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Data and Model Requirements for Statistically Weighted Determination of Fire Origin for Fire Forensics

火灾取证统计加权火源确定的数据和模型要求

基本信息

批准号：
1707090
负责人：
Ofodike Ezekoye
金额：
$ 35.02万
依托单位：
University of Texas at Austin
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-07-15 至 2022-05-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1707090&HistoricalAwards=false
关键词：
Data Model Requirements Statistically Weighted

项目摘要

Fire forensic reconstruction, in many respects, is one of the most complex and error-plagued areas of forensic science. The United States civil and criminal justice systems rely on expert witness testimony in the evaluation of fault and blame in legal cases. Increasingly, these expert witnesses rely on scientific models to either explain or promote a particular hypothesis/theory of how the fire evolved. The challenge for fire investigators is that by its very nature, a fire damages the contents of the compartment and obscures signatures produced in its early evolution. Extracting statistically meaningful signatures in a fire scene requires characterizing the thermochemical damage to typical materials present in the fire environment. These damage signatures must then be connected in a self-consistent manner to large-scale fire evolution. The overarching goal of this project is to develop a rigorous statistical methodology to connect these measured data and fire signatures to fire evolution for forensic reconstruction. This provides a scientific foundation for future development of fire forensic standards and best practices. Such standards will be the basis for training of more scientifically sophisticated fire investigators.This project will evaluate the effects of model bias and data uncertainty in issuing predictions about the origin of a fire. Measurements and observations present in typical fire scenes will be distilled into mathematical terms that can be modeled and subjected to quantifiable assessments. Several tasks are required to accomplish the project goals. First, degradation of condensed phase materials will be experimentally investigated at small and large scales to characterize thermal damage. Next, stochastic damage models will be developed to describe material degradation. Concurrently, a Bayesian framework using computational models and measured data will be developed to statistically evaluate fire origin and evolution hypotheses. Finally, a validation study will be conducted in a densely-instrumented and actuator-controlled large-scale fire test room. The project will advance knowledge in fire science by characterizing property changes for condensed phase materials during fire thermal exposure, encapsulating property change features into damage models, and identifying the limits on how these damage models coupled to gas-phase fire models should be applied to fire forensic reconstruction.

火灾法医重建，在许多方面，是最复杂和错误困扰的法医学领域之一。美国的民事和刑事司法系统依靠专家证人的证词来评估法律的案件中的过失和责任。这些专家证人越来越多地依靠科学模型来解释或推广火灾如何演变的特定假设/理论。火灾调查人员面临的挑战是，就其本质而言，火灾会破坏车厢的内容物，并掩盖其早期演变中产生的特征。在火灾现场提取统计上有意义的签名需要表征火灾环境中存在的典型材料的热化学损伤。这些破坏特征必须以自洽的方式与大规模火灾演变联系起来。该项目的总体目标是开发一种严格的统计方法，将这些测量数据和火灾特征与火灾演变联系起来，以进行法医重建。这为今后制定火灾法医标准和最佳做法提供了科学基础。这些标准将成为培训更有科学经验的火灾调查人员的基础，本项目将评估模型偏差和数据不确定性对发布火灾起因预测的影响。典型火灾现场的测量和观察将被提炼成可以建模和量化评估的数学术语。要实现项目目标，需要完成多项任务。首先，将在小尺度和大尺度上对凝聚相材料的降解进行实验研究，以表征热损伤。接下来，将开发随机损伤模型来描述材料退化。同时，将开发一个使用计算模型和测量数据的贝叶斯框架，以统计学方式评估火灾起源和演变假设。最后，验证研究将在一个密集的仪器和执行器控制的大型火灾试验室进行。该项目将通过表征火灾热暴露过程中凝相材料的属性变化，将属性变化特征封装到损坏模型中，并确定这些损坏模型如何与气相火灾模型相结合的限制，以提高火灾科学的知识。

项目成果

期刊论文数量（7）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Improving Heat Flux Predictions for Directional Flame Thermometers by Incorporating Convective Effects

通过结合对流效应改进定向火焰温度计的热通量预测

DOI：
10.1007/s10694-022-01263-w
发表时间：
2022
期刊：
Fire Technology
影响因子：
3.4
作者：
Franqueville, Juliette I.;Cabrera, Jan-Michael;Ezekoye, Ofodike A.
通讯作者：
Ezekoye, Ofodike A.

Inversion for Fire Heat-Release Rate Using Heat Flux Measurements

使用热通量测量反演火灾放热率

DOI：
10.1115/1.4046264
发表时间：
2020
期刊：
Journal of Heat Transfer
影响因子：
0
作者：
Kurzawski, Andrew J.;Ezekoye, Ofodike A.
通讯作者：
Ezekoye, Ofodike A.

Deep-Learning Emulators of Transient Compartment Fire Simulations for Inverse Problems and Room-Scale Calorimetry

反演问题和室内量热法瞬态舱室火灾模拟的深度学习模拟器

DOI：
10.1007/s10694-020-01037-2
发表时间：
2021
期刊：
Fire Technology
影响因子：
3.4
作者：
Buffington, Tyler;Cabrera, Jan-Michael;Kurzawski, Andrew;Ezekoye, Ofodike A.
通讯作者：
Ezekoye, Ofodike A.

Bayesian Inference of Fire Evolution Within a Compartment Using Heat Flux Measurements

使用热通量测量对室内火灾演化进行贝叶斯推断