Navier-Stokes方程等几何离散化增广拉格朗日预处理中的参数

Pub Date : 2022-02-17 DOI:10.21136/AM.2022.0130-21
Jiří Egermaier, Hana Horníková
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引用次数: 0

摘要

本文讨论了由不可压缩Navier-Stokes方程离散化得到的线性系统的有效解的GMRES方法的增广拉格朗日预处理的参数γ的最优选择。我们使用基于B样条的等几何分析方法来考虑方程的离散化。我们对各种问题参数(雷诺数、网格细化)的收敛性对参数γ的依赖性感兴趣,尤其是对各种等几何离散化(B样条离散化基的阶数和单元间连续性)。其思想是能够为计算成本相对较低的问题确定γ的最佳值,并基于该值为其他问题预测合适的值,例如,使用更精细的网格、不同的离散化等。还讨论了内部求解器(基于多重网格方法的直接或迭代)的影响。
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On the parameter in augmented Lagrangian preconditioning for isogeometric discretizations of the Navier-Stokes equations

In this paper, we deal with the optimal choice of the parameter γ for augmented Lagrangian preconditioning of GMRES method for efficient solution of linear systems obtained from discretization of the incompressible Navier-Stokes equations. We consider discretization of the equations using the B-spline based isogeometric analysis approach. We are interested in the dependence of the convergence on the parameter γ for various problem parameters (Reynolds number, mesh refinement) and especially for various isogeometric discretizations (degree and interelement continuity of the B-spline discretization bases). The idea is to be able to determine the optimal value of γ for a problem that is relatively cheap to compute and, based on this value, predict suitable values for other problems, e.g., with finer mesh, different discretization, etc. The influence of inner solvers (direct or iterative based on multigrid method) is also discussed.

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