Novel mixed approximate deconvolution subgrid-scale models for large-eddy simulation

Abstract

Approximate deconvolution (AD) has emerged as a promising closure for large-eddy simulation in complex multi-physics flows, where the conventional pure dynamic eddy-viscosity (DEV) models experience issues. In this research, we propose novel improved mixed hard-deconvolution or secondary-regularization models and compare their performance with the existing standard mixed AD-DEV and penalty-term regularizations. For this aim, five consistency criteria, based on the properties of the modeled sub-filter-scale stress in limiting conditions, are introduced for the first time. It is proved that the conventional hard-deconvolution models do not adhere to a couple of important primary criteria. Furthermore, through a priori and a posteriori analyses of Burgers turbulence and turbulent channel flow, it is manifested that the inconsistency with the primary criteria can result in larger modeling errors, the over-prediction and pileup of kinetic energy in eddies of a length scale between the explicit filter width and grid size, and even the solution instability. On the other hand, the favorable characteristics of the new mixed models, in terms of the consistency criteria, significantly improve the accuracy of the predictions, the solution stability, and even the computational cost, particularly for one of the new models called mixed alternative-DEV (A-DEV).