Application of FDS and FireFOAM in Large Eddy Simulations of a Turbulent Buoyant Helium Plume

Maragkos, Georgios; Rauwoens, Pieter; Merci, Bart

doi:10.1080/00102202.2012.664002

Cited by 19 publications

(34 citation statements)

References 9 publications

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“…In the present study, we implement AMR in fireFoam, a solver developed by FM Global to simulate complex fire phenomena [10,11]. Designed for simulations of industrial fire problems, fireFoam has been used previously to study problems ranging from non-reacting buoyant plumes [14,15] and pool fires with static [10,16,17] and dynamic meshes (i.e., using AMR) [12], to larger room-scale fires [18,19], pyrolysis modeling [20][21][22][23][24], and fire suppression [22,25].…”

Section: Computational Framework: Wildfirefoammentioning

confidence: 99%

Efficient simulation of turbulent diffusion flames in OpenFOAM using adaptive mesh refinement

Lapointe

Wimer

Glusman

et al. 2020

Fire Safety Journal

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Wildland fires are complex multi-physics problems that span wide spatial scale ranges. Capturing this complexity in computationally affordable numerical simulations for process studies and "outer-loop" techniques (e.g., optimization and uncertainty quantification) is a fundamental challenge in reacting flow research. Further complications arise for propagating fires where a priori knowledge of the fire spread rate and direction is typically not available. In such cases, static mesh refinement at all possible fire locations is a computationally inefficient approach to bridging the wide range of spatial scales relevant to wildland fire behavior. In the present study, we address this challenge by incorporating adaptive mesh refinement (AMR) in fireFoam, an OpenFOAM solver for simulations of complex fire phenomena. The AMR functionality in the extended solver, called wildFireFoam, allows us to dynamically track regions of interest and to avoid inefficient over-resolution of areas far from a propagating flame. We demonstrate the AMR capability for fire spread on vertical panels and for large-scale fire propagation on a variable-slope surface that is representative of real topography. We show that the AMR solver reproduces results obtained using much larger statically refined meshes, at a substantially reduced computational cost.

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Section: Computational Framework: Wildfirefoammentioning

confidence: 99%

Efficient simulation of turbulent diffusion flames in OpenFOAM using adaptive mesh refinement

Lapointe

Wimer

Glusman

et al. 2020

Fire Safety Journal

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“…The usage of a dynamic procedure for determining the turbulent Schmidt, Sc t and turbulent Prandtl, P r t , numbers is also rather novel when it comes to fire scenarios. The research presented in this paper builds on the knowledge obtained by the authors in the past on fire plume modelling [13,14,15,16,17] and aims at making a step forward towards predictive fire modelling.…”

Section: Introductionmentioning

confidence: 99%

On the use of dynamic turbulence modelling in fire applications

Maragkos

Merci

2020

Combustion and Flame

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In an attempt to make a step forward towards predictive fire modelling, the application of turbulence modelling approaches, calculating the sub-grid scale viscosity, kinetic energy, turbulent Prandtl and Schmidt numbers based on a dynamic procedure, has been tested on a wide range of pool fire scenarios (i.e., MaCFP workshop test cases). Through the use of Large Eddy Simulations, it is investigated whether the applied modelling approaches can accurately predict the pool fire dynamics on different grid sizes and result in improved predictions compared to models using constant parameters. Additionally, it is evaluated whether the theoretical constants related to turbulence modelling, derived from isotropic decaying turbulence scenarios, are justified in turbulent buoyant flows.

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“…Furthermore, the FDS+Evac module may be used to simulate evacuation with agent behavior changing based on smoke spread predicted in the base FDS model [36,37]. A large variety of fire experiments at different scales, and in all areas of fire development including turbulence, heat transfer, and solid-state pyrolysis have been used in the validation of the software [38][39][40].…”

Section: Overview Of Current Capabilities For Fire Growth Modeling Bymentioning

confidence: 99%

“…Turbulence, combustion, thermal radiation, solid-state pyrolysis, soot, water spray, and surface film flow models are all incorporated in the base code. It is a relatively new software package that is being actively validated against available experimental data in the areas of turbulence, heat flux, soot generation, and flame spread for small-to full-scale fire experiments [39,[44][45][46][47]. SMARTFIRE v4.1 was developed by the Fire Safety Engineering Group at the University of Greenwich [48,49].…”

Section: Overview Of Current Capabilities For Fire Growth Modeling Bymentioning

confidence: 99%

A multi-component dataset framework for validation of CFD flame spread models

Wong

Dembsey

Alston

et al. 2013

Journal of Fire Protection Engineering

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A review of the literature has shown the need for a comprehensive flame spread dataset framework for computational fluid dynamics model validation purposes. To develop this framework, the flame spread process was viewed as having four key components: turbulent fluid dynamics, gas phase kinetics, flame heat transfer, and condensed-phase pyrolysis. A series of extensively instrumented interrelated experiments based on the four components was conducted under different source fire permutations. This series of three progressively more complex experiments, from free plume, to inert wall fires, to combustible wall flame spread were carried out to enable collection of data relevant to each component of flame spread. Measurements made include heat release rate, plume centerline temperature and velocity, heat flux to wall, near-wall temperature, flame height, flame spread progression, mass loss, and burn pattern. The combustible wall test data in the current research may not be enough to validate a complex real-world integrated flame spread model. However, the sub-models within the integrated model may be validated cohesively using the various types of data presented here as the first step of the validation process.

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Application of FDS and FireFOAM in Large Eddy Simulations of a Turbulent Buoyant Helium Plume

Cited by 19 publications

References 9 publications

Efficient simulation of turbulent diffusion flames in OpenFOAM using adaptive mesh refinement

Efficient simulation of turbulent diffusion flames in OpenFOAM using adaptive mesh refinement

On the use of dynamic turbulence modelling in fire applications

A multi-component dataset framework for validation of CFD flame spread models

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