Volltext-Downloads (blau) und Frontdoor-Views (grau)

Lattice Boltzmann method with artificial bulk viscosity using a neural collision operator

  • The lattice Boltzmann method (LBM) stands apart from conventional macroscopic approaches due to its low numerical dissipation and reduced computational cost, attributed to a simple streaming and local collision step. While this property makes the method particularly attractive for applications such as direct noise computation, it also renders the method highly susceptible to instabilities. A vast body of literature exists on stability-enhancing techniques, which can be categorized into selective filtering, regularized LBM, and multi-relaxation time (MRT) models. Although each technique bolsters stability by adding numerical dissipation, they act on different modes. Consequently, there is not a universal scheme optimally suited for a wide range of different flows. The reason for this lies in the static nature of these methods; they cannot adapt to local or global flow features. Still, adaptive filtering using a shear sensor constitutes an exception to this. For this reason, we developed a novel collision operator that uses space- and time-variant collision rates associated with the bulk viscosity. These rates are optimized by a physically informed neural net. In this study, the training data consists of a time series of different instances of a 2D barotropic vortex solution, obtained from a high-order Navier–Stokes solver that embodies desirable numerical features. For this specific text case our results demonstrate that the relaxation times adapt to the local flow and show a dependence on the velocity field. Furthermore, the novel collision operator demonstrates a better stability-to-precision ratio and outperforms conventional techniques that use an empirical constant for the bulk viscosity.

Download full text files

Export metadata

Additional Services

Search Google Scholar Check availability


Show usage statistics
Document Type:Article
Author:Jan Tobias Horstmann, Mario Christopher Bedrunka, Holger Foysi
Parent Title (English):Computers & Fluids
Article Number:106191
Number of pages:13
Publishing Institution:Hochschule Bonn-Rhein-Sieg
Date of first publication:2024/02/02
Copyright:© 2024 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license.
Keyword:Artificial bulk viscosity; MRT-LBM; Neural collision operator; Numerical stability
Departments, institutes and facilities:Fachbereich Ingenieurwissenschaften und Kommunikation
Institut für Technik, Ressourcenschonung und Energieeffizienz (TREE)
Dewey Decimal Classification (DDC):6 Technik, Medizin, angewandte Wissenschaften / 62 Ingenieurwissenschaften / 620 Ingenieurwissenschaften und zugeordnete Tätigkeiten
Entry in this database:2024/02/09
Licence (German):License LogoCreative Commons - CC BY - Namensnennung 4.0 International