Pseudoentropic derivation of the regularized lattice Boltzmann method

Andreas Krämer, Dominik Wilde, Knut Küllmer, Dirk Reith, and Holger Foysi
Phys. Rev. E 100, 023302 – Published 7 August 2019
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Abstract

The lattice Boltzmann method (LBM) facilitates efficient simulations of fluid turbulence based on advection and collision of local particle distribution functions. To ensure stable simulations on underresolved grids, the collision operator must prevent drastic deviations from local equilibrium. This can be achieved by various methods, such as the multirelaxation time, entropic, quasiequilibrium, regularized, and cumulant schemes. Complementing a part of a unified theoretical framework of these schemes, the present work presents a derivation of the regularized lattice Boltzmann method (RLBM), which follows a recently introduced entropic multirelaxation time LBM by Karlin, Bösch, and Chikatamarla (KBC). It is shown that both methods can be derived by locally maximizing a quadratic Taylor expansion of the entropy function. While KBC expands around the local equilibrium distribution, the RLBM is recovered by expanding entropy around a global equilibrium. Numerical tests were performed to elucidate the role of pseudoentropy maximization in these models. Simulations of a two-dimensional shear layer show that the RLBM successfully reproduces the largest eddies even on a 16×16 grid, while the conventional LBM becomes unstable for grid resolutions of 128×128 and lower. The RLBM suppresses spurious vortices more effectively than KBC. In contrast, simulations of the three-dimensional Taylor-Green and Kida vortices show that KBC performs better in resolving small scale vortices, outperforming the RLBM by a factor of 1.8 in terms of the effective Reynolds number.

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  • Received 8 February 2019
  • Corrected 22 January 2020

DOI:https://doi.org/10.1103/PhysRevE.100.023302

©2019 American Physical Society

Physics Subject Headings (PhySH)

Fluid Dynamics

Corrections

22 January 2020

Correction: The previously published Figure 9 contained a minor error and has been replaced.

Authors & Affiliations

Andreas Krämer1,2,*, Dominik Wilde1,3,†, Knut Küllmer1,3,‡, Dirk Reith1,4,§, and Holger Foysi3,¶

  • 1Institute of Technology, Resource and Energy-efficient Engineering (TREE), Bonn-Rhein-Sieg University of Applied Sciences, Grantham-Allee 20, 53757 Sankt Augustin, Germany
  • 2National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, United States
  • 3Department of Mechanical Engineering, University of Siegen, Paul-Bonatz-Straße 9-11, 57076 Siegen-Weidenau, Germany
  • 4Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, 53754 Sankt Augustin, Germany

  • *kraemer.research@gmail.com
  • dominik.wilde@h-brs.de, wilde.aerospace@gmail.com
  • knut.kuellmer@h-brs.de
  • §dirk.reith@h-brs.de
  • holger.foysi@uni-siegen.de

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Issue

Vol. 100, Iss. 2 — August 2019

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