Local mesh refinement sensor for the lattice Boltzmann method

TL;DR

This paper introduces a new kinetic sensor for lattice Boltzmann methods that effectively guides static and dynamic mesh refinement, improving computational efficiency across various flow regimes.

Contribution

A novel kinetic-based mesh refinement sensor for LBMs that outperforms existing sensors and adapts meshes effectively for static and dynamic simulations.

Findings

Sensor produces high-quality meshes for turbulent flows.

Sensor enables computational time savings.

Effective in static and dynamic mesh refinement scenarios.

Abstract

A novel mesh refinement sensor is proposed for lattice Boltzmann methods (LBMs) applicable to either static or dynamic mesh refinement algorithms. The sensor exploits the kinetic nature of LBMs by evaluating the departure of distribution functions from their local equilibrium state. This sensor is first compared, in a qualitative manner, to three state-of-the-art sensors: (1) the vorticity norm, (2) the Q-criterion, and (3) spatial derivatives of the vorticity. This comparison shows that our kinetic sensor is the most adequate candidate to propose tailored mesh structures across a wide range of physical phenomena: incompressible, compressible subsonic/supersonic single phase, and weakly compressible multiphase flows. As a more quantitative validation, the sensor is then used to produce the computational mesh for two existing open-source LB solvers based on inhomogeneous, block-structured meshes with static and dynamic refinement algorithms, implemented in the Palabos and AMROC-LBM software, respectively. The sensor is first used to generate a static mesh to simulate the turbulent 3D lid-driven cavity flow using Palabos. AMROC-LBM is then adopted to confirm the ability of our sensor to dynamically adapt the mesh to reach the steady state of the 2D lid-driven cavity flow. Both configurations show that our sensor successfully produces meshes of high quality and allows to save computational time.