Forward and inverse modeling of fire physics towards fire scene reconstructions
Forward and inverse modeling of fire physics towards fire scene reconstructions
复制标题
火灾场景重建的火灾物理正向和逆向建模
DOI：
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
K. Overholt
中科院分区：
文献类型：
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作者：
K. Overholt
火灾模型通常用于评估建筑设计项目的生命安全方面，并且更经常地用于火灾和纵火调查以及消防员执勤伤亡的重建。隔间内的火灾有效地留下火灾活动和历史的记录（即，火灾信号）。火灾和纵火调查人员可以在火灾重建演习期间利用这些火灾特征来确定原因和起源。进行火灾实验的研究人员可以利用这些火灾活动的记录来更好地理解潜在的物理学。在所有这些应用中，火灾热释放速率（HRR）、火灾位置和烟雾产生是控制防火分区内热条件演变的重要参数。这些输入参数可能是火灾模型中不确定性的一个很大来源，特别是在无法获得有关火灾行为的实验数据或详细信息的情况下。为了更好地理解与烟灰相关的火灾行为指标，考虑烟灰在表面上的沉积。在计算流体动力学（CFD）火灾模型中实现了对碳烟沉积子模型的改进。为了更好地了解火灾行为指标与火灾规模，逆HRR方法的开发，计算瞬态HRR在一个隔间的基础上测得的温度导致的火源。为了解决与输入参数的不确定性有关的问题，开发了一个反演框架，该框架具有对火灾现场重建的应用。不是使用输入参数的点估计，而是使用基于贝叶斯推理方法的统计反演框架来确定输入参数的概率分布。这些概率分布包含关于输入参数的不确定性信息，并且可以通过火灾模型传播以获得关于感兴趣的预测量的不确定性信息。贝叶斯推理方法被应用于各种火灾问题，并与区域和CFD火灾模型相结合，以扩展反演框架的物理能力和准确性。示例应用包括在一个隔间中的稳态和瞬态火灾大小的估计，与热解相关的材料特性，以及在一个隔间中的火灾的位置。
Fire models are routinely used to evaluate life safety aspects of building design projects and are being used more often in fire and arson investigations as well as reconstructions of firefighter line-of-duty deaths and injuries. A fire within a compartment effectively leaves behind a record of fire activity and history (i.e., fire signatures). Fire and arson investigators can utilize these fire signatures in the determination of cause and origin during fire reconstruction exercises. Researchers conducting fire experiments can utilize this record of fire activity to better understand the underlying physics. In all of these applications, the fire heat release rate (HRR), location of a fire, and smoke production are important parameters that govern the evolution of thermal conditions within a fire compartment. These input parameters can be a large source of uncertainty in fire models, especially in scenarios in which experimental data or detailed information on fire behavior are not available. To better understand fire behavior indicators related to soot, the deposition of soot onto surfaces was considered. Improvements to a soot deposition submodel were implemented in a computational fluid dynamics (CFD) fire model. To better understand fire behavior indicators related to fire size, an inverse HRR methodology was developed that calculates a transient HRR in a compartment based on measured temperatures resulting from a fire source. To address issues related to the uncertainty of input parameters, an inversion framework was developed that has applications towards fire scene reconstructions. Rather than using point estimates of input parameters, a statistical inversion framework based on the Bayesian inference approach was used to determine probability distributions of input parameters. These probability distributions contain uncertainty information about the input parameters and can be propagated through fire models to obtain uncertainty information about predicted quantities of interest. The Bayesian inference approach was applied to various fire problems and coupled with zone and CFD fire models to extend the physical capability and accuracy of the inversion framework. Example applications include the estimation of both steady-state and transient fire sizes in a compartment, material properties related to pyrolysis, and the location of a fire in a compartment.