CFAST- consolidated model of fire growth and smoke transport (version 5) :

Jones, Walter W.; Peacock, Richard D.; Forney, Glenn P.; Reneke, Paul A.

doi:10.6028/nist.sp.1030

Cited by 14 publications

(2 citation statements)

References 101 publications

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“…Current fire models are on the two extremes of the spectrum: two-zone fire models, 6,7 based on heat and mass balances between the hot smoke layer and the cold layer of a room, are sufficiently fast (much faster than real time), but only yield very basic output such as average smoke layer temperature and smoke layer height. Thus, they can offer only limited assistance in the decision-making process of the emergency response services.…”

Section: Introductionmentioning

confidence: 99%

Towards real-time fire data synthesis using numerical simulations

Jahn

Sazunic

Sing‐Long

2021

Journal of Fire Sciences

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Synthesising data from fire scenarios using fire simulations requires iterative running of these simulations. For real-time synthesising, faster-than-real-time simulations are thus necessary. In this article, different model types are assessed according to their complexity to determine the trade-off between the accuracy of the output and the required computing time. A threshold grid size for real-time computational fluid dynamic simulations is identified, and the implications of simplifying existing field fire models by turning off sub-models are assessed. In addition, a temperature correction for two zone models based on the conservation of energy of the hot layer is introduced, to account for spatial variations of temperature in the near field of the fire. The main conclusions are that real-time fire simulations with spatial resolution are possible and that it is not necessary to solve all fine-scale physics to reproduce temperature measurements accurately. There remains, however, a gap in performance between computational fluid dynamic models and zone models that must be explored to achieve faster-than-real-time fire simulations.

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Section: Introductionmentioning

confidence: 99%

Towards real-time fire data synthesis using numerical simulations

Jahn

Sazunic

Sing‐Long

2021

Journal of Fire Sciences

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“…[6]. It is a two zone model that models the evolution of smoke, combustion gases and temperature in a building compartment that is on fire [11]. The details of the software program can be found in reference [11].…”

Section: Consolidated Fire and Smoke Transport (Cfast) Modelmentioning

confidence: 99%

Treatment of design fire uncertainty using Quadrature Method of Moments

Upadhyay¹,

Ezekoye²

2008

Fire Safety Journal

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The use of a single design fire in a performance based fire design code typically fails to account for the inherent uncertainty in knowledge of the future use of the space. Uncertainties in knowledge of intended use and the implications in terms of fuel loading and potential heat release rate can be bounded using probabilistic methods. Use of a cumulative distribution function (CDF) and the related probability density function (PDF) specify the best available estimate of the probability (likelihood) of a fire of given size to take place in a compartment. Monte Carlo simulation is a widely used computational method for treating uncertainty that might be described by a PDF . In this technique, one samples the uncertain variables from their underlying PDFs and runs a fire model for each sample. For complex fire models, this approach may be computationally intractable. In this work we present a computationally efficient technique called the Quadrature Method of Moments (QMOM) for propagating uncertainty bound in distributions. In QMOM one solves for only the moments of a relevant uncertain parameter. The cumulative distribution function (CDF) of the uncertain parameter provides all the statistical information required for risk assessments. We consider a simplified propagation of uncertainty problem. Results using both the ASET and CFAST fire models indicate that computation of the moments of the PDF using QMOM and the reconstruction of the CDF by matching the moments with those of a 4-parameter Generalized Lambda Distribution (GLD) give accurate results at a significantly smaller computational cost.

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