Multi-fidelity surrogate algorithm for fire origin determination in compartment fires

Li, Nan; Lee, Eric Wai Ming; Cheung, Sherman C.P.; Tu, Jiyuan

doi:10.1007/s00366-019-00738-9

Cited by 8 publications

(3 citation statements)

References 42 publications

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“…Li et al utilized the multi-fidelity Kriging algorithm in order to predict the origin of a fire based on soot deposition patterns [ 172 ]. The soot patterns were either measured in the fire scene or determined from numerical simulations; however, all experimental data generated was a result of burning propane, which is a low soot-producing fuel.…”

Section: Fire Researchmentioning

confidence: 99%

Interpol review of fire debris analysis and fire investigation 2019–2022

Evans¹

2023

Forensic Science International: Synergy

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Section: Fire Researchmentioning

confidence: 99%

Interpol review of fire debris analysis and fire investigation 2019–2022

Evans¹

2023

Forensic Science International: Synergy

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“…Additional efforts are made to account for the geometric effect of flashover conditions (Yu et al 2012;Zhang et al 2014;Li et al 2019;Kurzawski and Ezekoye 2020). Although their research outcomes provide substantial improvement for the development of flashover prediction models in multi-compartment structures, their models rely on assumptions that over-simplify the fire scenarios.…”

Section: Related Workmentioning

confidence: 99%

Predicting Flashover Occurrence using Surrogate Temperature Data

Tam

Wang

et al. 2021

AAAI

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Fire fighter fatalities and injuries in the U.S. remain too high and fire fighting too hazardous. Until now, fire fighters rely only on their experience to avoid life-threatening fire events, such as flashover. In this paper, we describe the development of a flashover prediction model which can be used to warn fire fighters before flashover occurs. Specifically, we consider the use of a fire simulation program to generate a set of synthetic data and an attention-based bidirectional long short-term memory to learn the complex relationships between temperature signals and flashover conditions. We first validate the fire simulation program with temperature measurements obtained from full-scale fire experiments. Then, we generate a set of synthetic temperature data which account for the realis-tic fire and vent opening conditions in a multi-compartment structure. Results show that our proposed method achieves promising performance for prediction of flashover even when temperature data is completely lost in the room of fire origin. It is believed that the flashover prediction model can facilitate the transformation of fire fighting tactics from traditional experience-based decision marking to data-driven decision marking and reduce fire fighter deaths and injuries.

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“…These have been successfully applied in e.g. the design of ships [18] airfoils [15], satellites [25], additive manufacturing [28], and fire start determination [14].…”

Section: Introductionmentioning

confidence: 99%

Finding Efficient Trade-offs in Multi-Fidelity Response Surface Modeling

van Rijn,

Schmitt,

van Leeuwen

et al. 2021

Preprint

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In the context of optimization approaches to engineering applications, time-consuming simulations are often utilized which can be configured to deliver solutions for various levels of accuracy, commonly referred to as different fidelity levels. It is common practice to train hierarchical surrogate models on the objective functions in order to speed-up the optimization process. These operate under the assumption that there is a correlation between the high-and low-fidelity versions of the problem that can be exploited to cheaply gain information. In the practical scenario where the computational budget has to be allocated between multiple fidelities, limited guidelines are available to help make that division. In this paper we evaluate a range of different choices for a two-fidelity setup that provide helpful intuitions about the trade-off between evaluating in high-or low-fidelity. We present a heuristic method based on subsampling from an initial Design of Experiments (DoE) to find a suitable division of the computational budget between the fidelity levels. This enables the setup of multi-fidelity optimizations which utilize the available computational budget efficiently, independent of the multi-fidelity model used.

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Multi-fidelity surrogate algorithm for fire origin determination in compartment fires

Cited by 8 publications

References 42 publications

Interpol review of fire debris analysis and fire investigation 2019–2022

Interpol review of fire debris analysis and fire investigation 2019–2022

Predicting Flashover Occurrence using Surrogate Temperature Data

Finding Efficient Trade-offs in Multi-Fidelity Response Surface Modeling

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