灾害学2025，Vol.40Issue(1)：67-73,7.DOI:10.3969/j.issn.1000-811X.2025.01.011
不同结构参数下的半U形地下空间烟气运动参数的机器学习预测
Machine Learning Prediction of Smoke Motion Parameters in a Half-U-shaped Underground Space with Different Structural Parameters
摘要
Abstract
A comprehensive analysis of smoke movement during a fire in a half-U-shaped underground space is con-ducted by using a combination of Fire Dynamics Simulator(FDS)and machine learning techniques.The findings indicate that the Backpropagation Neural Network(BPNN)outperforms Support Vector Regression(SVR)in forecasting the length of smoke backlayering and the maximum temperature increase,with a determination coefficient exceeding 95%and relative errors predominantly within the 20%range,marking a significant improvement over the SVR method.By elucidating the machine learning model through SHAP values and integrating the outcomes of FDS numerical simulations,it is determined that the slope height is the pivotal factor influencing the length of smoke backlayering.An increase in slope height,a reduc-tion in width,or an escalation in heat release rate are all found to curtail the smoke reflux length.Concurrently,the heat re-lease rate is identified as the primary factor affecting the maximum temperature rise of the smoke,with the slope height ex-erting a substantial influence.Although a decrease in width can marginally diminish the maximum temperature rise of the smoke,its impact is not pronounced.This research not only broadens the scope of predictive methods for fire smoke motion parameters in underground spaces but also presents an innovative approach to forecasting the dynamics of fires in under-ground environments and to the optimization of ventilation and smoke exhaust systems.关键词
半U形地下空间/机器学习/截面宽度/坡高/烟气回流长度/最高烟气温升Key words
half-U-shaped underground space/machine learning/section width/slope height/smoke backlayer-ing length/maximum smoke temperature rise分类
资源环境引用本文复制引用
徐志胜,殷耀龙,雷志强,陈诗仪,应后淋..不同结构参数下的半U形地下空间烟气运动参数的机器学习预测[J].灾害学,2025,40(1):67-73,7.基金项目
国家自然科学基金项目"环保型泡沫灭火剂性能调控机制与理论模型研究"(52176146) （52176146）
