Parallel Optimization for Coupled Lattice Boltzmann-Finite Volume Method on Heterogeneous Many-Core Supercomputer

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Authors: Xiaojing Lv, Chengsheng Wu, Zhao Liu, Yujing Fan, Jianchun WANG, Yaying Zhang, Yixing Jin, Xuesen Chu
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Abstract

Nowadays various coupling strategies have been developed to combine the strengths of different numerical methods in computational fluid dynamics (CFD), among which the coupled algorithm of the lattice Boltzmann-finite volume method (LBM-FVM) has gained widespread attention. However, research on parallel optimization of LBM-FVM coupled solvers remains limited, mostly focused on independent solvers. In this work, we proposed a flexible framework and optimization schemes to explore the coordinated balance of accuracy-efficiency-hardware adaptability. First, we designed a processor layout strategy to address load imbalance and communication redundancy in the coupled solver. We then developed several parallelization techniques, including LBM restructuring, data reuse, and SIMD optimization for targeted kernels on the most advanced architecture of the Sunway series in China, namely SW26010P heterogeneous many-core processors, which provide hardware architectural advantages well suited for large-scale parallel computational fluid dynamics. Finally, the accuracy of the LBM-FVM coupling simulations was validated through benchmark simulations of 2D/3D lid-driven cavity flow. The results show that our LBM-FVM coupling solver can accurately capture flow characteristics, with vortex structures consistent with experimental data. Additionally, we achieved a 152× speedup for the LBM solver and a 126× speedup for the coupled simulation compared to the standalone FVM simulation on the New Sunway supercomputer system. Our approach marks a milestone in the field of LBM implementations and provides a promising future for coupled algorithms in CFD.

Fields of Study & Subjects

Lattice Boltzmann Simulation Studies Fluid Dynamics and Vibration Analysis Fluid Dynamics and Heat Transfer

Publication details

Journal / source
Applied Sciences
Volume
16
Issue
2
Pages
721–721
Publication date
9 January 2026
Language
EN