Rail profile optimization is a critical strategy for mitigating wear and extending service life.However,damage at the wheel-rail contact surface goes beyond simple rail wear,as it also involves fatigue phenomena.Focus...Rail profile optimization is a critical strategy for mitigating wear and extending service life.However,damage at the wheel-rail contact surface goes beyond simple rail wear,as it also involves fatigue phenomena.Focusing solely on wear and not addressing fatigue in profile optimization can lead to the propagation of rail cracks,the peeling of material off the rail,and even rail fractures.Therefore,we propose an optimization approach that balances rail wear and fatigue for heavy-haul railway rails to mitigate rail fatigue damage.Initially,we performed a field investigation to acquire essential data and understand the characteristics of track damage.Based on theory and measured data,a simulation model for wear and fatigue was then established.Subsequently,the control points of the rail profile according to cubic non-uniform rational B-spline(NURBS)theory were set as the research variables.The rail’s wear rate and fatigue crack propagation rate were adopted as the objective functions.A multi-objective,multi-variable,and multi-constraint nonlinear optimization model was then constructed,specifically using a Levenberg Marquardt-back propagation neural network as optimized by the particle swarm optimization algorithm(PSO-LM-BP neural network).Ultimately,optimal solutions from the model were identified using a chaos microvariation adaptive genetic algorithm,and the effectiveness of the optimization was validated using a dynamics model and a rail damage model.展开更多
This research aimed to overcome challenges such as high costs,lengthy optimization time,and low efficiency in resolving issues related to wheel-rail contact,rail wear,and vehicle dynamics.Based on the wheel-rail conta...This research aimed to overcome challenges such as high costs,lengthy optimization time,and low efficiency in resolving issues related to wheel-rail contact,rail wear,and vehicle dynamics.Based on the wheel-rail contact parameters,an optimal design method for rail grinding target profile is proposed from wear profile measurement to grinding profile design according to the actual railway track and vehicle operating conditions.We utilized Isight to create a simulation test and developed an RBF proxy model that incorporated both mechanical and geometric aspects of wheel-rail contact.By integrating rail modeling,wheel-rail contact analysis,and multi-objective optimization,we established a rail grinding optimization model that was solved using the NSGA-II algorithm.After optimization,the study achieved a 31.863%reduction in average contact stress,a 70.5%reduction in matching wear work,and a 100.391%increase in the difference in rolling radius between the wheel and rail.展开更多
基金supported by the National Natural Science Foundation of China(No.52388102)the Sichuan Science and Technology Program(No.2023ZDZX0008)China.The authors would like to thank the Guoneng Shuo-Huang Railway Development Company,China for providing vehicle parameters and line data for this project.The authors would also like to acknowledge the Xplorer Prize for sponsoring the project.
文摘Rail profile optimization is a critical strategy for mitigating wear and extending service life.However,damage at the wheel-rail contact surface goes beyond simple rail wear,as it also involves fatigue phenomena.Focusing solely on wear and not addressing fatigue in profile optimization can lead to the propagation of rail cracks,the peeling of material off the rail,and even rail fractures.Therefore,we propose an optimization approach that balances rail wear and fatigue for heavy-haul railway rails to mitigate rail fatigue damage.Initially,we performed a field investigation to acquire essential data and understand the characteristics of track damage.Based on theory and measured data,a simulation model for wear and fatigue was then established.Subsequently,the control points of the rail profile according to cubic non-uniform rational B-spline(NURBS)theory were set as the research variables.The rail’s wear rate and fatigue crack propagation rate were adopted as the objective functions.A multi-objective,multi-variable,and multi-constraint nonlinear optimization model was then constructed,specifically using a Levenberg Marquardt-back propagation neural network as optimized by the particle swarm optimization algorithm(PSO-LM-BP neural network).Ultimately,optimal solutions from the model were identified using a chaos microvariation adaptive genetic algorithm,and the effectiveness of the optimization was validated using a dynamics model and a rail damage model.
基金Supported by Fundamental Research Funds for the Central Universities(Grant No.2019JBM050).
文摘This research aimed to overcome challenges such as high costs,lengthy optimization time,and low efficiency in resolving issues related to wheel-rail contact,rail wear,and vehicle dynamics.Based on the wheel-rail contact parameters,an optimal design method for rail grinding target profile is proposed from wear profile measurement to grinding profile design according to the actual railway track and vehicle operating conditions.We utilized Isight to create a simulation test and developed an RBF proxy model that incorporated both mechanical and geometric aspects of wheel-rail contact.By integrating rail modeling,wheel-rail contact analysis,and multi-objective optimization,we established a rail grinding optimization model that was solved using the NSGA-II algorithm.After optimization,the study achieved a 31.863%reduction in average contact stress,a 70.5%reduction in matching wear work,and a 100.391%increase in the difference in rolling radius between the wheel and rail.
