基于CoKriging模型的单室火灾温度预测  

Temperature predictions of a single-room fire based on the CoKriging model

作　　者：沈迪 蒋勇[1] 祝现礼 李梦婕 Shen Di;Jiang Yong;Zhu Xianli;Li Mengjie(State Key Laboratory of Fire Science,University of Science and Technology of China,Hefei 230027,China)

机构地区：[1]中国科学技术大学火灾科学国家重点实验室,安徽合肥230027

出　　处：《中国科学技术大学学报》2021年第1期75-86,共12页JUSTC

基　　金：National Natural Science Foundation of China(51576183);the Fundamental Research Funds for the Central Universities(WK2320000048&WK2320000042).

摘　　要：高保真数值模拟和单保真机器学习替代模型均需大量时间才能对火场温度做出准确预测,无法满足消防管理的应急需求.为解决上述问题,引入了CoKriging模型,并从建模时间成本、预测时间成本及预测结果准确性这3方面讨论了该模型在单室火灾温度预测中的适用性与特点.该模型利用CFAST和FDS模拟产生的154组混合数据进行训练.交叉验证的结果表明,当高、低保真度数据占比为10∶1时,该模型就能得到有效训练.进一步对不同方法、模型展开对比分析,结果表明,CoKriging模型的预测结果与高保真模拟FDS的计算结果十分接近,且模型一旦构建成功,其做出一次新的预测所需时间远少于FDS.除此之外,在将建模时间成本缩短至1/10的情况下,CoKriging模型仍能达到与单保真替代模型ANN、Kriging一致的预测准确度.实验还发现,高、低保真数据占比不会对CoKriging模型预测结果产生显著影响,即使只有少量FDS数据参与训练,仍能保证CoKriging模型的预测准确性.因此,CoKriging模型可作为一种快速而有效的回归分析方法,应用在单室火灾的温度预测中.This paper aims at accurately predict the smoke temperature in a single-room fire.Since both high-fidelity simulations and single-fidelity surrogate models cost much computational time,it is hard to meet the emergency needs of fire safety management.Therefore,a multi-fidelity model named CoKriging was introduced,which made use of the simulation data from Consolidate Fire and Smoke Transport(CFAST)and Fire Dynamic Simulator(FDS)for training.The leave-one-out cross-validation suggests that this model has been effectively trained when the data ratio of CFAST to FDS is 10∶1.Further comparisons among different methods show that the prediction accuracy of CoKriging is comparable to that of artificial neural network(ANN)and Kriging,while the modeling time is only 1/10 of the latter.Additionally,the predicted temperatures of CoKriging are very close to the simulated results of FDS,and once the CoKriging model is successfully constructed,much less time will be taken to make a new prediction than that of FDS.The exploratory research on the proportion of high-and low-fidelity data to the prediction results of CoKriging shows that there is no obvious correlation between them,and the prediction accuracy can still be ensured even if only a small amount of FDS data participates in model testing.In conclusion,the CoKriging model could be used as a fast and effective regression analysis method for the temperature prediction in a single-room fire.

关 键 词：多保真 替代模型 COKRIGING 烟气层温度 单室火灾 

分 类 号：O643.36[理学—物理化学]