kth.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
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
Show others and affiliations
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 and Aerospace 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: 2025-02-14Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Conference webpage

Authority records

Krishna, Visakh VHossein Nia, SaeedStichel, Sebastian

Search in DiVA

By author/editor
Krishna, Visakh VHossein Nia, SaeedStichel, Sebastian
By organisation
Engineering MechanicsThe KTH Railway GroupVinnExcellence Center for ECO2 Vehicle designVehicle Engineering and Solid Mechanics
Mechanical EngineeringVehicle and Aerospace Engineering

Search outside of DiVA

GoogleGoogle Scholar

urn-nbn

Altmetric score

urn-nbn
Total: 734 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf