Model Considerations for Fire Scene Reconstruction Using a Bayesian Framework
Model Considerations for Fire Scene Reconstruction Using a Bayesian Framework
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DOI：
10.1007/s10694-019-00886-w
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发表时间：
2019-07
期刊：
影响因子：
3.4
通讯作者：
A. Kurzawski;J. Cabrera;O. Ezekoye
中科院分区：
文献类型：
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作者：
A. Kurzawski;J. Cabrera;O. Ezekoye
为了开发一种更严格的方法来将收集的火灾现场数据与计算工具相结合，本工作提出了贝叶斯计算策略。利用两种著名的正演模型，对合成的时间积分数据运用贝叶斯反演技术反演未知火灾的位置、大小和到达峰值的时间；火灾和烟雾传输综合模型（CFAST）和火灾动力学模拟器（FDS）。为了便于马尔可夫链蒙特卡罗采样，将高斯过程代理模型拟合到粗FDS模拟中。反演框架能够预测所有火灾情况下的总能量释放，除了一个CFAST正演模型，一个1000 kW的稳定火灾。研究发现，在时间积分数据中，没有足够的信息来区分高峰时间的时间变化。FDS在预测最大能量释放率方面优于CFAST，最佳配置的后验均值分别为真实值的0.05%和2.77%。这两种模型在确定隔间内的火灾位置方面表现同样出色。
Towards the development of a more rigorous approach for coupling collected fire scene data to computational tools, a Bayesian computational strategy is presented in this work. The Bayesian inversion technique is exercised on synthetic, time-integrated data to invert for the location, size, and time-to-peak of an unknown fire using two well-known forward models; Consolidated Model of Fire and Smoke Transport (CFAST) and Fire Dynamics Simulator (FDS). A Gaussian process surrogate model was fit to coarse FDS simulations to facilitate Markov Chain Monte Carlo sampling. The inversion framework was able to predict the total energy release by all fire cases except for one CFAST forward model, a 1000 kW steady fire. It was found that insufficient information was available in the time-integrated data to distinguish the temporal variations in peak times. FDS performed better than CFAST in predicting the maximum energy release rate with the posterior mean of the best configurations being 0.05% and 2.77% of the true values respectively. Both models performed equally well on locating the fire in a compartment.