Linear stability of athermal regularized lattice Boltzmann methods

TL;DR

This paper analyzes the linear stability of various regularized lattice Boltzmann methods, revealing that recursive regularization offers superior stability, especially at zero viscosity, due to mode filtering and dissipation properties.

Contribution

It provides a systematic linear stability analysis of regularized LB models, highlighting the superior stability of recursive regularization and clarifying the physical interpretation of LB modes.

Findings

Recursive regularization is the most stable model for D2Q9 lattice.

Regularized models exhibit mode filtering and anisotropic dissipation.

Stability is linked to off-equilibrium population reconstruction.

Abstract

The present work is dedicated to a better understanding of the stability properties of regularized lattice Boltzmann (LB) schemes. To this extent, linear stability analyses of two-dimensional models are proposed: the standard Bhatnagar-Gross-Krook (BGK) collision model, the original pre-collision regularization and the recursive regularized model, where off-equilibrium distributions are partially computed thanks to a recursive formula. A systematic identification of the physical content carried by each LB mode is done by analyzing the eigenvectors of the linear systems. Stability results are then numerically confirmed by performing simulations of shear and acoustic waves. This work allows drawing fair conclusions on the stability properties of each model. In particular, recursive regularization turns out to be the most stable model for the D2Q9 lattice, especially in the zero-viscosity limit. Two major properties shared by every regularized model are highlighted: (1) a mode filtering property, and (2) an incorrect, and broadly anisotropic, dissipation rate of the modes carrying physical waves in under-resolved conditions. The first property is the main source of increased stability, especially for the recursive regularization. It is a direct consequence of the reconstruction of off-equilibrium populations before each collision process, decreasing the rank of the system of discrete equations. The second property seems to be related to numerical errors directly induced by the equilibration of high-order moments. In such a case, this property is likely to occur with any collision model that follows such a stabilization methodology.