Suspension parameter optimal design to enhance stability and wheel wear in high-speed trains
Xiangwang Chen, Longjiang Shen, Xiaoyi Hu, Guang Li, Yuan Yao
Abstract
Ensuring running stability and minimising wheel tread wear are critical factors for enhancing the dynamic performance of high-speed trains. As an efficient method for improving vehicle performances, previous studies on suspension parameter optimisation have not fully considered these two factors. It is essential to pay attention to stability at extreme wheel-rail contact states, and the wheel wear evolution needs to be revealed instead of investigating the wear performance only through an index like wear number. This research aims to investigate the stability/wear Pareto optimisation of bogie suspension parameters for a high-speed passenger train. Stability-related indices are defined as the lateral ride comfort index at low equivalent conicity and the bogie frame acceleration at high conicity. To represent the long-term wheel wear performance, a contact spreading index is proposed and combined with the wear number index. The key suspension parameters are optimised through the genetic algorithm NSGA-II. The obtained Pareto front clearly reveals the relationship between the objective functions. To meet different performance requirements, four distinct matching rules of the suspension parameters are summarised by employing clustering analysis. Finally, the applicability of the parameter matching rules is examined by analysing stability at different equivalent conicities and wheel wear evolution.