Navier-Stokes方程时空有限元离散化的几何多重网格方法及其在三维流动模拟中的应用

IF 2.7 1区 数学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Mathias Anselmann, M. Bause
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引用次数: 9

摘要

我们提出了一种基于单元Vanka平滑器的并行几何多重网格(GMG)方法,用于不可压缩Navier–Stokes方程的高阶时空有限元方法(STFEM)。STFEM被实现为时间行进方案。GMG求解器被用作广义最小残差迭代的预处理器。它的性能特性在圆柱体周围流动的2D和3D基准上进行了演示。GMG方法的关键组成部分是在各个子区间的所有时间自由度上构造局部Vanka平滑器及其有效应用。为此,生成了存储所有层次级别的雅可比矩阵的预先计算的单元逆并且只需要合理量的存储器开销的数据结构。GMG方法是为这笔交易而构建的。II有限元库。这些概念是灵活的,可以转移到类似的软件平台上。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Geometric Multigrid Method for Space-Time Finite Element Discretizations of the Navier–Stokes Equations and its Application to 3D Flow Simulation
We present a parallelized geometric multigrid (GMG) method, based on the cell-based Vanka smoother, for higher order space-time finite element methods (STFEM) to the incompressible Navier–Stokes equations. The STFEM is implemented as a time marching scheme. The GMG solver is applied as a preconditioner for generalized minimal residual iterations. Its performance properties are demonstrated for 2D and 3D benchmarks of flow around a cylinder. The key ingredients of the GMG approach are the construction of the local Vanka smoother over all degrees of freedom in time of the respective subinterval and its efficient application. For this, data structures that store pre-computed cell inverses of the Jacobian for all hierarchical levels and require only a reasonable amount of memory overhead are generated. The GMG method is built for the deal.II finite element library. The concepts are flexible and can be transferred to similar software platforms.
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来源期刊
ACM Transactions on Mathematical Software
ACM Transactions on Mathematical Software 工程技术-计算机:软件工程
CiteScore
5.00
自引率
3.70%
发文量
50
审稿时长
>12 weeks
期刊介绍: As a scientific journal, ACM Transactions on Mathematical Software (TOMS) documents the theoretical underpinnings of numeric, symbolic, algebraic, and geometric computing applications. It focuses on analysis and construction of algorithms and programs, and the interaction of programs and architecture. Algorithms documented in TOMS are available as the Collected Algorithms of the ACM at calgo.acm.org.
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