Evaluation of LES sub-grid scale models and time discretization schemes for prediction of convection effect in a buoyant
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ORIGINAL
Evaluation of LES sub-grid scale models and time discretization schemes for prediction of convection effect in a buoyant pool fire Mohammad Safarzadeh 1 & Ghassem Heidarinejad 1 & Hadi Pasdarshahri 1 Received: 6 November 2019 / Accepted: 31 August 2020 # Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract The identification of the type of regime has an essential effect on the selection of sub-grid scale and discretization methods, regarding the accuracy and computational time. In order to investigate the role of sub-grid scale and time discretization method in a pool fire modeling by the Large Eddy Simulation (LES), three sub-grid scales Smagorinsky, one-equation and Wall-Adapting Local Eddy-Viscosity (WALE) and three time discretization methods of first-order Euler method, second-order Crank Nicolson and backward second-order were investigated. The results indicate that WALE and one-equation sub-grid scale have an acceptable prediction, while Smagorinsky has a significant error with the experimental results. Different time discretization methods have little effects on the results of mean velocity and mean turbulent parameters of the pool fire. By considering the instantaneous distribution of parameters, all three methods perform similar results where the kinetic energy is less than 5 m2/s2. In areas where the perturbation is aggravated, the two second-order discretization methods have the same error with a little difference by the first-order Euler method. The accuracies of second-order methods are about 10% for the velocity prediction, compared to the experimental results, while the error of the Euler method is 15%. Nomenclature and abbreviations cp specific heat, J/kg.K Cs Smagorinsky constant Cw WALE constant CFD computational fluid dynamic D Derivative dt time step, s g Gravity, m/s2 k Turbulence kinetic energy, m2/s2 LES large eddy simulation p Pressure, kPa Pr Prandtl number qi Diffusion/flux vector S source term Sij Rate of strain tensor, s−1 sgs subgrid-scale Sc Schmidt numbers
* Hadi Pasdarshahri [email protected] Mohammad Safarzadeh [email protected] Ghassem Heidarinejad [email protected] 1
Faculty of Mechanical Engineering, Tarbiat Modares University, P.O.Box 14115-143, Tehran, Iran
T t u x z
Temperature, K Time, s Velocity, m/s Coordinate, m mixture fraction
Greek symbol Δ sub-grid length scale, m δij Dirac delta function μ Molecular viscosity, kg/m.s ν kinematic viscosity, m2/s ρ Density, kg/m3 τij Viscous stress tensor, kg/m.s τ ui T Turbulent diffusion/flux vector, kg/s3 τ ui u j Turbulent viscous stress tensor, kg/m.s τ ui φ Turbulent mass flux, kg/m2.s φ scalar quantity such as species ωT Combustion heat release rate, Subscripts i, j, k space index Ref reference t Turbulence w WALE index Superscripts ˜ Favre Filtering d resolve scale sgs Sub-grid scale
Heat Mass Transfer
1 Introduction In general, fire can be divided into two sets, jet fire and pool fire [1]. The division criteria are based on the ratio of buoyancy forces to momentum. The pool fi
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