主管:中华人民共和国应急管理部
主办:应急管理部天津消防研究所
ISSN 1009-0029  CN 12-1311/TU

Fire Science and Technology ›› 2023, Vol. 42 ›› Issue (12): 1656-1662.

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Study on the effect of longitudinal ventilation on the smoke temperature characteristics of subway train fires inside tunnel

Peng Min1, Wu Zhenkun1, Jiang Shun2, Zhu Guoqing1   

  1. (1. School of Safety Engineering, China University of Mining and Technology, Jiangsu Xuzhou 221116, China;2. State Key Laboratory of Fire Science, University of Science and Technology of China, Anhui Hefei 230026, China)
  • Online:2023-12-15 Published:2023-12-15

Abstract: This paper investigates the impact of longitudinal ventilation on the smoke temperature characteristics of subway train fires inside a double long—narrow confined space, composed of the subway train inside a tunnel. The study takes into account the ventilation direction in the tunnel, which is parallel to the lateral opening of the train compartment. The maximum temperature rise of smoke and the longitudinal temperature distribution near the fire source beneath the train ceiling are analyzed under different longitudinal ventilation velocities and heat release rates. The results indicate that the maximum smoke temperature rise predicted by the previous classical single long—narrow confined space model (Li’s model) underestimates the smoke temperature beneath the train ceiling, particularly when v'>0.9, due to the confinement of double boundaries. Furthermore, the effect of longitudinal air flow in the tunnel on the smoke temperature characteristics in the subway train is insignificant under the conditions studied in this paper, owing to the unique action mechanism of the forced air flow in the tunnel through the lateral opening of the subway train. Based on a comparative analysis of experimental data from other studies, this paper proposes empirical prediction models for the maximum smoke temperature rise and longitudinal smoke distribution beneath the train ceiling.

Key words: longitudinal ventilation, subway train fires, maximum temperature, temperature distribution, tunnel, heat release rate, prediction model