Turbulence statistics in a fire room model by large eddy simulation

Zhang, Weigang; Hamer, A. J.; Klassen, Michael S.; Carpenter, Daniel; Roby, Richard J.

doi:10.1016/s0379-7112(02)00030-9

Cited by 96 publications

(51 citation statements)

References 24 publications

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“…The small effect of the Schmidt number indicates that the transport mechanisms at the resolved scale are advection dominated. This was also observed in previous LES calculations [28,34].…”

Section: Sensitivity Analysissupporting

confidence: 89%

Buoyancy‐corrected k–ε models and large eddy simulation applied to a large axisymmetric helium plume

Chung¹,

Devaud²

2008

Numerical Methods in Fluids

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SUMMARYThe present numerical study is focused on testing two different modeling approaches to simulate a large turbulent buoyant helium plume, in particular the near-field region. First, buoyancy-corrected k-models are applied in Reynolds-averaged Navier-Stokes (RANS) calculations, then large eddy simulation (LES) using a standard Smagorinsky model is examined. Good results are produced using the buoyancy-corrected models, in particular, excellent agreement is achieved for the radial profiles of the streamwise velocity. However, the predictions are very sensitive to the choice of the buoyancy constant, C 3 , in the models. The present LES calculations show that the puffing frequency is accurately predicted. Predictions for the time-averaged velocities are within experimental uncertainty at all locations. The predicted plume concentrations are in good agreement at the base of the plume, but the centerline values are overpredicted farther downstream. The higher-order statistics are best predicted with the finest mesh. A sensitivity analysis on grid refinement, values of the Smagorinsky constant and the Schmidt number are included.

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“…The small effect of the Schmidt number indicates that the transport mechanisms at the resolved scale are advection dominated. This was also observed in previous LES calculations [28,34].…”

Section: Sensitivity Analysissupporting

confidence: 89%

Buoyancy‐corrected k–ε models and large eddy simulation applied to a large axisymmetric helium plume

Chung¹,

Devaud²

2008

Numerical Methods in Fluids

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“…For example, the Smagorinsky constant depends on the flow conditions; unfortunately, the near-wall flows are not resolved sufficiently by LES. Another drawback is that the Smagorinsky sub-grid model can only account for forward scatter (i.e., energy dissipation from large to small scale), which can cause significant error when there is energy flow in the other direction [13,14].…”

Section: Fire Dynamics Simulator Version 40mentioning

confidence: 99%

“…For mixing layer flows, a Smagorinsky constant between 0.12 and 0.14 produces the most suitable results and 0.1 is required for turbulent channel flow [27]. For fire and smoke related flows in a standard fire room test model, 0.18 is appropriate for certain grid resolutions [14].…”

Section: Smagorinsky Constantmentioning

confidence: 99%

Fire Dynamics Simulator (Version 4.0) Simulation for Tunnel Fire Scenarios with Forced, Transient, Longitudinal Ventilation Flows

2007

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In this study, a series of sensitivity analyses were conducted to evaluate a computational fluid dynamic (CFD) model, Fire Dynamics Simulator (FDS) version 4.0, for tunnel fire simulations. A tunnel fire test with a fire size on the order of a 100 MW with forced, time-varying longitudinal ventilation was chosen from the Memorial Tunnel Ventilation Test Program (MTVTP) after considering recent tunnel fire accidents and the use of CFD models in practice. A careful study of grid size and parameters used in the Large Eddy Simulation (LES) turbulence model-turbulent Prandtl number, turbulent Schmidt number, and Smagorinsky constant-was conducted. More detailed analyses were performed to refine the smoke layer prediction of FDS, especially on backflow (i.e., a reversed smoke flow near the ceiling). Also, energy conservation was checked for this scenario in FDS. A simple guideline is given for smoke layer simulations using FDS for similar tunnel fire scenarios.

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“…The applied k- model [26] is a two-equation eddy viscosity model [27,28] uses transport equations for these two variables [29]. One of these equations governs the distribution through the field of k, the local kinetic energy of the fluctuating motion.…”

Section: Mathematical Modelmentioning

confidence: 99%

Air Movement Within Enclosed Road-Objects with Contra-Traffica CFD-Investigation

Muhasilovic¹,

Mededovic²,

Gacanin³

et al. 2012

Applied Computational Fluid Dynamics

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Turbulence statistics in a fire room model by large eddy simulation

Cited by 96 publications

References 24 publications

Buoyancy‐corrected k–ε models and large eddy simulation applied to a large axisymmetric helium plume

Buoyancy‐corrected k–ε models and large eddy simulation applied to a large axisymmetric helium plume

Fire Dynamics Simulator (Version 4.0) Simulation for Tunnel Fire Scenarios with Forced, Transient, Longitudinal Ventilation Flows

Air Movement Within Enclosed Road-Objects with Contra-Traffica CFD-Investigation

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