A Vanka‐based parameter‐robust multigrid relaxation for the Stokes–Darcy Brinkman problems

IF 1.8 3区 数学 Q1 MATHEMATICS
Yunhui He
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引用次数: 0

Abstract

Abstract We consider a block‐structured multigrid method based on Braess–Sarazin relaxation for solving the Stokes–Darcy Brinkman equations discretized by the marker and cell scheme. In the relaxation scheme, an element‐based additive Vanka operator is used to approximate the inverse of the corresponding shifted Laplacian operator involved in the discrete Stokes–Darcy Brinkman system. Using local Fourier analysis, we present the stencil for the additive Vanka smoother and derive an optimal smoothing factor for Vanka‐based Braess–Sarazin relaxation for the Stokes–Darcy Brinkman equations. Although the optimal damping parameter is dependent on meshsize and physical parameter, it is very close to one. In practice, we find that using three sweeps of Jacobi relaxation on the Schur complement system is sufficient. Numerical results of two‐grid and V(1,1)‐cycle are presented, which show high efficiency of the proposed relaxation scheme and its robustness to physical parameters and the meshsize. Using a damping parameter equal to one gives almost the same convergence results as these for the optimal damping parameter.
Stokes-Darcy Brinkman问题的基于Vanka的参数鲁棒多网格松弛
摘要:我们考虑了一种基于Braess-Sarazin松弛的块结构多重网格方法,用于求解由标记和单元格式离散的Stokes-Darcy Brinkman方程。在松弛方案中,使用基于元素的加性Vanka算子来近似离散Stokes-Darcy Brinkman系统中相应移位拉普拉斯算子的逆。利用局部傅里叶分析,我们给出了Vanka光滑的模板,并推导了Stokes-Darcy Brinkman方程的基于Vanka的braress - sarazin松弛的最佳平滑因子。虽然最优阻尼参数依赖于网格尺寸和物理参数,但它非常接近于1。在实践中,我们发现在Schur补系统上使用三遍Jacobi松弛就足够了。两网格和V(1,1)循环的数值结果表明,所提出的松弛方案效率高,对物理参数和网格尺寸具有鲁棒性。使用等于1的阻尼参数得到的收敛结果与最优阻尼参数的收敛结果几乎相同。
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来源期刊
CiteScore
3.40
自引率
2.30%
发文量
50
审稿时长
12 months
期刊介绍: Manuscripts submitted to Numerical Linear Algebra with Applications should include large-scale broad-interest applications in which challenging computational results are integral to the approach investigated and analysed. Manuscripts that, in the Editor’s view, do not satisfy these conditions will not be accepted for review. Numerical Linear Algebra with Applications receives submissions in areas that address developing, analysing and applying linear algebra algorithms for solving problems arising in multilinear (tensor) algebra, in statistics, such as Markov Chains, as well as in deterministic and stochastic modelling of large-scale networks, algorithm development, performance analysis or related computational aspects. Topics covered include: Standard and Generalized Conjugate Gradients, Multigrid and Other Iterative Methods; Preconditioning Methods; Direct Solution Methods; Numerical Methods for Eigenproblems; Newton-like Methods for Nonlinear Equations; Parallel and Vectorizable Algorithms in Numerical Linear Algebra; Application of Methods of Numerical Linear Algebra in Science, Engineering and Economics.
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