Numerical analysis of the effect of track parameters on the wear of turnout rails in high-speed railways

Abstract

In this study, a numerical procedure is developed to predict the wear of turnout rails, and the effect of track parameters is investigated. The procedure includes simulation of the dynamic interaction between the train and the turnout, the rolling contact analysis, and the wear model. The dynamic interaction is simulated with the validated commercial software Simpack that uses a space-dependent model of a railway turnout. To reproduce the actual operating conditions of a railway turnout, stochastic variations in the input parameters are considered in the simulation of the dynamic interaction. The rolling contact is analyzed with the semi-Hertzian method and improved FASTSIM algorithm, which enable the contact model to deal with situations of multipoint contact and nonelliptic contact. Based on the Archard's wear law, the wear model requires the calculation of normal/tangential stresses and a relative slide on the contact patches. The numerical procedure is performed for the selected sections of the vehicle, which runs through the railway turnout in the diverging route. By using the numerical procedure, the effect of track parameters (track gage, rail inclination, and friction coefficient) on the wear of turnout rails is analyzed. The results show that the wear of the front wheelset is more serious than the wear of the rear wheelset for a single vehicle. The degree of wear of switch rails is more severe than that of the stock rails and the difference is more obvious for the front wheelset of the switch rails. The wear of switch rails is mainly concentrated on the rail gage corner, while the wear of stock rails is mainly concentrated on the rail crown. For the analysed CN60-1100-1:18 turnout and the high-speed vehicle CRH2 in China, the rail wear rate could be slowed down by increasing the track gage and decreasing the rail inclination. Alternatively, the rail wear rate could be slowed by decreasing the friction coefficient; however, the variation of wear depth is quite small for friction coefficients that are larger than 0.3.

Optimizing the Rail Profile for High-Speed Railways Based on Artificial Neural Network and Genetic Algorithm Coupled Method

“…Therefore, there is no wear coefficient for high-speed railways in China at present. However, many researchers and scholars [20][21][22][23] have used the wear coefficient obtained from the commuter rail system in Stockholm to study the wheel/rail wear, which indicates that this wear coefficient can be suitable for various conditions, including the wheel/rail wear in China high-speed railways. Therefore, the wear coefficient adopted in this paper is depicted by Figure 5 according to the commuter rail system in Stockholm (Jendel [24]).…”

“…The statistical histograms of the vertical/lateral wheel-rail force, derailment coefficient, and the wheel load reduction ratio are illustrated in Figure 19, and the time history curves of contact patch area are shown in Figure 20. Moreover, the derailment coefficient and the wheel load reduction ratio can be calculated by Equations (20) and (21).…”

Effects of Traffic Loads and Track Parameters on Rail Wear: A Case Study for Yenikapi–Ataturk Airport Light Rail Transit Line

“…Accurate prediction of rail wear may improve riding comfort, safety of railway operations, and efficiency of track maintenance by decreasing track maintenance costs and risk of derailment [7]. Therefore, establishing rail wear prediction models and examining effective parameters on rail wear are crucial in terms of cost, comfort, and railway safety [8].…”

Numerical Analysis of Rail Wear Behavior in Railway Systems

“…Railway industry is developing rapidly to meet the increasing demands of modern societies. However, railway operations are critically influenced by the wear of wheels and rails [1][2][3][4]. When a wheel rolls over a rail, the wheel/rail surfaces are subjected to high sticking and sliding.…”