Research of operation mode of high-speed trains on the effect of rail wear evolution law

Abstract

Purpose With the increased speed and mileage of high-speed lines, the problem of rail wear is increasing. In actual operation, a large number of abnormal wear phenomena occur on both vehicles and rails during fixed line operation; therefore, the purpose of the study is to explored the rail wear for a variety of vehicles running in mixed operation. Design/methodology/approach This paper used the universal mechanism multibody dynamics software to establish the CRH2 high speed train (HST) and the CRH3 HST vehicle dynamic models, respectively. The mixed running of HSTs on the effect of rail wear evolution law was analyzed. The rail wear of the two vehicles with different curve radii, different wheel diameters and different under-rail stiffness was compared and analyzed. Findings The result showed that the rail wear of CRH3 HST is greater than that of CRH2 HST. The rail wear in the tangent track under mixed operation conditions is 25.4% less than when CRH3 HST operated independently. When there is a 1-mm wheel diameter difference, the maximum rail wear of CRH2 HST and CRH3 HST increases by 263% and 44%, respectively. The amount of rail wear is proportional to the under-rail stiffness, and the position of the maximum wear is almost unchanged. Originality/value Most studies on the evolution law of rail wear are conducted for a single vehicle type and a single line. This study explored the mixed running of HSTs on the effect of rail wear evolution law. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-08-2023-0276/

Predicting the wear on the non-working side of the rail profile registration method and its validation

“…However, as the number of trains passing accumulated, the lateral wear showed a generally linear growth pattern. Its development pattern is consistent with that in [37]. Using the data from figure 16, train pass counts up to 1.1 million within the period are utilized as training data to predict the data for passes beyond 1.1 million.…”