Development of a wear prediction tool for steel railway wheels using three alternative wear functions

When compared with road traffic, railway transportation is safer, more comfortable, less polluting and presents less energy consumption per passenger/km. When compared with the airplane, high speed trains are able to compete for short and medium distances, with the advantage of having better energy efficiency and causing less pollution. However, to maintain the operational performance of railway vehicles, it is necessary that the quality of the wheel–rail contact is controlled, which requires, among others, a good understanding of the wear mechanisms of the wheels and the consequences of their changing profile on vehicle dynamics. In this work, a computational tool that is able to predict the evolution of the wheel profiles for a given railway system, as a function of the distance run, is presented. The strategy adopted consists of a commercial multibody software to study the railway dynamic problem and a purpose-built code for managing its pre and post-processing data in order to compute the wear. Three alternative wear functions are implemented to compute the amount of worn material on the railway wheels. The computational tool is applied here to a realistic operational scenario in order to demonstrate its capabilities on wear prediction. Special attention is given to the comparison of the results obtained with the different wear functions implemented in this work and to the global and local contact models used in such formulations.