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Long freight trains & Long-term rail surface damage: Towards digital twins that enable predictive maintenance of track
KTH, School of Engineering Sciences (SCI), Engineering Mechanics. KTH, School of Engineering Sciences (SCI), Centres, The KTH Railway Group.ORCID iD: 0000-0002-4477-971X
KTH, School of Engineering Sciences (SCI), Engineering Mechanics.ORCID iD: 0000-0002-6346-6620
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2022 (English)In: Proceedings of the 13th World Congress on Railway Research (WCRR2022), Birmingham, UK, 2022Conference paper, Published paper (Refereed)
Abstract [en]

In this work, the authors present a detailed ’train-track’ interaction model of a long freight train operation topredict long-term rail surface damage. In addition to vehicles and track, intermediate maintenance actions inthe form of cyclic grinding passes have also been modelled according to EN standards to effectively representthe evolving wheel-rail interface. The influence of longitudinal train dynamics in the form of traction, braking,gradients, etc are also accounted in the method to reflect their effect on damage evolution. The two-stepmethodology consists of modules each modelling longitudinal train dynamics and long-term rail surface damagerespectively. The multi-disciplinary integrated numerical framework comprising of train, real operational casesand track attributes has been built based on principles of vehicle dynamics, tribology, and fatigue analysis. Themodel has been demonstrated for a heavy haul freight train operation on a 120 km long track section in thenorth of Sweden for which the results have been presented. Also, additional scenarios that can be expected ina real time operation with varying traction/braking, gradients etc have been considered. In the end, thisintegrated numerical framework comprising of 4 T’s namely train, track, tractive strategies, and trackmaintenance can be tuned into a digital twin to guide infrastructure managers regarding the condition of railassets as a function of tonnage passage. This can in turn facilitate predictive maintenance of track depending ontraffic and operation.

Place, publisher, year, edition, pages
Birmingham, UK, 2022.
National Category
Mechanical Engineering Vehicle Engineering
Research subject
Järnvägsgruppen - Fordonsteknik; Järnvägsgruppen - Effektiva tågsystem för godstrafik; Vehicle and Maritime Engineering
Identifiers
URN: urn:nbn:se:kth:diva-314798OAI: oai:DiVA.org:kth-314798DiVA, id: diva2:1675860
Conference
World Congress on Railway Research (WCRR2022)
Funder
EU, Horizon 2020, 101004051
Note

QC 20220627

Available from: 2022-06-23 Created: 2022-06-23 Last updated: 2022-06-27Bibliographically approved

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Krishna, Visakh VHossein Nia, SaeedStichel, Sebastian

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CiteExportLink to record
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Citation style
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