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

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.

Automated summary

A non-Hertzian normal wheel-rail contact model considering wheelset yaw is proposed, and an automatic adjustment strategy is introduced to adjust the wear rate of the wear model. The results show that the predicted result of the adjusted USFD model is more plausible.