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Vehicle System Dynamics
International Journal of Vehicle Mechanics and Mobility
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Research Article

A PCM-based train post-derailment dynamics model and its application

, , , &
Received 26 Feb 2024, Accepted 26 Jun 2024, Published online: 04 Jul 2024
 

Abstract

Understanding post-derailment behaviours is critical and fundamental to preventing derailment escalation and developing containment methods, thereby avoiding catastrophic consequences in a derailment accident. However, modelling and simulating the train post-derailment behaviours is a significant challenge because of the complexity and unpredictability of the train post-derailment contact interaction. This paper presents a novel post-derailment contact model built upon the Polygon Contact Model (PCM), aimed at enabling accurate and efficient simulation of train post-derailment contact interactions. Expanding upon the PCM-based post-derailment contact model, we introduce a framework for simulating train post-derailment behaviours and utilise it to develop a train post-derailment dynamics model that considers complex contact interactions between the bogie and the track structure. Additionally, we introduce a dynamic optimisation method to determine the essential parameters for this model. The feasibility and accuracy of the PCM-based train post-derailment dynamics model have been validated through a comparative study with an actual derailment test. Subsequently, the verified model is used to reproduce different post-derailment scenarios with varying combinations of vehicles and tracks, yielding valuable insight and understanding into the train post-derailment behaviour.

Acknowledgements

The authors gratefully acknowledge the support from the National Natural Science Foundation of China (No.52172407, No. U2268210 and No. U19A20110) and the Fundamental Research Funds for the State Key Laboratory of Rail Transit Vehicle System of Southwest Jiaotong University (No.2024RVL-T14).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The authors gratefully acknowledge the support from the National Natural Science Foundation of China (No. 52172407, No. U2268210 and No. U19A20110) and the Fundamental Research Funds for the State Key Laboratory of Rail Transit Vehicle System of Southwest Jiaotong University (No. 2024RVL-T14).

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