports. References [1] Du Taihang, Mi Yanzhi: Journal of Electrician Technique, Vol. 8 (2003), p.36-39. [2] Du Taihang, Chen Peiying: Transactios of China Electrot Echnical Society, Vol.18 (2003) No.6, p.80-83. [3] Wang Xiaoyi. Computer detection and control technology for instantaneous characteristics test of molded case circuit breaker (MS. Hebei University of Technology, China, 2008), p.9. [4] Jia Xingjian: Research on the control technology of instantaneous calibration current based on neural network (MS. Hebei University of Technology, China 2009), p.5.

Applied Mechanics and Materials Vol. 721 (2015) pp 342-348 © (2015) Trans Tech Publications, Switzerland doi:10.4028/www.scientific.net/AMM.721.342

Submitted: 22.10.2014 Accepted: 28.10.2014

Analysis on Composite Action Control Strategy of Separate Meter-in Separate Meter-out Control System Wanrong Wua, Jianchao Yaob School of Central South University, Changsha 410083, China a

[email protected], [email protected]

Keywords: SMISMO valve, Control of pressure and flow, Composite action, Co-simulation.

Abstract. Based on the shortcomings of traditional multi actuator composite action on its coordination and load adaptability, this paper has put forward a hydraulic system model where separate meter-in separate meter-out controls the multi actuator, according to different action working conditions of actuator, it has provided a composite control strategy based on pressure flow, and through AMEsim and MATLAB, it has established the composite action hydraulic transmission model of double-actuator system and simulation model of control system, and then conducted co-simulation to verify the designed controller’s good coordination and load adaptability to the separate meter-in and separate meter-out control system under different composite action working conditions. Introduction Traditional multi actuator electrohydraulic control technology (hydraulic machinery technology, electro-hydraulic technology, flow matching technology, and multi pumping flow matching load-sensing technology, etc.) adopts single valve core control, so each valve only has one controllable degree of freedom, and it is easy to cause great throttling loss [1]. Separate meter-in separate meter-out is a new control valve with double cores whose oil inlet and outlet throttling area can be separately controlled and adjusted, and compared with traditional single valve core, its double-valve-core system boasts increased degree of freedom, which improves the flexibility of system control. Professor Palmberg is the first to propose the concept of separate meter-in separate meter-out control system, and by decoupling the actuator velocity and pressure, Palmberg has achieved the bivariate control of system pressure and actuator velocity [2]. Liu Yingjie, from Zhejiang University, has ever made researches on the controller and its system characteristics of separate meter-in separate meter-out direction valve [3]. Xu Bin has studied the mode switching characteristics of separate meter-in separate meter-out and proposed system controlling methods of working mode choice and mode switch in different load mode [4]. This paper, based on the advantages of separate meter-in separate meter-out control over traditional valve core position control, builds its own method of united control of valve core displacement by pressure and flow, and according to different working conditions of actuator, it sets up the double-actuator composite action controller and simulation model, thus enhancing the coordination and load adaptability when the actuator of separate meter-in separate meter-out (SMISMO) control system makes composite action. Control strategy design of SMISMO control system Working principle of the system. Each actuator in the SMISMO control system has two SMISMOs to independently control the inlet and outlet of oil cylinder, so the control flexibility of this system will be increased, which helps the system to achieve the combination of different control strategies through programming and to give full play to advantages of SMISMO control system, thus improving its dynamic performance and static performance. As is shown in Fig. 1, SMISMO control system mainly consists of variable pump 1, controller 2, actuator 3 and 4, SMISMO pilot valve 9, 10, 11 and 12, SMISMO king valve 5, 6, 7 and 8, and safety valve 13. The system can decide actuator’s working condition by means of given input flow signal and monitored stress F’s direction of piston rod; according to the working condition, the controller

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chooses control strategy of valve 1 or valve 2; thereafter it will output signal D11 and D12 controlling pilot valve core displacement and collect valve core displacement signal X11 and X12 of king valve, stress signal P11 and P12 of actuator’s oil inlet and outlet, and system working stress signal P0, thus to realize the composite pressure-flow control to actuator 3. Actuator 4 employs the same control strategy. The two actuators use separate close-loop pressure-flow control, so when the two actuators make composite action, the system will control the output flow of variable pump through the controller, and when adequate flow of pump, it can ensure the controller to make composite control action separately but not to influence each other; when insufficient flow of pump, it can decrease the proportion of each actuator’s flow through the controller, to ensure the coordinate action of actuators and avoid the situation where overloading road in load-sensing system stops working.

Fig. 1 Schematic diagram of double-actuator SMISMO control system The pressure-flow control of single valve. In order to get good static control precision and dynamic performance of flow, this paper introduces the way to calculate flow control, and the computational formula is Q = Cd ( xv ) A( xv ) 2∆P / ρ . Its working principle is: based on the input flow Q and the differential pressure of meter-in and meter-out ∆P and according to formula xv = Q ρ / 2∆P / Cd ω , the controller will figure out valve core’s theoretical displacement xv ' , then

output this signal to control valve core displacement and monitor the core’s actual displacement xv , and by means of the flow control formula, it will work out valve core’s actual flow Q ' , and then contrast between actual flow Q ' and theoretical flow Q to realize the close-loop control of flow[5]. The control principle is shown as Fig.2.

Fig. 2 Calculating flow control principle

Fig. 3 Pressure feedback control principle

Pressure control method means controlling the outlet and inlet pressure of valve to maintain steady, calculating pressure feedback signal of valve core and comparing this signal and control input signal, and output displacement of the corresponding valve core through controller. The control principle is as fig.3. The Basic control strategy of SMISMO control system. As for excavator, loader and other common engineering machinery, the work mode of their hydraulic cylinder can be divided into four conditions [6]: a, impedance stretching out b, surpassing retraction c, surpassing stretching out d,

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impedance pulling back. According to the pressure-flow control strategy of SMISMO control system, the control schemes of hydraulic cylinder’s four working conditions are shown in fig.4.

Fig. 4 Pressure-flow control strategy of hydraulic cylinder When the hydraulic cylinder is in the mode of impedance stretching out and impedance pulling back, the oil inlet will choose flow control strategy to command the velocity of piston rod, and the oil outlet will choose pressure control strategy to reduce the system’s back pressure thus to decrease the energy consumption; while when the hydraulic cylinder is the mode of surpassing pulling back and surpassing stretching out, the oil inlet will choose pressure control strategy to control the system’s back pressure thus to avoid cavitation, and the oil outlet will choose flow control strategy to command the velocity of piston rod. System Modeling SMISMO Electro-hydraulic Proportional System Modeling. The SMISMO input signal that controls hydraulic system will act on the pilot valve to make the king valve core take the action of controlling actuator. Here it needs to build the mathematic model of electro-hydraulic proportion control system and mathematic model of hydraulic cylinder, among which SMISMO is the main component controlling the system. Here are the model of proportional amplifier, model of proportional electromagnet and pilot-king valve mathematic model. Model of proportional amplifier: N (s) = KaU (s) (1) In this equation: N ( s ) means amplifier’s output voltage; U ( s ) means input voltage; K a means amplified proportional coefficient. Model of proportional electromagnet, moving-coil motor electromagnetic force equation: F = Kt i1 (2) In this equation: F means electromagnetic force; K t means electromagnetic force coefficient; i1 means electric current through the coil. Proportional electromagnet control coil incremental equation: eg = L

di1 dx + Ri1 + K d dt dt

(3)

In this equation, eg means coil’s control voltage; R means internal resistance of the coil and amplifier; L means coil inductance; x means displacement of pilot valve. Pilot-king valve mathematic model. Because the corner frequency of inertial unit is far bigger than the coil’s nature frequency, the influence of inertial unit can be neglected [7]; therefore the transfer function of moving-coil motor can be got:

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K0 x1 ( s) = 2 2ξ K eg ( s ) s + ( 0 + K0 K d ) s + 1 + 1 2 w0 w0 K sp

345

(4)

In this model: K1 = 2Cd wp cos θ ( Psp − Pt ) K 0 = K vc / RK sp . According to the kinetic equation of king valve core [8], the transfer function of pilot-king valve can be acquired: Kq

K ce vt s) ( M m g + K 1 y0 )(1 + 2 xv 1 ( s ) A A 4 β e K ce = K V K K M mV0 3 M K Kv K K x1 ( s ) s + ( m 2 ce + v 0 2 ) s 2 + (1 + v 2 ce + pe 02 ) s + pe 2 ce 2 A A A 4βe A 2βe A 2βe A x v1 −

(5)

1 In this equation: K pe = K3 + 2Cd w cos θ ( Ps − Pt ) , K ce = K c + Cic . 2 In case of disregarding the hydraulic system’s pressure loss along the line, the highest pressure of SMISMO is highest load pressure of the system, so when the piston rod of hydraulic cylinder stretches out, the flow equation of the system’s big cavity and small cavity can be: Q1 = Cd wxv1 2( PLs − P1 ) / ρ (6)

Q2 = Cd wxv 2 2( P2 − Pt ) / ρ

(7)

And the flow equation of continuity is: Q1 − Cic ( P1 − P2 ) = A1 x + Cic ( P1 − P2 ) − Q2 = A2 x +

V1 dP1 β e dt V p dP2

(8)

(9) β e dt While when the piston rod of hydraulic cylinder pulls back, the flow equation of the system’s big cavity and small cavity can be:

Q1 = Cd wxv1 2( P1 − Pt ) / ρ

(10)

Q2 = Cd wxv 2 2( PLs − P2 ) / ρ

(11)

And the flow equation of continuity is: Q1 + Cic ( P1 − P2 ) = − A1 x −

Cic ( P1 − P2 ) + Q2 =

dV2 p dt

V1 dP1 β e dt

(12)

V2 dP2 β e dt

(13)

+

After conducting force analysis to the piston rod, we get its force balance equation: A1P1 − A2 P2 = mx + Bc x + kx + Fl

(14)

In this equation, A1 and A2 respectively represent the action area of the big cavity and small cavity, P1 and P2 respectively represent the pressure of the two cavities, V1 and V2 respectively represent the volume of them, and xv1 and xv 2 respectively indicate the displacement of king valve cores, x indicates the displacement of piston rod, β e the hydraulic elasticity modulus, Cic the leakage coefficient of the valve, Bc the viscous damping coefficient, k the load elastic rigidity, and Fl the loading capacity. Pressure-flow control model of single actuator. Based on the above calculating flow control method and pressure feedback control method, the simulink stimulation model of pressure and flow can be built and packaged; according to the control strategy shown in Fig. 4, it can establish the action control model of single actuator (Fig. 5) which contains flow control module, pressure control module and pressure-flow composite control selective module. Through measuring the stress direction of piston rod, the system can make switch between the flow model and pressure model, that is, when the

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stress direction of piston rod is in opposite to the direction of motion, the oil inlet will take the calculating flow control strategy and the outlet pressure feedback control strategy; while when their directions are the same, the oil inlet pressure feedback control strategy and outlet calculating flow control.

Fig. 5 Action control model of single actuator

Fig. 6 Simulink model of composite action control

Composite action control model of double actuators. In the double-actuator SMISMO control system, it will take the above close-loop control strategy for each actuator to make composite pressure-flow control, which includes the two actuators’ action control module and the flow distribution control module. Here the flow distribution control strategy can be made aimed at the double actuators’ composite action. When the pump flow can satisfy the flow needed by the double actuators, the controller will make the actuators’ flow together as the total output flow of pump; while when the maximum flow of pump cannot satisfy the flow needed, it will distribute the flow to each actuator according to proportion, to ensure the two actuators’ coordinate action. The Simulink model of double-actuator composite action control is presented in Fig. 6. System simulation model. After acquiring the pump outlet pressure P0, the inlet and outlet pressure P11, P12 and P11, P12 for hydraulic cylinder 1 and cylinder 2, kind valve core displacement X11, X12 and X21, X22 of SMISMO, as well as the force F1 and F2 of actuator 1 and actuator 2 in the double-actuator SMISMO control system,, we can connect MATLAB through simulink controller to make joint simulation, thus getting the pilot valve core displacement D11, D12, D21, D22, and the displacement of hydraulic pump. By means of AMESim, it can build the hydraulic system simulation model of double-actuator SMISMO, which is presented in Figure 7. The SMISMO in this model is valve ZTS16 of EATON Company. Simulation Results and Analysis Control performance simulation. The signals needed to set in the simulation are actuators’ flow control signal and back pressure signal, and the cylinder barrel diameter is 80mm and piston rod diameter 36mm in the model. The double actuators’ inlet flow is shown in Figure 8, where the flow of Actuator 1 is first set as 20L/min and switched into 40L/min after 1 second and the flow of Actuator 2 is set as 40L/min and switched into 80L/min after 3 seconds. The system’s back pressure is 0.5MPa, the load of Actuator 1 and Actuator 2 are both 3000N, and the maximum output flow of pump is 100L/min. The simulation duration is set as 5 seconds and simulation step is 0.01 second.

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Fig. 7 AMESim simulation model of double-actuator SMISMO control system The simulation results are presented in Figure 9: Actuator 1 and 2 can make rapid reaction during the start-up, piston rod 1 can react quickly at first second, while piston rod 2 has caused minor fluctuation but come to stable fast; at the third second, the flow required by the two actuators has exceeded the pump’s supply, so according to the distribution strategy in flow control module, the actuators’ flow supply proportion decreases to ensure they can make coordinate action when the pump’s supply is not enough. 0.4 flow of actuator 1 flow of actuator 2

60 40

0.2 0.1

20 0

velocity of actuator 1 velocity of actuator 2

0.3 Y: m/s

Y: L/min

80

0 0

1

2 3 X: Time [s]

4

Fig. 8 Inlet flow of actuators

5

0

1

2 3 X: Time [s]

4

5

Fig. 9 Velocity of piston rods

Load performance simulation. Both the input flow signals of Actuator 1 and 2 are 40L/min and have been maintained for 5 seconds. The two actuators’ load is shown in Figure 10: here the initial load of Actuator 1 is set as 3000N and switched into -3000N at the first second, while that of Actuator 2 is 6000N and changed into -6000N at the third second. And the simulation duration is 5 seconds and simulation step 0.01 second. The velocity of actuators’ piston rods can be seen in Figure 10. The two actuators can make action quickly and smoothly at startup, while at the first second, the abrupt load change of Actuator 1 has an impact on the velocity of the piton rod, but thanks to the switch of system control strategy, the piston rod can keep stable very soon; at the third second, Actuator 2 has a large range of load change, and there emerges velocity fluctuation simultaneously for both actuators but comes back to stable quickly. From Figure 11, it can be seen that when there is an abrupt change of load, the system can come to stable quickly, which means the mutual coupling effect between all branches of the system is less than that of traditional LUDV system [9]. Therefore, the double-actuator composite action control system of SMISMO control system boasts favorable load adaptability.

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0.25

5000

0

velocity of actuator 1 velocity of actuator 2

0.2 Y: m/s

Y: N

load of actuator 1 load of actuator 2

0.15 0.1 0.05

-5000 0

1

2 3 X: Time [s]

Fig. 10 Inlet load of actuators

4

5

0

0

1

2 3 X: Time [s]

4

5

Fig. 11 Velocity of piston rods

Summary This paper has put up with the pressure-flow compound control strategy, according to the flexible control characteristic of separate meter-in separate meter-out, established the simulink control model and AMESim simulation model of the double-actuator separate meter-in separate meter-out control system, made simulation analysis on the system’s control performance and load adaptability and verified its good coordination and load adaptability of the designed SMISMO control strategy when the double actuators make composite action, thus providing a new method for the multi-actuator composite action control strategy of SMISMO control system. References [1] L. Quan: The Present Status and Latest Progress of Electro hydraulic Control Technology of Multi-actuators Used in Construction Machinery. Hydraulics Pneumatics & Seals, Vol. 30 (2010) No.1,p.40. [2] A. Jansson, J.O. Palmberg: Separate controls of meter-in and meter-out orifices in mobile hydraulic systems. SAE Transactions, Vol.99 (1990) No.2, P.377. [3] Y.J. Liu: Research on Key Techniques of Independent Metering Directional Valve Control System (Ph.D.,Zhejiang University , China 2011), p.19. [4] B. Xu, D.R.Zeng, Y.Z.Ge and Y.J. Liu: Research on characteristic of mode switch of separate meter inand meter out load sensing control system. Journal of Zhejiang University(Engineering Science), Vol. 45 (2011) No.5, p.858. [5] L.Y. Gu and Q.F. Wang: Research on Calculated Flow Feedback Control Method and Its Characteristic. Chinese Journal of Mechanical Engineering, Vol. 35 (1999) No.4, P.4. [6] A.H. Hansen, H.C. Pedersen, T.O. Andersen and L. Wachmann: Design of energy efficient SMISMO-ELS control strategies. Proceedings of the 2011 International Conference on Fluid Power and Mechatronics (Beijing, CHINA, 17 - 20 August 2011), P.522. [7] M.X. Cao: Hydraulic Servo Systems (Press of Metallurgy Industry, China 1991), p.125. [8] J.X. Zhu, X. Yang, Y.B. Mei and H.Y. Hu: Research on the dynamic characteristics of a new electro-hydraulic Proportional Valve. Mining & Processing Equipment, Vol. 35 (2007) No.10, P.118. [9] L.Y. Yu, L. Huang, J. Ke, W.H. WU and B. Deng: Research on load characteristic of LUDV control system. Machine Building & Automation, Vol. 41 (2012) No.4,

Applied Mechanics and Materials Vol. 721 (2015) pp 349-352 © (2015) Trans Tech Publications, Switzerland doi:10.4028/www.scientific.net/AMM.721.349

Submitted: 31.10.2014 Accepted: 06.11.2014

A Design and Implementation for The Auto-booting of Baseband Board with ARM, DSP and FPGA Liwei Hua College of Computer Science and Technology, Chongqing University of Posts and Communications, Chongqing 400065, China a

[email protected]

Keywords: ARM, DM8168, FPGA, nandflash, uboot.

Abstract. In this paper, I propose a solution for the auto-booting of baseband board, including the auto-booting of an ARM processor and the configuration of a FPGA chip. The baseband board’s FPGA chip is Xilinx’s XC6VSX475T which belongs to Virtex-6 series, the ARM processor is TI’s DM8168. ARM processor boots from nandflash, using uboot as its boot loader. I use Slave SelectMAP mode with 8-bit data bus interface as FPGA configuration mode. This paper introduces the transplantation of uboot and the FPGA configuration process with details, also analyses the result. This solution has already been used in TD-LTE TTCN extended test sets instrument, and it works well, which proves itself a practical solution. Introduction The main function of TD-LTE TTCN extended test sets instrument is to verify the ability of user equipment for management of wireless resources. Its baseband board is based on ARM, DSP and FPGA, featuring short development cycle and excellent expansibility. ARM is a 32-bit RISC processor, designed by ARM Holding, which is widely used in embedded systems. In this project, we use DM8168 which is developed by TI based on ARM Cortex-A8 core, and is booted by uboot from nandflash. FPGA, Field-Programmable Gate Array, provides user with a custom-built circuit with configuration data. We store the configuration data in a nandflash chip. Every time the board is started, ARM processor will load this data from nandflash to FPGA chip to configure it. This solution is easy to modify and update. As nandflash need to be wiped before written again, this solution also has a good security. Auto-booting Process The design of baseband boar. The baseband board’s hardware structure is ARM+DSP+FPGA. ARM serves as the master processor dealing with the protocol stack software.DSP chip deals with MAC layer and physical layer, and FPGA is used to hardware acceleration, system timing and baseband data interface control. The ARM chip, DMSoC-TMS320DM8168 is from TI. This chip has a Cortex-A8 microprocessor with NEON extension and a C674x DSP core. The ARM processor provides a high processing capability and communicates through an AXI bus with the AXI2OCP Bridge and receive interrupts from the MPU subsystem interrupt controller. It also has two on-chip RAM, each of them is 256KB. The chip can be booted from nandflash, norflash, SD card and SPI. In this paper, I choose to boot it from nandflash. The nandflash chip, MT29F2G16AADWP, on the baseband board is from Micron, has a capacity of 256MB. The width of its bus is 16-bit. It has 2048 blocks, each block has 64 pages, and each page is 2KB. CS0 is related to nandflash. The FPGA chip on the baseband board is Xilinx’s Virtex-6 XC6VSX475T. This chip featured high level logic and digital single processing capacity and low energy cost.

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Design of Auto-booting. The system boots from a nandflash chip, Fig.1 shows the auto-booting process.

Fig. 1 Auto-boot Process

Fig.2 Nandflash Memory Distribution

Transplantation of Uboot Uboot is an open source, primary boot loader used widely in embedded system. It is available for a number of computer architectures. It can also boot many operation systems. In this project, we use Nucleus as our embedded real-time OS, which is not supported by uboot’s bootm. So I decide boot it as a standalone application. The original copy of uboot I use in development is provided by TI for DM8168 EVM. The EVM has a similar structure compared to the baseband board. Stage 1. Some code for uboot booting stage 1 is in /board/ti/ti8168/evm.c file. This file has 4 major function below: Configure Main_PLL Configure Main_PLL Configure DDR_PLL Configure DDR_PLL Configure pin multiplexing Initialize SDRAM I edit parameters in clocks_ti816x.h, ensuring which is compatible with our baseband board. Then configure pin multiplexing. As baseband board’s configuration of pin multiplexing is different from that of EVM, we need edit set_muxconf_regs function in evm.c to adopt to baseband board. Another big difference happens in DDR. EVM has eight DDR3 chips with 796MHz, while the baseband board has only four DDR2 chips with 400MHz. I modify the configuration file to adopt to this differences. Firstly, delete the below defines: #define CONFIG_TI816X_EVM_DDR3 #define CONFIG_TI816X_DDR3_796 Then, define the below expression: #define CONFIG_TI816X_EVM_DDR2. After that, modify the DDR frequency defined in clocks_ti816x.h, delete other frequency definitions but DDR_PLL_400. Then modify the EMIF Parameters in ddr_defs_ti816x.h to adjust it to baseband board. After all that, I need overload below functions in evm.c according to the development manual for DM8168.

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Static void config_ti816x_sdram_ddr(void); Static void emif4p_init(u32 TIM1, u32 TIM2, u32 TIM3, u32 SDREF, u32 SDCFG, u32 RL); Static void ddr_init_settings(int emif); Then the transplantation of the booting stage 1 of uboot is finished. Stage 2. After uboot’s booting stage 2 starts (enter start_armboot function), a global environment variable named gd is initialized firstly, then init_sequence run. Init_sequence is a function array including functions to initialize the baseband board. I delete those functions not used. Finally init_sequence is like the one below. init_fnc_t *init_sequence[] = { board_init, timer_init, env_init, dram_init, NULL, }; For the same reason, I delete those functions and drivers which are not used in baseband board in start_armboo. Nand_init function configure nand_info, another global environment variable. Then I use the read function in nand_info to load FPGA configuration program from nandflash to the first on-chip RAM at 0x40300000. nand_info[0].read(&nand_info[0], 0x580000, nand_len, &nand_len, (unsigned char *)(0x40300000)); Nand_info[0] mean the first nandflash chip. As the board has only nandflash chip, this is the only choice. Then I use do_go_exec_al (defined by myself) function to redirect ARM processor’s PC pointer to the entry point of FPGA configuration program. After cross compilation, the uboot image uboot.noxip.bin, a binary file, is generated, which will be burnt into nandflash at 0x580000. Fig.2 shows the distribution of nandflash. Configuration of FPGA There’re eight methods to configure XC6VSX475T chip. According to the hardware, we choose Slave SelectMAP method. Slave SelectMAP method supports up to 32-bit data width bus, this bus can be used to configure XC6VSX475T as well as read back. In this project, we use 8-bit bus at first, and we plan to use 32-bit bus in future. ARM, FPGA and dual-port RAM communicate through CCLK, PROGRAM_B, DONE, D[0:7] and INIT_B single. Fig.3 shows the hardware connection diagram. ARM processor set PROGRAM_B single through its GPIO0_0 pin. And its GPIO0_5 and GPIO0_25 pins are used to receive INIT_B and DONE singles. Dual-port RAM’s write enable single is connected to FPGA’s CCLK, and its DATA[0:7] are connected to FPGA’s D[0:7]. The configuration data for XC6VSX475T is generated by ISE 12.4 from Xilinx. This binary file is burnt to nandflash at 0x600000 through JTAG interface. When system power on, the first thing to do is to boot ARM processor. At this point, PROGRAM_B is kept to a low level. The system will automatically copy uboot image to the second on-chip RAM(0x40400000), then execute uboot code to configure the processor itself and DDR. After that, uboot will copy itself from on-chip RAM to DDR(0x80700000), and continue to configure other hardware to complete the baseband board’s initialization. After all of that, the system will copy FPGA initialization program to the first on-chip RAM(0x40300000) and begin to execute it.

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Fig. 3 Hardware Connection

Fig. 4 XC6VSX475T Configuration Clocking Sequence

FPAG initialization program will configure dual-port RAM, Pin multiplexing and related GPIO ports. Then FPGA configuration data will be read from nandflash to DDR 0x82000000, APP image will be copy to DDR 0x90000000. When the copy is finished, the configuration of FPGA will begin. Firstly, pull high PROGRAM_B, at the same time; INIT_B is still at low level. When all XC6VSX475T’s configuration registers are reset to zero, INIT_B is pulled high. This means it is the time to configure XC6VSX475T. On the rising edge of INIT_B, the FPGA chip reads the mode select pins M[0:2] to decide which mode to use to configure XC6VSX475T. After that, system begins read data from DDR 0x82000000 to dual-port RAM. As the data pins used by XC6VSX475T also be used by dual-port RAM, data will be written to XC6VSX475T at the same time. When the data arrives FPGA, it will be checked with CRC. When configuration completes, XC6VSX475T will be activated. At this point, ARM processor will read the DONE signal, when it is in high level, ARM processor’s PC pointer will be redirected to 0x90000000, and begin executing APP image.

Fig.5 FPGA Configuration Data Transferring Conclusion Fig.5 shows how singles change when transferring configuration data to FPGA after DM8168 boots. The vertical axis represents single’s value, the lateral axis represents time. From the figure, we can see configuration data is written into FPGA. At present, the transplanted uboot and Slave SelectMAP configuration with 8-bit width for FPGA have been applied in TD-LTE extended test sets instrument. And it works stably, which shows that the solution is a right chose. In future, I plan to use Slave SelectMAP configuration with 32-bit width to speed up the configuration of FPGA. In this paper, I describe the design and realization of the baseband board for TD-LTE extended test sets instrument. The solution has flexible configuration, good expansibility and security. At the same time, this solution can also be used in other similar hardware environment for its high portability. References [1] Texas Instruments, TMS320C6A816x C6-Integra DSP+ARM Processors Technical Reference Manual, Texas, 2011. [2] Xilinx Inc., Virtex-6 FPGA Configuration User Guide, 2013.

CHAPTER 5: System Test, Diagnosis, Detection and Monitoring, Instrumentation and Measurement, Optimization and Algorithms, Numerical Methods and Simulation

Applied Mechanics and Materials Vol. 721 (2015) pp 355-359 © (2015) Trans Tech Publications, Switzerland doi:10.4028/www.scientific.net/AMM.721.355

Submitted: 01.10.2014 Accepted: 13.10.2014

Reliability Evaluation for Distribution System Considering the Access of Distributed Generations Hongshan Zhao1, a, Song Chen1, b, Yingying Wang1, c, Ying Wang2, d 1

School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, Hebei Province, China; 2

Economic and Technology Research Institute, State Grid Electric Power Company of Hebei Province, 050021, China

a

b

c

d

[email protected], [email protected], [email protected], [email protected]

Keywords: distributed generations; the probability of adequacy of island; distribution network; reliability evaluation

Abstract. The construction and operating mode of distribution network have changed due to the access of distributed generations (DGs), which has a deep influence on reliability evaluation. A new approach and the related analytical formulation is proposed in this paper to get a better evaluation for distribution network connected to DGs, after considering the fault reconstruction, switch type, DGs types and access locations, islands abundance probabilities and other factors. We propose a classification that includes various cases defined by the relative position of load points (LPs), faults, and switches, in networks with and without DGs. Taking IEEE RBTS BUS6 distribution system for example, the simulation results in the proposed method indicate that DGs improved the reliability for distribution network effectively. Introduction Renewable energy is an effective way to solve the world's energy problems since it is abundant and friendly to the environment [1]. Recently, renewable energy development in the national economy has become increasingly prominent with the transformation and upgrading of China's economic structure. At the same time, wind power, solar power and other renewable distributed generations (DGs) of the large number of access has changed the structure and functioning of traditional distribution networks. At present, the research for reliability assessment of distribution network containing DGs by domestic and foreign scholars focuses on renewable energy modeling considering DGs properties [2], the divided and operational strategies of island [3], the load reduction process [4], and the reliability evaluation algorithms and so on. In this paper, Analytical expressions for the reliability indexes of load points are proposed considering fault reconstruction, switch type, DGs types and access locations, islands abundance probabilities and other factors. We propose a classification that includes various cases defined by the relative position of load points (LPs), faults, and switches, in networks with and without DGs. At last, taking IEEE RBTS BUS6 system for example, the reliability evaluation results demonstrate the effectiveness of the method proposed in this paper. Analytical Algorithms of Distribution System Reliability Assessment Reliability index of distribution network is divided into load point indexes and system indexes [5]. Load point reliability indices include load point average failure rate λi , Load point average outage time Ui . Typical distribution system reliability are system average interruption frequency index (SAIFI), system average interruption duration index (SAIDI), customer average interruption duration index (CAIDI), average service availability index (ASAI), energy not supplied (ENS), average service unavailability index (ASUI), and average energy not supplied (AENS).

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Reliability Assessment of Distribution Network without Considering the Access of DGs OF DGS. First, let us consider a distribution system without any DGs. A branch failure can affect an LP in five different ways (Cases 1-5).

Fig.1. Example distribution network used to illustrate the possible cases. Case 1: If no switch is located in the network between the fault k and LP i (e.g., for i =12 and k =11 in Fig. 1), then the LP i is left unsupplied for a time equal to branch repair time tR,k . Hence, λi ,k = f k ; Ui ,k = f k tR,k . (1) Where fk is the failure rate of the branch k . Case 2: If at least one circuit breaker switch (CBS) j is placed in the distribution system between the fault k and LP i (e.g., for i =12, k =14 and j =13 in Fig. 1) and it is not placed between the primary substation (PS) and the LP i , then the fault does not affect the supply for the LP i . Hence, λi ,k = 0 ; Ui ,k = 0 . (2) Case 3: If there are several CBSs between the fault k and LP i and they are all placed between the PS and the LP i only, then the LP i is left unsupplied for a time equal to tR,k . Hence, λi ,k = f k ; Ui ,k = f k tR,k . (3) Calling j the CBS closest to the fault k among the ones mentioned above, (3) is applied when: if no sectionalizer is installed between CBS j and the fault (e.g., for i =12， k =7 and j =4 in Fig. 1); there are several sectionalizers between CBS j and the fault and they are all located between the CBS j and the PS (Subcase 3.1,e.g., for i =12， k =2 , j =4 and sectionalizer 3 in Fig. 1).Otherwise, at least one sectionalizer between the CBS j and the fault is not located between the CBS j and PS (Subcase 3.2, for i =12， k =9， j =4, and sectionalizer 8 in Fig. 1), and (4) is applied in this situation. Case 4: If no CBS is placed between the fault k and LP i , but there is at least one sectionalzer in that position and it is not placed between PS and the LP (e.g. for i =12， k =16， j =15), then the LP i is left unsupplied for a time equal to tS , which is fault isolation/load transfer time. Hence, λi ,k = f k ; Ui ,k = f k tS (4) If no CBS is placed between the fault k and LP i , but there are several sectionalzers in that position and they are all placed between PS and the LP (e.g. for i =12，k =6，and sectionalizer 11 in Fig.1), then the LP i is left unsupplied for a time equal to branch repair time tR,k . Hence, λi ,k = f k ; Ui ,k = f k tR,k . (5) Reliability Assessment of Distribution Network Considering the Access of DGs. The access of DGs has no influence on the power supply in case 1, 2 and 4. In the following, we will discuss the influence on the outage of load supply by the access of DGs in case 3 and 5. In case 3, the CBS j trips after a fault occurs ( j is the CBS closest to the fault) and island j is formed at the same time; at this point, the supply of the loads in the island powered by the internal DGs is determined by the adequacy probability of the island, denoted as ρ A, j . Hence: λi ,k = f k (1 − ρ A, j ) ; Ui ,k = f k (1 − ρ A, j )tR ,k (6) The formulation (6) is only applied if no sectionalizer is placed between the CBS j and the fault. In subcase 3.1(all the sectionalizers between CBS j and the fault are placed between the CBS j and the PS), the CBS j trips after a fault occurs, island j is formed at the same time; then open the sectionalizer

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closest to the fault (denoted as sc ) and close the CBS j ,we find that the island j changes to island sc . The island sc is formed through two periods, one is the switching time of sectionalizer sc tS ,sc and the other one is the re-enter time of DGs t AV ,sc . In other words, this island is formed after a time equal to tS ,sc + t AV ,sc . The LP i belongs to island j before the CBS j lose; however, the LP belongs to island sc while the CBS close. Therefore the interruption influence on LP i by the fault k is determined by both ρ A, j and ρ A,sc (adequacy probability of the islands) Hence, λi ,k = fk [(1 − ρ A, j ) + ρ A, j (1 − ρ A,sc )] ; Ui ,k = fk [(1 − ρ A, j )(tS ,sc + t AV ,sc ) + (1 − ρ A,sc )(tR,k − tS ,sc − t AV ,sc )] (7) In subcase 3.2(at least one sectionalizer between the CBS j and the fault is not located between the CBS j and PS), the CBS j trips after a fault occurs and forms island j . Then open the sectionalizer sc closest to the fault in order to isolate the fault, and thus the LP i continues to be supplied by the PS. Hence, the analytical formulation taking into account these considerations in subcase 3.1 is λi ,k = fk (1 − ρ A, j ) ; Ui ,k = fk (1 − ρ A, j )tR,sc (8) In case 5, the LP i will have to suffer outage for once whether the access of DGs or not. Island j is formed while open the sectionalizer j due to the fault; the improvement of reliability of LP i thanks to the access of DGs is reflected during the time equals to (tS , j + tAV , j , tR,k ) since the LP i continues to be supplied by the PS after the fault is repaired. Hence, in this condition λi ,k = fk ; Ui ,k = f k [tS , j + t AV , j + (1 − ρ A, j )(tR ,k − tS , j − t AV , j )] (9) The Equivalent of Branch (Nod) in Distribution Network. It can be more efficient to treat a set of elements as a unit in reliability evaluation. A set of elements controlled by the same switch could be seen as an equivalent element j (EQE); similarly, a set of nods controlled by the same switch could be seen as an equivalent node j (EQN).The equivalent branch failure rate f EQ, j and repair time of EQE tREQ, j must be computed as following: nj

nj

f EQ, j = ∑ f k k =1

;

∑ f k t R ,k

tREQ, j = k =1 f EQ, j

(10)

Probabilistic Models of DGs and loads The output power of renewable DGs is determined by not only the variations of load demand and their capacity limit, but also their primary energy (such as wind speed, light intensity, etc.). In this paper, we use the probabilistic model of renewable DG reported in [6]. The renewable DGs and LPs are characterized by an annual load model presenting several power demand levels linked to their probabilities, as shown in table 1 and table 3. Case Study The case study is based on IEEE RBTS BUS6 system [7-8], in which several DGs are added, the system include one 10 kV bus and 4 feeder outlets (F1, F2, F3, F4). As is shown in figure 2, F1 and F2 contact with each other through a NOS. The failure rates of feeder sections and distribution transformer are 0.065 and 0.013 respectively; the case doesn’t take the switch fault into account. Load data the case used is reported in [7]. The repair time of element tR,k =5 hours and the fault isolation time or load transfer time tS =1hours. The generator availability probability ρ AV ,d =0.98 and time to be available t AV , j =0.08 hour are considered for the DGs. Table 1 shows the probability model for a load, table 2 shows the model for a conventional DG, and table 3 shows the probability models for renewable DG. Table 4 shows the reliability indexes of feeders and the system.

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Tab.1 Probability model for a load Level, l

% of LP

ρdL,l

1

20

0.22

2

40

0.21

3

60

0.25

4

80

0.24

5

100

0.08

Tab.2 Probability model for a conventional DG Level, l 1 2

Power (kW) 0 3000

ρdC,l

0 1

Tab.3 Probability models for renewable DGs Power (kW) 0

0.15

2

250

0.22

3

500

0.21

4

750

0.28

Fig.2. IEEE RBTS BUS6 system after 5 1000 rebuilding Tab. 4 Reliability indexes of feeders and the system Distribution network F1 F2 F3 F4 System

SAIFI DGs no DGs 0.6078 0.6078 0.7248 0.7248 0.3932 0.3932 1.3450 2.5886 0.8491 1.1795

SAIDI DGs no DGs 1.0129 1.0129 1.1389 1.1389 1.0112 1.3813 5.2697 12.9079 2.1950 4.2276

ρiR,l

Level, l 1

CAIDI DGs no DGs 1.6674 1.6674 1.5706 1.5706 2.6058 3.5124 3.9178 4.9864 2.5851 3.5842

0.14

ENS DGs no DGs 7864.00 7864.00 9368.40 9368.40 7259.72 9948.48 33014.31 64175.04 57506.43 91355.91

It can be seen from table 4 that each reliability index is improved effectively due to the access of DGs. However, each index belongs to various feeders obtains a different improvement: 1) F1 and F2 have the same reliability indexes before and after the access of DGs, for these two feeders contact with each other through a NOS and the adequacy probability of islands formed due to fault in F1 and F2 equal to 1, which means the same condition whether taking the DGs into account or not; 2) All the reliability indexes of F3 are improved in various degrees except for the SAIFI index. We can seen from table 4 that all the SAIFI indexes in F1, F2 and F3 have not changed, since there is no other CBS in those feeders except for the CBS connected to PS. The islands formed in these feeders would only affect the outage duration, but could not reduce the times of outages. 3) The reliability indexes of F4 are all improved, for the DGs in the islands act as backup power in this situation, which leads to an obvious improvement in each reliability index. Conclusion The mass access of DGs has a deep influence on reliability assessment of distribution system. Analytical expressions for the reliability indexes of load points are proposed in this paper, which considering fault reconstruction, switch type, DGs types and access locations, islands abundance probabilities and other factors. We propose a classification that includes various cases defined by the relative position of load points (LPs), faults, and switches, in networks with and without DGs. Finally, taking IEEE RBTS BUS6 system for example, the reliability evaluation results demonstrate the effectiveness of the method proposed in this paper.

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References [1] Bie Zhaohong, Li Gengfeng, Wang Xifan．Review on reliability evaluation of new distribution system with micro-grid[J]. Electric Power Automation Equipment, 2011, 31(1):1-6(in Chinese). [2] ChenCan, Wu Wenchuan, Zhang Boming, Qin Jianguang, Guo Zijian. An active distribution system reliability evaluation method based on multiple scenarios technique [J]. Proceedings of CSEE, 2012, 32(34):67-73(in Chinese). [3] ATWA Y M, EL-SAADANY E F. Evaluation for distribution system with renewable distributed generation during islanded mode of operation [J]. IEEE Trans on Power System, 2009, 24(2):572-581. [4] Xu Yuqin, Wu Yingchao. Reliability evaluation for distribution system connected with wind-turbine generators [J]. Power System Technology, 2011, 35(4):154-158(in Chinese). [5] IEEE STD 1366~2012. IEEE Guide for Electric Power Distribution Reliability Indices[S]. [6] R. Karki, P. Hu, and R. Billinton,A simplified wind power generation model for reliability evaluation. IEEE Trans. Energy Convers, 2006, 21(2):533–540. [7] R. N. Allan, R. Billinton, I. Sjarief. A Reliability Test System for Educational Purposes-basic Distribution System Data and Results. IEEE Transactions on Power Systems, 1991, 6(2):813-830. [8] R. Billinton, S. Johnnavithula. A Test System for Teaching Overall Power System Reliability Assessment. IEEE Transactions on Power Systems, 1996, 11(4):1670-1676.

Applied Mechanics and Materials Vol. 721 (2015) pp 360-365 © (2015) Trans Tech Publications, Switzerland doi:10.4028/www.scientific.net/AMM.721.360

Submitted: 01.10.2014 Accepted: 22.10.2014

Transformer Fault Diagnosis Based On Online Sequential Extreme Learning Machine Linlin Wang, Fei Pei and Yongli Zhu School of North China Electric Power University, Heibei 10079, China [email protected] Keywords: Online Sequential Extreme Learning Machine, Ensemble of online sequential extreme learning machine, transformer fault diagnosis, on-line monitoring.

Abstract. Experiment analyzed the main factors that affect the performance of Online Sequential Extreme Learning Machine (OS-ELM). And the experimental comparison show that the OS-ELM in classification performance is better than the support vector machine (SVM) and extreme learning machine (ELM). But there still is not a stable network output now. For this aspect, this article presents the optimization algorithm of integrated Ensemble of online sequential extreme learning machine (EOS-ELM). The algorithm using a limited number of sample data has been applied to transformer fault diagnosis. The time of training and testing can be shortened and the classification accuracy can be improved. The experimental results show that the OS-ELM has better performance in response to online monitoring and real-time data processing. Introduction Power system in our country is heading for the super high voltage, large capacity and nationwide interconnection. Scale will also continue to expand and the number and capacity of the transformer will be further improved. For the development of the super high voltage and extra high voltage substation system, the key equipment in power grid demand the higher safety and reliability. Transformer as one of the main equipment in the power system, its fault may do great harm and produce great influence to power system and the users. At present some super high voltage transformers installed some sensors such as oil chromatogram, vibration, oil temperature and so on. And then these transformers achieved a certain level of on-line monitoring. In the real environment, sensor data often cannot be one-time collected. Data collection and processing is an online process. Based on the demand, this project aims to research on transformer fault diagnosis real-time response data change method. This research method is helpful for power supply enterprise transformer maintenance personnel timely and accurately to find the transformer latent fault. There were many traditional transformer fault diagnosis intelligent methods, for example, IEC recommended three ratio method, Rogers and Dornerburg method etc.. But coding boundary is too absolute and coding need be artificially drawn in the traditional threshold detection means. There is some defects especially lack of encoding[1,2]; At the same time, emerging intelligent methods such as BP neural network, SVM, a bayesian network method and so on, are facing the main problems example high intensity human interference, learning speed slowly, poor learning extensibility and a large sample of demand etc. The existing DGA data cannot meet the demand of their training [3-6]. When applied to the responsively demanding online program, these emerging intelligent methods are often inefficient. In order to improve the overall performance of the building network, ELM algorithm was put forward by Huang G.B. [7,8]. ELM is a kind of fast single hidden layer neural network training algorithm. Using the algorithm in the process of network parameters, the parameters of the node(right and offset value)is randomly selected without having to adjust; and network to outside is by minimizing the squared loss function of the least squares solution. In the process of these network parameters without any iteration steps, which greatly reduces the network parameters adjusting time. In a real environment, some training samples possibly cannot one-time collected so that online learning in

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general is an online process. In traditional learning algorithm, when new data is gotten ,stale data often repeat training with new data, which will waste a lot of time. In order to solve this problem, Nan - Ying, Liang, Guang - Bin Huang put forward online sequence Learning OS-ELM algorithm [9].The method adopts the way of partitioned matrix effectively to avoid the repeated training data and to a great extent to improve the efficiency of learning[10-13].In this paper, the online sequential extreme learning machine algorithm is applied to transformer fault diagnosis, and the OS-ELM algorithm hidden layer parameters caused by the random network output is not stable and the thought of integration is applied to online sequence ELM algorithm optimization. Finally the feasibility and superiority of this method are verified through the comparison and analysis. Organization of the Text OS-ELM introduces. Online sequence extreme learning machine (OS-ELM) is a single hidden layer forward neural network (SLFN) training algorithm which can be applied to some regression and classification tasks. The method adopts the way of partitioned matrix effectively to avoid the repeated training data and to a great extent improve the efficiency of learning. OS-ELM algorithm described as follows: Given training data set ℵ the hidden layer output function G(ai,bi,x) and the number of hidden layer nodes L. Step1 Initialization phase: choose the part of the data set ℵ0 form the ℵ,whereN0≥L a)Randomly selected input weights ai and threshold of hidden layer node bi,i=1,…L; b)Calculate the output matrix In the hidden layer H0 c)Calculate the initial output weightsβ0 d)make k=0 Step 2 Sequence learning phase: set the k+1 step to add the data block as ℵk+1. a) Calculate output matrix of the hidden layer Hk+1, after the new data to add. b) make c) to calculate the output weightsβk+1. d) make k=k+1, return to Step 2. When N0=N, the OS-ELM algorithm is equivalent to the original ELM algorithm. The OS-ELM algorithm can not only study data one by one, but also can study data a batch after batch. And after these data are learned, the OS-ELM algorithm will immediately give up having learned data. The transformer fault diagnosis based on OS-ELM. Choice of the characteristics. This article selects IEC recommended DGA data H2,CH4,C2H6,C2H4 and C2H2 five gases dissolved amount as the input of OS-ELM algorithm. Since the DGA data value distribution range is very large, even though there may be larger differences between the same types of data, in order to reduce its influence due to differences in values between each other. Before the characteristics are input in classifier, data must be normalized to process according to formula . (1) Xnormalized is a gas concentration values after data is normalized to process. xmin is Minimum gas content. xmax is Maximum gas content. Up and Lo are respectively upper bound and lower bound of the normalization and respectively value 1 and -1. The gas sample selection and transformer status code. In transformer DGA data, the fault samples belong to minority class samples. In the original data has two unbalanced phenomenon: the unbalanced phenomenon between normal samples and fault samples and the unbalanced phenomenon between the various fault samples. We should select samples to keep a balance of samples. In this

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experiment, the data collection of HengShui city, HeBei province power bureau and other relevant literature of the 268 groups of data, according to the proportion of 3:1 is divided into training samples and testing samples. Because the transformer fault diagnosis is more than a classification task and OS-ELM algorithm has more classification ability, a classifier transformer status categories can divided six kinds of state into normal, partial discharge, low discharge, overheating in low-temperature, high temperature and high energy discharge superheat. OS-ELM output is defined as a vector classification and classification of vector dimension is defined as the number of samples status categories. This algorithm should code for each state in the application. For the table 1, Table 1 Transformers state code table Status Category State vector Normal (N) (0,0,0,0,0,1) Partial Discharge(PD) (0,0,0,0,1,0) Low energy discharge(D1) (0,0,0,1,0,0) High energy discharge(D2) (0,0,1,0,0,0) Low temperature thermal(T12) (0,1,0,0,0,0) Super heater(T3) (1,0,0,0,0,0) The selection of OS-ELM algorithm parameters. The OS-ELM algorithm used in the experiment on the number of parameters need hid artificially layer neurons L and select the data set the size of NO for the first training time, the size of the data blocks BLOCK in learning process be set as well. In the case of BLOCK and NO being decided (NO=100, BLOCK=10), classification accuracy changing on L is shown as figure 1(a). From figure 1(a) training time vary with the number of the hidden layer neurons, you can see that when NO>L, classification accuracy is improving with the number of hidden layer neurons increasing, and when NO L, namely the initial stage of initial training data is greater than the number of hidden layer neurons number, in the later stage, the training accuracy vibrate dramatically and decline with the number of hidden layer neurons more than the initial amount of training data.

Fig. 3 ELM and OS-ELM Fig. 4 ELM and OS-ELM training accuracy comparison chart training time comparison chart In terms of the training accuracy, OS-ELM and ELM even a slight increase. Figure 4 show that in terms of training time, the OS-ELM algorithm requires less time than ELM algorithm. As shown in table 2,from several common algorithms for the performance comparison of the training time, testing time and accuracy, it can be seen that the OS-ELM is better than the SVM and ELM in terms of training time, test time and accuracy. Table 2 The SVM, ELM, OS-ELM algorithm comparison Algorithm SVM ELM OS-ELM

Parameters C=500 =0.5 L=100 L=100 NO=160 Block=10

Training time/s

Testing time/s

Accuracy%

0.5347

0.0109

84.35

0.0624

0.0156

86.36

0.0156

0.0031

87.37

Conclusion Firstly, this paper analyzes the influence of the OS-ELM algorithm performance about the different parameters and the activation function through the experiment, and the OS-ELM method is applied to transformer fault diagnosis. Secondly, through the analysis of the OS -ELM, ELM and SVM intelligent methods, the OS -ELM has more obvious advantages in the terms of training time, test time and accuracy. Thirdly, when the OS-ELM algorithm randomly select hidden layer parameters, network output is usually not stable and the EOS-ELM is applied in fault diagnosis to improve its stability. The experimental results show that the OS-ELM method has faster response speed and higher efficiency for on-line monitoring and online processing data.

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References [1] P Mirowski, Y LeCun. Statistical Machine Learning and Dissolved Gas Analysis: A Review [J].IEEE Transactions on Power Delivery,2010,27(4):1-6. [2] Ronaldo R.B.de Aquino, Milde M.S.Lira.A fuzzy system for detection of incipient fault in power transformers based on gas-in-oil analysis[C]. Fuzzy Systems (FUZZ), 2010 IEEE International Conference on,2010.7:1-6. [3] Du Wenxia,Lv Feng,Ju Xiyuan, et al. Fault Diagnosis of Power Transformer Based on BP Neural Network[J]. Transformer, 2007,44(3):45-47. [4] Li Shuang,Wang Langzhu,Zhang Wei,et al.Fault Diagnosis Method of Transformer Based on Improved BP Neural Network of DGA[J].Transformer,2010,47(12):61-65. [5] Zhao Wenqing,Zhu Yongli,Wang Xiaohui. Fault Diagnosis of Power Transformer Based on Combination of Bayesian Networks[J].Electric Power Automation Equipment, 2009,29(11):6-9. [6] Li Chunxiang,Zhang Weimin,Zhong Biliang,et al.Reserach On Parameter Optimization Algorithm of Least Squares Support Vector Machine[J].Journal of Hangzhou Dianzi University, 2010,30(4):213-216.DOI:10.3969/j.issn.1001-9146.2010.0406 [7] Huang G B,Zhu Q Y,Siew C K.Extreme Application[J].Neurocomputing,2006,70(1/2/3):489-501.

Leraning

Maching:Theory

and

[8] Huang G B,Zhu Q Y,Siew C K. Extreme Learning Machine:A New Learning Scheme of Feedforward Neural Networks[C].Proceedings of the International Joint Conference on Neural Networks.Piscataway:Institute of Electrical and Electronics Engineers Inc,2004:985-990. [9] Liang Nanying,Huang Guang-bin,SaratchandranP,et al.A fast and accurate online sequential learning algorithm for feedforward networks[J]. IEEE Transaction on Neural Networks, 2006, 17(6):1411-1423. [10] Lan Y, Soh Y C, Huang G B. Ensemble of sequential extreme learning machine[J].Neural Computation, 2009,72:3391-3395. [11] Zhao J W, Wang Z H, Park D S.Online sequential extreme learning machine with forgetting mechanism[J]. Neural Computation，2012，87: 79-89. [12] Huynh H T, Won Y. Regularized online sequential learning algorithm for single-hidden layer feedforward neural networks[J]. Pattern Recognition Letters, 2011,32:1930-1935. [13] Hoang M T T, Huynh H T, Vo N H, et,al. A robust online sequential extreme learning machine[J].LNCS,2007,4491:1077-1086. [14] Yuan Lan,Yeng Chai Soh,Guang-Bin Huang. Ensemble of online sequential extreme learning machine[J].Neurocomputing,2009,72(13/14/15): 3391-3395.

Applied Mechanics and Materials Vol. 721 (2015) pp 366-369 © (2015) Trans Tech Publications, Switzerland doi:10.4028/www.scientific.net/AMM.721.366

Submitted: 07.10.2014 Revised: 30.10.2014 Accepted: 01.11.2014

Analysis of a Nonlinear System with a Random Parameter Honggang Danga, Xiaoya Yang, Wansheng He School of Mathematics and statistics, Tianshui Normal University, Tianshui, China a

[email protected]

Keywords: random parameter, nonlinear system, Laguerre polynomial approximation.

Abstract. In this paper, a nonlinear system with random parameter, which is called stochastic fractional-order complex Lorenz system, is investigated. The Laguerre polynomial approximation method is used to study the system. Then, the stochastic fractional-order system is reduced into the equivalent deterministic one with Laguerre approximation. The ensemble mean and sample responses of the stochastic system can be obtained. Introduction The history of fractional calculus is similar to the classical derivatives. However, its development is very rapid just in recent several decades due to the application in physics, engineering, secure communications and so on. Meanwhile, it has been found that many fractional-order systems can demonstrate chaotic behavior, such as fractional-order Chua circuit, fractional-order Lorenz system, fractional-order Chen system, and so on. These examples and many other similar samples perfectly clarify the importance of consideration and analysis of dynamical systems with fractional-order models. As research continues, the importance of stochastic fractional-order systems is realized by many researchers. It is worth noting that there exist many nonlinear dynamical systems with complex variables in practical applications. These systems can be widely applied to describe a variety of physical phenomena, for example, the atomic polarization amplitudes, electric field, population inversion, detuned laser systems, and thermal convection of liquid flows, etc [1-5]. Recently, Mahmoud et al investigated the dynamics of several integer-order chaotic systems with complex variables [6]. However, the study about stochastic characteristic for the fractional-order complex systems with random parameters is very few. System description In this paper, the fractional-order complex Lorenz system with a random parameter will be investigated. The stochastic fractional-order complex system is: D q y = a( y − y ) 1 2 1 q D y2 = by1 − y2 − y1 y3 , (1) 1 q D y3 = ( y1 y2 + y1 y2 ) − cy3 2 where y1 , y2 , y3 are state variables. y1 = x + iy, y2 = z + iv are defined in complex region, y3 = w is a real state variable, and i = −1 . b is a random parameter for the system, and can be written as b = b + δ u , when u is a random variable defined on [0, +∞) obeys the exponential distribution. Firstly, the complex variables of the system are separated into real and imaginary parts, respectively. For the linearity property of the Caputo derivative, the system (1) is rewritten as

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D q x = a ( z − x), D q y = a (v − y ) q q D z = bx − z − xw, D v = by − v − yw , (2) q D w = xz + yv − cw For the convenience of the system (2), we will use it as the replacement of the system (1).The stochastic fractional-order system (2) is reduced into the equivalent deterministic system via the orthogonal polynomial expansion. The responses of system (2) are the functions of time t and the random variable u . Therefore, the responses of the system (2) are approximated by the series: N N ( , ) ( ) ( ), ( , ) x t u = x t L u y t u = yi (t ) Li (u ) ∑ ∑ i i i =0 i =0 , (3) N N N z (t , u ) = z (t ) L (u ), v(t , u ) = v (t ) L (u ), w(t , u ) = w (t ) L (u ) ∑ ∑ ∑ i i i i i i i =0 i =0 i =0 where xi (t ), yi (t ), zi (t ), vi (t ), wi (t ) are time functions, Li (u ) means the ith Laguerre polynomial. The N represents the largest order of the polynomials, and when the number N tends to infinite, the left of Eqs. (3) is equal to its right. Then the Eqs. (3) is substituted in to the system (2), we can get

(4)

With the help of the recurrence relationship of the polynomial, the nonlinear terms of the system (4) are rewritten as the linear assembly of Li (u ) , which can be described as follows: N N 2N N N 2N x ( t ) L ( u ) w ( t ) L ( u ) = P ( t ) L ( u ), y ( t ) L ( u ) w ( t ) L ( u ) ∑ i i i i i i ∑ i ∑ i ∑ i ∑ i = ∑ Qi (t ) Li (u ) i =0 i =0 i =0 i =0 i=0 i =0 , (5) N N 2N N N 2N x (t ) L (u ) z (t ) L (u ) = F (t ) L (u ), y (t ) L (u ) v (t ) L (u ) = G (t ) L (u ) i i i i i i i ∑ i ∑ i ∑ i ∑ i ∑ i ∑ i =0 i =0 i =0 i =0 i =0 i =0

where Pi (t ), Qi (t ), Fi (t ), Gi (t ) can be get with the aid of the software Maple. Meanwhile, the terms and

can be expressed as the following form:

N N 2 δ ( ) ( ) = δ u x t L u u ∑ i i ∑ xi (t ) (2i + 1) Li (u ) − i Li −1 (u ) − Li +1 (u ) i =0 i =0 N = δ ∑ Li (t ) (2i + 1) xi (u ) − (i + 1)2 xi −1 (u ) − xi +1 (u ) − LN +1 (u ) xN (t ) i =0 , N N uδ y (t ) L (u ) = uδ y (t ) (2i + 1) L (u ) − i 2 L (u ) − L (u ) i i i i −1 i +1 ∑ i ∑ i =0 i =0 N = δ ∑ Li (t ) (2i + 1) yi (u ) − (i + 1) 2 yi −1 (u ) − yi +1 (u ) − LN +1 (u ) y N (t ) i =0

(6)

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Vehicle, Mechanical and Electrical Engineering

In the paper, x−1 (t ), xN +1 (t ), y−1 (t ), yN +1 (t ) are assumed to zero. Eqs. (5) and (6) are substituted into(4),then

(7)

Owing to the orthogonality of Laguerre polynomials, we can multiply both sides of (7) by Li (u ) , i = 0,1, 2, , N in sequence and taking expectation with respect to u . Based on these, the equivalent deterministic system is obtained. Due to the requirement of computational precision, we choose N = 3 in the following numerical computations. Therefore, the deterministic system is approximated as D q x0 = a( z0 − x0 ), D q y0 = a (v0 − y0 ) q q D z0 = bx0 + δ ( x0 − x1 ) − z0 − P0 , D v0 = by0 + δ ( y0 − y1 ) − v0 − Q0 q q q D w0 = F0 + G0 − cw0 , D x1 = a( z1 − x1 ), D y1 = a(v1 − y1 ) D q z = bx + δ (3x − 4 x − x ) − z − P , D q v = by + δ (3 y − 4 y − y ) − v − Q 1 1 1 2 0 1 1 1 1 1 2 0 1 1 q q q . (8) D w1 = F1 + G1 − cw1 , D x2 = a( z2 − x2 ), D y2 = a(v2 − y2 ) q q D z2 = bx2 + δ (5 x2 − 9 x3 − x1 ) − z2 − P2 , D v2 = by2 + δ (5 y2 − 9 y3 − y1 ) − v2 − Q2 D q w = F + G − cw , D q x = a( z − x ), D q y = a (v − y ) 2 2 2 2 3 3 3 3 3 3 q q D z3 = bx3 + δ (7 x3 − x2 ) − z3 − P3 , D v3 = by3 + δ (7 y3 − y2 ) − v3 − Q3 q D w3 = F3 + G3 − cw3 The numerical solutions xi , yi , zi , vi , wi , (i = 0,1, 2,3) of system (8) can be computed by the improved predictor-corrector algorithm for fractional differential equations. Therefore, the approximate random responses of the stochastic system (2) are 3 3 x ( t , u ) ≈ x ( t ) L ( u ), y ( t , u ) ≈ yi (t ) Li (u ) ∑ ∑ i i i =0 i =0 . (9) 3 3 3 z (t , u ) ≈ z (t ) L (u ), v(t , u ) ≈ v (t ) L (u ), w(t , u ) ≈ w (t ) L (u ) ∑ ∑ ∑ i i i i i i i =0 i =0 i =0 Meanwhile, we get the ensemble mean responses for the stochastic system 3 3 E [ x ( t , u )] ≈ x ( t ) E [ L ( u )] = x ( t ), E [ y ( t , u )] ≈ yi (t ) E[ Li (u )] = y0 (t ) ∑ ∑ i i 0 i =0 i =0 3 3 E [ z ( t , u )] ≈ z ( t ) E [ L ( u )] = z ( t ), E [ v ( t , u )] ≈ vi (t ) E[ Li (u )] = v0 (t ) . (10) ∑ ∑ i i 0 i =0 i =0 3 E[ w(t , u )] ≈ ∑ wi (t ) E[ Li (u )] = w0 (t ) i =0

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When u = 0 , namely b = b , this sample system is very important for reference, called a mean parameter system, the responses are approximated as 3 x ( t , 0) ≈ xi (t ) Li (0) = x0 (t ) + x1 (t ) + 2 x2 (t ) + 6 x3 (t ), y (t , 0) ∑ i =0 3 ≈ yi (t ) Li (0) = y0 (t ) + y1 (t ) + 2 y2 (t ) + 6 y3 (t ) ∑ 0 i = 3 (11) z (t , 0) ≈ ∑ zi (t ) Li (0) = z0 (t ) + z1 (t ) + 2 z2 (t ) + 6 z3 (t ), v(t , 0) . i =0 3 ≈ ∑ vi (t ) Li (0) = v0 (t ) + v1 (t ) + 2v2 (t ) + 6v3 (t ) i =0 3 w(t , 0) ≈ ∑ wi (t ) Li (0) = w0 (t ) + w1 (t ) + 2 w2 (t ) + 6 w3 (t ) i =0 Conclusions In this paper, the dynamics of a stochastic fractional-order complex Lorenz system is analyzed. According to the approximation principle of Laguerre orthogonal polynomial method, the equivalent deterministic system is obtained. We can get the ensemble mean and sample responses through the investigation of the approximated equivalent deterministic system. References [1] G. Mahmoud, T. Bountis : The dynamics of systems of complex nonlinear oscillators: a review. Int. J. Bifurcation chaos, 14 (2004): 3821-3846. [2] M. Moghtadaei and M.H. Golpayegani Complex dynamic behaviors of the complex Lorenz system. Scientia Iranica 19(2012): 733-738. [3] G. Mahmoud, S. Aly, M. AL-Kashif: Dynamical properties and chaos synchronization of a new chaotic complex system. Nonlinear Dynamics, 51(2008): 171-181. [4] C. Ning, H. Hake : Detuned laser and the complex Lorenz equations-subcritical and supercritical hopf bifurcations. Physical Review A, 41(1990): 591-598. [5] G. Mahmoud, E. Mahmoud, M. Ahmed: On the hyperchaotic complex Lorenz system. Nonlinear Dynamics, 58 (2009): 725-738.

Applied Mechanics and Materials Vol. 721 (2015) pp 370-373 © (2015) Trans Tech Publications, Switzerland doi:10.4028/www.scientific.net/AMM.721.370

Submitted: 07.10.2014 Accepted: 13.10.2014

The Research on Flow Calculation Method of the Ball Solenoid Valve Yi Yang, Liang Chu, Di Fan, Yuting Huanga State Key Laboratory of Automobile Simulation and Control, Jilin University, Changchun, China a

[email protected]

Keywords: Ball Solenoid Valve, Flow Calculation, Matlab/Simulink

Abstract. This paper proposes a flow calculation method of the ball solenoid valve, by measuring diameter of the input valve spool, we can estimate the rated flow of the solenoid valve. Aiming at the calculation method, we have built a MATLAB/Simulink model to calculate the valve flow, and we also validated the model by the flow demand of one type of RBS system. Introduction Recently, for the advantages of simple structure, fast response, good stability and so on, ball solenoid valve has been used in various control fields [1, 2], so the condition of ball solenoid valve directly determines the performance of the system. The rated flow is not only one of the important parameters on designing or manufacturing solenoid valves, but also an important indicator in matching other parameters of ball solenoid valve. In this paper, we proposes a flow calculation method of the ball solenoid valve, just using the diameter of the input valve spool, we can estimate the rated flow of the solenoid valve, and this can provide a theoretic foundation for the matching of the ball solenoid valve. Calculation model of the valve flow With reference to the solenoid valve flow characteristic equation [3]: (1) Where, –Different pressure of valve port export, –Fluid density, –The area of fluid passing through, Discharge Coefficient. We can find that, the flow not only associates with differential pressure of valve port export, but also relates to valve port area and discharge coefficient. The following, we will focus on these two parameters and propose the calculation model of the valve flow. Area model of solenoid valve port. Figure 1 shows a schematic diagram of ball solenoid valve port.

Fig.1 Schematic diagram of ball solenoid valve port The flow of solenoid valve depends on the area size that allows the liquid pass it. In figure 1, we can see the flow of ball solenoid valve divided into two segments [4], as is shown in figure 2. In figure 2, we can find that when the spool displacement x