Skip to main navigation Skip to search Skip to main content

Derailment mechanism of heavy-haul trains on complex curves during braking and release operations

Kaizhong Liu, Zhiwei Wang, Mingtao Zhang, Kaiyun Wang, Fan Zhang, Pengfei Liu, Paul Allen

Research output: Contribution to journalArticlepeer-review

Abstract

Curved sections of track are recognised as critical high-risk zones for heavy-haul train (HHT) derailments. To better understand derailment mechanisms under braking and release events and to support effective derailment mitigation strategies, a unified safety evaluation framework was constructed. This framework integrates a pneumatic braking model, a longitudinal–vertical coupled HHT dynamics model, and a quasi-static lateral mechanics model, validated by field data. Using this framework, the running safety of a 10,000-ton HHT negotiating S-curves was systematically examined. The analysis shows that longitudinal impulses during release are considerably more pronounced than those during braking, resulting in elevated lateral coupler forces that heighten derailment risk. Further investigation demonstrates this risk is primarily driven by the spatiotemporal alignment between longitudinal impulses and lateral curve geometry. A critical condition emerges when peak impulses align with small-radius segments, producing a compounded effect of high lateral force and unfavourable geometry that sharply increases the derailment coefficient. Additionally, excessive superelevation caused by reduced speeds during final braking is identified as an independent additional risk factor. These insights provide a technical foundation for reducing derailment risks on mountainous heavy-haul railways.

Original languageEnglish
Number of pages26
JournalVehicle System Dynamics
Early online date9 Jun 2026
DOIs
Publication statusE-pub ahead of print - 9 Jun 2026

Fingerprint

Dive into the research topics of 'Derailment mechanism of heavy-haul trains on complex curves during braking and release operations'. Together they form a unique fingerprint.

Cite this