摘要
半U形隧道是考虑火灾发生在水下隧道变坡点前而简化得到的隧道结构,通过理论推导很难建立有关烟气运动参数预测模型。因此,借助于FDS数值模拟和机器学习对320组火灾工况进行模拟分析和机器学习预测。结果表明:纵向风速的增大对于空气补充速度会有一定的抑制效果,BP神经网络在测试集和训练集上的预测效果较其他机器学习模型更为精确,决定系数R~2能够达到0.99;通过Shap值对影响隧道内空气补充速度的特征因素按重要性从高到低排序依次为高度效应、热效应、风效应;最高烟气温升受风速影响,坡高小则温升随风速减小剧烈,坡高大时风速影响不显著,并且相较于其他机器学习方法,BP神经网络和理论计算均能准确预测烟气最高温升,R~2均大于0.9。研究结合数值模拟与机器学习,为高效预测隧道火灾动力学行为及通风排烟系统优化设计提供了新方法。
The coupling of the fire temperature field and wind field in a half-U-shaped tunnel makes it difficult to quantify the temperature change in the tunnel,thus the theoretical derivation is difficult,the hypothesis is oversimplified,and the prediction model is relatively complicated.Therefore,this paper uses FDS numerical simulation and machine learning to conduct simulation analysis and machine learning prediction of 320 groups of fire conditions.The data set of fire-related parameters of a half-U-shaped tunnel is composed of the numerical simulation of fire with different working conditions.The simulation results and the predictions of different machine learning models for air refill rates and maximum smoke temperature rise are analyzed,The results show that the increase of slope height and heat release rate can improve the air replenishment velocity,while the increase of the longitudinal wind velocity reduces the supplement velocity by restraining the rise of the fire temperature field.The prediction effect of the BP neural network on the test set and training set is more accurate than other machine learning models.The coefficient of determination R 2 can reach 0.99,and the average absolute error can be as low as 0.058.According to the Shap value,the characteristic factors affecting the air replenishment velocity in the tunnel are ranked in order of importance as height effect,thermal effect,and wind effect.The maximum smoke temperature rise is affected by the velocity.When the slope height is small,the temperature rise sharply decreases with the velocity,while the velocity has no significant effect on the maximum smoke temperature rise when the slope is high.This phenomenon accords with the result of theoretical analysis.Compared with other machine learning methods,both the BP neural network and theoretical calculation can accurately predict the maximum temperature rise of smoke,and R 2 is greater than 0.9.
作者
徐志胜
殷耀龙
王轩
雷志强
陈诗仪
XU Zhisheng;YIN Yaolong;WANG Xuan;LEI Zhiqiang;CHEN Shiyi(School of Civil Engineering,Central South University,Changsha 410075,China;Faculty of Geosciences and Engineering,Southwest Jiaotong University,Chengdu 611756,China)
出处
《安全与环境学报》
CAS
CSCD
北大核心
2024年第9期3376-3388,共13页
Journal of Safety and Environment
关键词
安全工程
半U形隧道火灾
烟囱效应
空气补充速度
最高烟气温升
机器学习
safety engineering
half-U-shaped tunnel fire
stack effect
air supplement velocity
maximum smoke temperature rise
machine learning
