Assessing freight train derailment risk: comparison of European rules integrated with digital twins for running safety indexes

Abstract

This paper compares the assessment of freight train running safety according to two European rules, namely the UIC IRS 40421 and the EN 14363, under emergency braking operations. The paper relies on a combination of longitudinal train dynamics (LTD) simulations for the evaluation of the in-train forces and multibody (MB) simulations for the calculation of the running safety indexes derived from the wheel-rail contact forces. The results confirm that the IRS 40421 is more conservative when assessing the safety of families of freight trains with a statistical perspective. Nonetheless, in individual operating scenarios, the IRS 40421 can predict safe conditions when MB simulations calculate running safety indexes above the limits prescribed by the EN 14363. To improve the running safety assessment from the outputs of LTD simulations, the paper suggests the introduction of kernel regressions, acting as digital twins of MB simulations, for the prediction of the running safety indexes. Digital twins improve the consistency with the classification of derailment conditions based on the outputs of the MB simulations. The findings highlight the complementary value of the two rules and the potential of digital twins to support the reliable assessment of freight train running safety.

Keywords

Railways / Running safety / Derailment / Longitudinal train dynamics / Multibody dynamics / Machine learning / Digital twins

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Funding

Ministero dell'Università e della Ricerca(FISA-2023-00314)

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