Abstract: The Lattice Boltzmann method(LBM) with local mesh refinement is widely used in large-scale unsteady computational fluid dynamics problems.Although the local mesh refinement method can effectively reduce the computational workload,it also brings serious load balancing issues.Especially in large-scale parallel computing,the choice of load balancing algorithms has a significant impact on the overall computational efficiency.This paper used a self-developed LBM program to compare the implementations of Palabos and to explore the static load balancing algorithm based on the Lattice Boltzmann method with local mesh refinement.This paper implementes various load balancing strategies based on global load and stratified load,uses greedy algorithms and space-filling curve algorithms,and proposes optimization of the load balancing of computing nodes.It uses the flow around a sphere and the DrivAer case as test cases for testing and comparison.Firstly,the results show the performance based on the stratified load strategy is better than that based on the global load strategy.At the same time,a comparative analysis reveals that the greedy algorithm demostrates superior scalability,whereas the space-filling curve algorithmexhibits higher efficiency when operating with a limited number of process.Finally,based on layered load,a hybrid algorithm is implemented by combining greedy algorithm and space filling curve algorithm,which achieves the best performance when the number of processes is larger.

Key words: Lattice Boltzmann method, Load balancing, Local grid refinement, Parallel computing, Node-wise load balancing