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Understanding fire combustion characteristics and available safe egress time in underground metro trains: A simulation approach

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  • Zhang, Zhenyu
  • Yao, Xiaowen
  • Xing, Zongyi
  • Zhou, Xinyi

Abstract

Due to insufficient oxygen supply in the tunnel, incomplete combustion of the underground metro train fire easily produces toxic gases and smoke dust, causing difficulty in breathing for passengers and rescue. The experimental study of the fire combustion characteristics of metro trains in the tunnel is of great significance for designing metro fire extinguishing and fire prevention systems and providing reliable guidance for emergency evacuation in fire scenarios. A train fire model is established to study the effects of various factors on fire combustion characteristics and calculate the available safe egress time (ASET). The smoke spread characteristics under different conditions such as fire source power, fire source location, carriage layout, and train location are simulated. The simulation results show that the fire source power, location, and seat arrangement have a significant impact on the smoke spread characteristics of carriage fires and ASET.

Suggested Citation

  • Zhang, Zhenyu & Yao, Xiaowen & Xing, Zongyi & Zhou, Xinyi, 2024. "Understanding fire combustion characteristics and available safe egress time in underground metro trains: A simulation approach," Chaos, Solitons & Fractals, Elsevier, vol. 187(C).
    • Handle: RePEc:eee:chsofr:v:187:y:2024:i:c:s096007792400986x
    DOI: 10.1016/j.chaos.2024.115434
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    References listed on IDEAS

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    1. Junfeng, Chen & Maohua, Zhong & Peiyun, Qiu & Zeng, Long & Jiacheng, Chen, 2023. "Mapping the fire risk in buildings: A hybrid method of ASET-RSET concept and FED concept," Reliability Engineering and System Safety, Elsevier, vol. 240(C).
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    Cited by:

    1. Xie, Chuan-Zhi Thomas & Chen, Qihua & Zhu, Bin & Lee, Eric Wai Ming & Tang, Tie-Qiao & Yin, Xianfei & Yuan, Zhilu & Zhang, Botao, 2025. "Coordinating dynamic signage for evacuation guidance: A multi-agent reinforcement learning approach integrating mesoscopic crowd modeling and fire propagation," Chaos, Solitons & Fractals, Elsevier, vol. 194(C).

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