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Prediction of rail surface damage in locomotive traction operations using laboratory-field measured and calibrated data.

Authors :
Bernal, Esteban
Spiryagin, Maksym
Vollebregt, Edwin
Oldknow, Kevin
Stichel, Sebastian
Shrestha, Sundar
Ahmad, Sanjar
Wu, Qing
Sun, Yan
Cole, Colin
Source :
Engineering Failure Analysis. May2022, Vol. 135, pN.PAG-N.PAG. 1p.
Publication Year :
2022

Abstract

• A simulation method for predicting rolling contact fatigue in railway systems. • A locomotive digital twin with mechatronic traction system co-simulation. • Calibrated shakedown maps using actual rail material mechanical properties. • Wheel-rail contact modelling with variable friction from field measurements. • Rolling contact fatigue hotspots discerning top of rail, gauge face and gauge corner. Rail damage prediction is a complex task because it depends on numerous tribological parameters and the dynamic conditions produced by the vehicles operating at different speeds and configurations. Shakedown maps and Whole-Life-Rail-Model/T-Gamma have been used to predict rail damage, but they involve assumptions that may reduce their accuracy. This paper proposes a simulation modelling method to predict rail surface damage based on a locomotive digital twin, calibrated shakedown maps and friction measurements. The method improves the accuracy of rail damage predictions by including slip-dependent friction characteristics, co-simulation of locomotive traction mechatronic system and the mechanical properties of the wheel and rail materials measured through tensile tests. A set of operating conditions are simulated on a high-performance computing cluster, with stress results being post processed into calibrated shakedown heatmaps. The method clearly indicated the influences of the different operating conditions on rail damage for specific combinations of wheel-rail materials and vehicle-track configurations. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
13506307
Volume :
135
Database :
Academic Search Index
Journal :
Engineering Failure Analysis
Publication Type :
Academic Journal
Accession number :
155778514
Full Text :
https://doi.org/10.1016/j.engfailanal.2022.106165