On the use of dynamic turbulence modelling in fire applications

Maragkos, Georgios; Merci, Bart

doi:10.1016/j.combustflame.2020.02.012

Cited by 21 publications

(14 citation statements)

References 43 publications

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“…can be computed and used in Equation (33) to take into account the presence of the body. The index k refers, in this context, to the sub-iteration that can be performed in the IB force, Equation (35), and projection, Equations ( 33) and (34), to match final velocities with the wall-modeled targets. This velocity reconstruction procedure has been combined with the level set method for fire spread described in the next section.…”

Section: Immersed Boundary Methods and Wall Modelingmentioning

confidence: 99%

“…Subgrid advection of mass and heat are also modeled using gradient diffusion. The turbulent diffusivities use constant turbulent Schmidt and Prandtl numbers, both set to 0.5; this simplification is justified based on high-resolution simulations of fire plumes (see, e.g., [34]). For mass diffusion of species α in direction i, the flux is…”

Section: Subgrid Advectionmentioning

confidence: 99%

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A Multi-Fidelity Framework for Wildland Fire Behavior Simulations over Complex Terrain

et al. 2021

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A method for the large-eddy simulation (LES) of wildfire spread over complex terrain is presented. In this scheme, a cut-cell immersed boundary method (CC-IBM) is used to render the complex terrain, defined by a tessellation, on a rectilinear Cartesian grid. Discretization of scalar transport equations for chemical species is done via a finite volume scheme on cut-cells defined by the intersection of the terrain geometry and the Cartesian cells. Momentum transport and heat transfer close to the immersed terrain are handled using dynamic wall models and a direct forcing immersed boundary method. A new “open” convective inflow/outflow method for specifying atmospheric wind boundary conditions is presented. Additionally, three basic approaches have been explored to model fire spread: (1) Representing the vegetation as a collection of Lagrangian particles, (2) representing the vegetation as a semi-porous boundary, and (3) representing the fire spread using a level set method, in which the fire spreads as a function of terrain slope, vegetation type, and wind speed. Several test and validation cases are reported to demonstrate the capabilities of this novel wildfire simulation methodology.

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Section: Immersed Boundary Methods and Wall Modelingmentioning

confidence: 99%

Section: Subgrid Advectionmentioning

confidence: 99%

A Multi-Fidelity Framework for Wildland Fire Behavior Simulations over Complex Terrain

et al. 2021

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“…A summary of the governing equations and the different sub-models employed, as well as an overview and the required input parameters of the AMR functionality used in the study, is presented below. It is worth noting that the code has been extensively validated in the past by the authors in a wide range of fire-related test cases (e.g., 17,18,19 ).…”

Section: Numerical Modellingmentioning

confidence: 99%

Analysis of adaptive mesh refinement in a turbulent buoyant helium plume

Maragkos

Funk

Merci

2022

Numerical Methods in Fluids

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The study evaluates OpenFOAM's adaptive mesh refinement (AMR) capabilities and accuracy when applied to numerical simulations of turbulent buoyant flows. To this purpose, large eddy simulations of Sandia's turbulent helium plume test case are considered, a scenario with similar characteristics (in terms of air entrainment and vortex shedding) to those encountered in large-scale fires. Comparison is made of the relative accuracy (in terms of both first and second order statistics), the predicted puffing frequencies and the computing times of numerical simulations using AMR and static meshes. Additionally, a sensitivity study on different AMR parameters is conducted and an evaluation of the performance of the dynamic Smagorinsky model when combined with AMR is made. Overall, the predictions of AMR are very satisfactory, in terms of both accuracy and computing times, compared to the use of static meshes of different sizes. Nevertheless, it is observed that a careful choice of a static mesh can result in equally accurate predictions with comparable, or even smaller, computing times than AMR for the case at hand. Additionally, the use of AMR slightly modified the entrainment characteristics in the near-field region of the plume and (significantly) altered some of the, dynamically determined, turbulence model parameters.

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“…The model parameters c s , c I and the turbulent Prandtl number, P r t , are calculated based on a dynamic procedure [22,23,25], and vary locally in time and space, while c = 1.0 [26] is a model constant.…”

Section: Modelsmentioning

confidence: 99%