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Modelling of the Curvature Term of the Flame Surface Density Transport Equation for Large Eddy Simulations
Author(s) -
Mohit Katragadda,
Nilanjan Chakraborty
Publication year - 2012
Publication title -
journal of combustion
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.45
H-Index - 18
eISSN - 2090-1968
pISSN - 2090-1976
DOI - 10.1155/2012/915482
Subject(s) - curvature , large eddy simulation , context (archaeology) , reynolds number , mechanics , diffusion , turbulence , convection–diffusion equation , displacement (psychology) , direct numerical simulation , eddy diffusion , term (time) , statistical physics , physics , mathematics , geometry , thermodynamics , geology , psychology , paleontology , quantum mechanics , psychotherapist
A simplified chemistry based three-dimensional Direct Numerical Simulation (DNS) database of freely propagating statistically planar turbulent premixed flames with a range of different values of turbulent Reynolds number has been used for the a priori modelling of the curvature term of the generalised Flame Surface Density (FSD) transport equation in the context of Large Eddy Simulation (LES). The curvature term has been split into the contributions arising due to the reaction and normal diffusion components of displacement speed and the term originating from the tangential diffusion component of displacement speed. Subsequently, these contributions of the curvature term have been split into the resolved and subgrid contributions. New models have been proposed for the subgrid curvature terms arising from the combined reaction and normal diffusion components and the tangential diffusion component of displacement speed. The performances of the new model and the existing models for the subgrid curvature term have been compared with the corresponding quantity extracted from the explicitly filtered DNS data. The new model for the subgrid curvature term is shown to perform satisfactorily in all cases considered in the current study, accounting for wide variations in LES filter size

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