运动列车火灾条件下隧道内温度分布快速预测方法  

Rapid prediction method for tunnel temperature distributions during fires on moving trains

作　　者：黄然 朱士友 何梦辰 李若愚 葛鑫茹 王巧 陈娟[3] 卢紫嫣 马剑[1] HUANG Ran;ZHU Shiyou;HE Mengchen;LI Ruoyu;GE Xinru;WANG Qiao;CHEN Juan;LO Jacqueline T.Y.;MA Jian(School of Transportation and Logistics,Southwest Jiaotong University,Chengdu 610031,China;Guangzhou Metro Group Co.Ltd.,Guangzhou 510330,China;Faculty of Geosciences and Engineering,Southwest Jiaotong University,Chengdu 610031,China;Department of Civil and Environmental Engineering,Hong Kong Polytechnic University,Hong Kong 999077,China)

机构地区：[1]西南交通大学交通运输与物流学院,成都610031 [2]广州地铁集团有限公司,广州510330 [3]西南交通大学地球科学与工程学院,成都610031 [4]香港理工大学土木与环境工程系,中国香港999077

出　　处：《清华大学学报(自然科学版)》2025年第3期479-494,共16页Journal of Tsinghua University(Science and Technology)

基　　金：国家重点研发计划项目(2022YFC3005205);国家自然科学基金项目(72104205);四川省科技厅项目(2024YFHZ0345)。

摘　　要：列车在区间隧道发生火灾后,可能在怠速行驶至前方站台进行应急处置的中途丧失动力迫停。隧道内火场烟气和温度分布受运动列车影响难以预测,给人员疏散管控带来困难,严重威胁人员疏散安全。针对上述问题,该文设计了75个隧道内运动列车火灾场景,构建数值模型,开展模拟计算,构建深度学习数据集,利用长短期记忆网络、卷积、反卷积模块搭建隧道内运行列车火灾火场温度预测模型。研究结果表明:在不同列车运动状态下,该模型均可根据当前探测器数据超前30 s预测隧道侧向疏散平台温度分布,且预测耗时仅0.08 s,平均绝对误差为2.2℃,平均绝对百分比误差为4%。该模型的泛化能力和鲁棒性较强,当部分传感器失效时,仍具有较好的预测效果。[Objective]In case of catching fire,a train moving in a tunnel section might lose power and come to an emergency halt while approaching the next station for emergency rescue.Predicting the distribution of smoke and temperature in tunnels under such fire scenarios is difficult because of the influence of moving trains.This difficulty in prediction might seriously threaten the safety of passengers and impede metro evacuation management.[Methods]This study considers influencing factors,including train speed,fire source location,and fire heat release rate,to solve this problem,and 75 different fire scenarios are designed and simulated.Train movement in the simulated scenarios is realized using the equivalent piston wind method.The simulation results of the smoke and temperature distributions collected using sensors near the tunnel ceiling are used to construct a dataset for deep learning.Accordingly,a deep learning model comprising long short-term memory networks,a convolution(Conv)module,and a deconvolution(DeConv)module is then proposed for rapid prediction of temperature distribution in tunnels under moving train fire conditions.The train speed,train braking time,and temperature time-series information from the sensors together are fed as inputs to the model.[Results]The results indicated that:(1)Under various train movement states,the model was able to predict the temperature distribution of the lateral evacuation platform in the tunnel 30 s in advance using the current sensor data,with a mean absolute error(MAE)of only 2.2℃ and a mean absolute percentage error(MAPE)of 4%,indicating high accuracy.(2)In a stark contrast with the week-long time taken to obtain temperature distribution in a fire dynamics simulator(FDS),this deep learning model could make prediction within only 0.08 s,hence representing a computational efficiency improvement of four orders of magnitude versus the computational fluid dynamics method.(3)Validation with fire scenarios in none of the training,validation,and test datasets resulted in mo

关 键 词：隧道火灾 运动列车 深度学习 温度场预测 

分 类 号：U231.96[交通运输工程—道路与铁道工程]