Long freight trains & Long-term rail surface damage: Towards digital twins that enable predictive maintenance of track

In this work, the authors present a detailed ’train-track’ interaction model of a long freight train operation to predict long-term rail surface damage. In addition to vehicles and track, intermediate maintenance actions in the form of cyclic grinding passes have also been modelled according to EN standards to effectively represent the 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-step methodology consists of modules each modelling longitudinal train dynamics and long-term rail surface damage respectively. The multi-disciplinary integrated numerical framework comprising of train, real operational cases and track attributes has been built based on principles of vehicle dynamics, tribology, and fatigue analysis. The model has been demonstrated for a heavy haul freight train operation on a 120 km long track section in the north of Sweden for which the results have been presented. Also, additional scenarios that can be expected in a real time operation with varying traction/braking, gradients etc have been considered. In the end, this integrated numerical framework comprising of 4 T’s namely train, track, tractive strategies, and track maintenance can be tuned into a digital twin to guide infrastructure managers regarding the condition of rail assets as a function of tonnage passage. This can in turn facilitate predictive maintenance of track depending on traffic and operation.