A wheel wear prediction model of non-Hertzian wheel-rail contact considering wheelset yaw: Comparison between simulated and field test results

Abstract

The standard model-based approach for predicting wheel wear consists of four main sub-modules: a macro vehicle-track model, a normal wheel-rail contact model, a tangential wheel-rail contact model, and a wear model. In terms of the normal contact model, the few existing studies using non-Hertzian methods to calculate wheel wear rarely consider wheelset yaw, although it is an unavoidable phenomenon when a train is running. Aiming at this issue, a non-Hertzian normal wheel-rail contact model considering wheelset yaw is proposed based on ANALYN and Sun's approximate expression (i.e., ANALYN-YAW). In terms of the wear model, the currently available models were developed for specific wheel/rail materials and under fixed conditions. To extend these models to a new situation, an automatic adjustment strategy based on Kriging surrogate model and particle swarm optimization algorithm (KSM-PSO) is introduced to adjust the wear rate of the wear model developed by the University of Sheffield (USFD model). Finally, an ANALYN-YAW-FaStrip-adjusted USFD-based wheel wear prediction model is proposed, and a comparison of results from simulation and field tests is presented and shows that the predicted result of the ANALYN-YAW-FaStrip-adjusted USFD model is more plausible than that of the ANALYN-FaStrip-adjusted USFD model and the Hertzian-FaStrip-adjusted USFD model.