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Abstract Fire scene reconstruction and determining the fire evolution (i.e., item-to-item ignition events) using the postfire compartment is an extremely difficult task because of the time-integrated nature of the observed damages. Bayesian methods are ideal for making inferences amongst hypotheses given observations and are able to naturally incorporate uncertainties. A Bayesian methodology for determining probabilities to items that may have initiated the fire in a compartment from damage signatures is developed. Exercise of this methodology requires uncertainty quantification of these damage signatures. A simple compartment configuration was used to quantify the uncertainty in damage predictions by firedynamicssimulator (fds) and, a compartment evolution program, jt-risk as compared to experimentally derived damage signatures. Surrogate sensors spaced within the compartment use heat flux data collected over the course of the simulations to inform damage models. Experimental repeatability showed up to 4% uncertainty in damage signatures between replicates. Uncertainties for fds and jt-risk ranged from 12% up to 32% when compared to experimental damages. Separately, the evolution physics of a simple three-fuel-package problem with surrogate damage sensors were characterized in a compartment using experimental data, fds, and jt-risk predictions. A simple ignition model was used for each of the fuel packages. The Bayesian methodology was exercised using the damage signatures collected, cycling through each of the three fuel packages, and combined with the previously quantified uncertainties. Only reconstruction using experimental data was able to confidently predict the true hypothesis from the three scenarios.
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Inversion for Fire Heat-Release Rate Using Heat Flux Measurements
Abstract In fire hazard calculations, knowledge of the heat-release rate (HRR) of a burning item is imperative. Typically, room-scale calorimetry is conducted to determine the HRRs of common combustible items. However, this process can be prohibitively expensive. In this work, a method is proposed to invert for the HRR of a single item burning in a room using transient heat flux measurements at the walls and ceiling near the item. The primary device used to measure heat flux is the directional flame thermometer (DFT). The utility of the inverse method is explored on both synthetically generated and experimental data using two so-called forward models in the inversion algorithm: fire dynamics simulator (FDS) and the consolidated model of fire and smoke transport (CFAST). The fires in this work have peak HRRs ranging from 200 kW to 400 kW. It was found that FDS outperformed CFAST as a forward model at the expense of increased computational cost and that the error in the inverse reconstruction of a 400 kW steady fire was on par with room-scale oxygen consumption calorimetry.
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- Award ID(s):
- 1707090
- PAR ID:
- 10381193
- Date Published:
- Journal Name:
- Journal of Heat Transfer
- Volume:
- 142
- Issue:
- 5
- ISSN:
- 0022-1481
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1115/1.4046264
