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首页> 外文期刊>Journal of Computational Physics >Preconditioning methods for discontinuous Galerkin solutions of the Navier-Stokes equations
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Preconditioning methods for discontinuous Galerkin solutions of the Navier-Stokes equations

机译:Navier-Stokes方程的不连续Galerkin解的预处理方法

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A Newton-Krylov method is developed for the solution of the steady compressible Navier-Stokes equations using a discontinuous Galerkin (DG) discretization on unstructured meshes. Steady-state solutions are obtained using a Newton-Krylov approach where the linear system at each iteration is solved using a restarted GMRES algorithm. Several different preconditioners are examined to achieve fast convergence of the GMRES algorithm. An element Line-Jacobi preconditioner is presented which solves a block-tridiagonal system along lines of maximum coupling in the flow. An incomplete block-LU factorization (Block-ILU(0)) is also presented as a preconditioner, where the factorization is performed using a reordering of elements based upon the lines of maximum coupling. This reordering is shown to be superior to standard reordering techniques (Nested Dissection, One-way Dissection, Quotient Minimum Degree, Reverse Cuthill-Mckee) especially for viscous test cases. The Block-ILU(0) factorization is performed in-place and an algorithm is presented for the application of the linearization which reduces both the memory and CPU time over the traditional dual matrix storage format. Additionally, a linear p-multigrid preconditioner is also considered, where Block-Jacobi, Line-Jacobi and Block-ILU(0) are used as smoothers. The linear multigrid preconditioner is shown to significantly improve convergence in term of number of iterations and CPU time compared to a single-level Block-Jacobi or Line-Jacobi preconditioner. Similarly the linear multigrid preconditioner with Block-ILU smoothing is shown to reduce the number of linear iterations to achieve convergence over a single-level Block-ILU(0) preconditioner, though no appreciable improvement in CPU time is shown.
机译:针对非结构网格上的不连续Galerkin(DG)离散化,开发了Newton-Krylov方法来求解稳态可压缩Navier-Stokes方程。使用Newton-Krylov方法获得稳态解,其中使用重新启动的GMRES算法求解每次迭代时的线性系统。研究了几种不同的预处理器,以实现GMRES算法的快速收敛。提出了一种元素Line-Jacobi预处理器,它可以沿着流中最大耦合的线求解块对角线系统。不完整的块LU分解(Block-ILU(0))也作为预处理器出现,其中分解是基于最大耦合线使用元素的重新排序来执行的。这种重新排序被证明优于标准的重新排序技术(嵌套解剖,单向解剖,商最小度,反向Cuthill-Mckee),特别是对于粘性测试用例。在原位执行Block-ILU(0)分解,并提出了一种用于线性化应用的算法,与传统的双矩阵存储格式相比,该算法减少了内存和CPU时间。此外,还考虑了线性p多重网格预处理器,其中使用Block-Jacobi,Line-Jacobi和Block-ILU(0)作为平滑器。与单级Block-Jacobi或Line-Jacobi预处理器相比,线性多网格预处理器在迭代次数和CPU时间方面显着提高了收敛性。类似地,显示了具有Block-ILU平滑功能的线性多网格预处理器可以减少线性迭代的次数,从而在单级Block-ILU(0)预处理器上实现收敛,尽管未显示CPU时间有明显改善。

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