{"title":"A parallel domain decomposition-based implicit finite volume lattice Boltzmann method for incompressible thermal convection flows on unstructured grids","authors":"Lei Xu , Rongliang Chen , Linyan Gu , Wu Zhang","doi":"10.1016/j.cam.2025.116578","DOIUrl":null,"url":null,"abstract":"<div><div>The double distribution function lattice Boltzmann method is known for its ability to handle various temperature changes and maintain strong numerical stability for incompressible thermal convection flows. However, being an explicit scheme on a Cartesian grid, it necessitates small time step sizes and limits its use in simulating fluid flows with intricate geometries. In this paper, a parallel fully implicit finite volume lattice Boltzmann method for incompressible thermal convection flows on unstructured grids is introduced. The double distribution function lattice Boltzmann equations are discretized by a finite volume method in space and an implicit backward Euler scheme in time. The resulting large sparse nonlinear system of algebraic equations is solved by a highly parallel Schwarz type domain decomposition preconditioned Newton–Krylov algorithm. The effectiveness of the proposed method is validated through five benchmark problems with a wide range of Rayleigh numbers: (a) porous plate problem with a temperature gradient, (b) natural convection in a square cavity, (c) natural convection in a concentric annulus, (d) mixed heat transfer from a heated circular cylinder and (e) nature convection in a sine-walled cavity. The numerical results demonstrate the robustness of the proposed method across all test cases, achieving a linear speedup in solving a problem with almost 40 million degrees of freedom using thousands of processor cores. The corresponding parallel efficiency reaches as high as 91.96% using 4096 processor cores.</div></div>","PeriodicalId":50226,"journal":{"name":"Journal of Computational and Applied Mathematics","volume":"465 ","pages":"Article 116578"},"PeriodicalIF":2.1000,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Computational and Applied Mathematics","FirstCategoryId":"100","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0377042725000937","RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATHEMATICS, APPLIED","Score":null,"Total":0}
引用次数: 0
Abstract
The double distribution function lattice Boltzmann method is known for its ability to handle various temperature changes and maintain strong numerical stability for incompressible thermal convection flows. However, being an explicit scheme on a Cartesian grid, it necessitates small time step sizes and limits its use in simulating fluid flows with intricate geometries. In this paper, a parallel fully implicit finite volume lattice Boltzmann method for incompressible thermal convection flows on unstructured grids is introduced. The double distribution function lattice Boltzmann equations are discretized by a finite volume method in space and an implicit backward Euler scheme in time. The resulting large sparse nonlinear system of algebraic equations is solved by a highly parallel Schwarz type domain decomposition preconditioned Newton–Krylov algorithm. The effectiveness of the proposed method is validated through five benchmark problems with a wide range of Rayleigh numbers: (a) porous plate problem with a temperature gradient, (b) natural convection in a square cavity, (c) natural convection in a concentric annulus, (d) mixed heat transfer from a heated circular cylinder and (e) nature convection in a sine-walled cavity. The numerical results demonstrate the robustness of the proposed method across all test cases, achieving a linear speedup in solving a problem with almost 40 million degrees of freedom using thousands of processor cores. The corresponding parallel efficiency reaches as high as 91.96% using 4096 processor cores.
期刊介绍:
The Journal of Computational and Applied Mathematics publishes original papers of high scientific value in all areas of computational and applied mathematics. The main interest of the Journal is in papers that describe and analyze new computational techniques for solving scientific or engineering problems. Also the improved analysis, including the effectiveness and applicability, of existing methods and algorithms is of importance. The computational efficiency (e.g. the convergence, stability, accuracy, ...) should be proved and illustrated by nontrivial numerical examples. Papers describing only variants of existing methods, without adding significant new computational properties are not of interest.
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