Multi-step wear evolution simulation method for the prediction of rail wheel wear and vehicle dynamic performance

Abstract

<jats:p> This paper presents a complete model to estimate the effects of wheel wear on the dynamic behavior and ride comfort of a railway vehicle. A co-simulation of the vehicle dynamics modeled in ADAMS VI-Rail and wear evolution modeled in MATLAB is performed in a loop. The outputs from the vehicle dynamics simulation are used to compute the wear evolution, which in turn affects the vehicle dynamics. The local contact parameters, such as normal contact force, tangential stresses and slip, etc., and wear distribution for each cell of the contact surface are estimated with the help of Kalker’s simplified theory of rolling contact and Archard’s wear model, respectively. The wear distribution and smoothening of the wheel profile are obtained for a short travel distance and are then scaled up for larger travel distance. The worn wheel profile is updated in the vehicle dynamics model after every 10,000 km of travel for further dynamic analysis and this process is repeated until either the critical dynamic performance or wheel wear limits are reached. Several new results emerge by considering both acceleration and braking on a tangent track with sinusoidal irregularities. Critical speed appears to increase initially and then decrease quickly, whereas worn wheels give better ride comfort in both lateral and vertical directions as compared to new wheels. According to the results in this work, wheels may be recommended for re-profiling or replacement much before the critical wear depth recommended in maintenance guidelines is reached. </jats:p>

Analysis and prediction of double-carriage train wheel wear based on SIMPACK and neural networks

“…The Archard model has become one of the most widely used models 22–26 for the prediction of railway wheel wear. It can be expressed as follows:…”

“…11,23,24 In addition, the wheel wear is predicted based on the Archard model. 25 Considering that prediction of wheel wear by using AI is limited in previous studies, the non-linear auto-regulatory (NAR) neural network and the wavelet neural network (WNN) are employed in this study to predict the high-speed train wheel wear, and the predictions are validated by comparison with the wheel wear data measured by the railway administration. Such validated predictions could be very useful for the maintenance and safe operation of high-speed trains.…”

Simulation of wheel and rail profile wear: a review of numerical models

“…The profile smoothing operations suggested by UniFi scholars were also adopted by Indian researchers from Indian Institute of Technology (IIT), who implemented a wear prediction tool based on the co-simulation between ADAMS VI Rail and a Matlab wear computation routine. This research team proposed both post-processing local contact models relying on a semi-Hertzian approach [113,114], and a simplified model in which the local contact model was based on the FASTSIM algorithm [115]. The worn material was calculated by means of a local application of the Archard law, and the profile update corresponded to a predefined travelled distance.…”

Development of an On-Board Measurement System for Railway Vehicle Wheel Flange Wear

“…There is a possible lateral displacement of the load center on the track, higher lateral forces on the track structure, vehicle instability, passenger discomfort and risk of derailment [2]. A high level of wear of the wheel flange thickness is a danger to the railway vehicle when moving on a curve or on a straight track [3,4]. Therefore, the measurement and inspection of external and internal wheel profile parts, especially the wheel flange thickness, should be done with high accuracy to ensure the safe train operation [5].…”