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Prediction of rail surface damage in locomotive traction operations using laboratory-field measured and calibrated data
Cent Queensland Univ, Ctr Railway Engn, Rockhampton, Australia.;CQ Univ, Ctr Railway Engn, 554-700 Yaamba Rd, Norman Gardens, Qld 4701, Australia..
Cent Queensland Univ, Ctr Railway Engn, Rockhampton, Australia..
Vtech CMCC, Rotterdam, Netherlands..
Simon Fraser Univ, Surrey, BC, Canada..
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2022 (English)In: Engineering Failure Analysis, ISSN 1350-6307, E-ISSN 1873-1961, Vol. 135, article id 106165Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Elsevier BV , 2022. Vol. 135, article id 106165
Keywords [en]
Rail damage, Prediction, Rolling contact fatigue, Locomotive, Digital twin
National Category
Vehicle Engineering
Identifiers
URN: urn:nbn:se:kth:diva-313770DOI: 10.1016/j.engfailanal.2022.106165ISI: 000798977400002Scopus ID: 2-s2.0-85124907317OAI: oai:DiVA.org:kth-313770DiVA, id: diva2:1667534
Note

QC 20220610

Available from: 2022-06-10 Created: 2022-06-10 Last updated: 2022-06-25Bibliographically approved

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Stichel, Sebastian

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CiteExportLink to record
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Citation style
  • apa
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