Isogeometric Residual Minimization Method (iGRM) with direction splitting preconditioner for stationary advection-dominated diffusion problems

被引:12
|
作者
Calo, V. M. [3 ,4 ]
Los, M. [1 ]
Deng, Q. [3 ,5 ]
Muga, I. [2 ]
Paszynski, M. [1 ]
机构
[1] AGH Univ Sci & Technol, Dept Comp Sci, Krakow, Poland
[2] Pontificia Univ Catolica Valparaiso, Inst Matemat, Valparaiso, Chile
[3] Curtin Univ, Fac Sci & Engn, Appl Geol, Perth, WA, Australia
[4] CSIRO, Mineral Resources, Kensington, WA 6152, Australia
[5] Univ Wisconsin, Dept Math, Madison, WI 53706 USA
基金
欧盟地平线“2020”;
关键词
Isogeometric analysis; Residual minimization; Iteration solvers; Advection-diffusion simulations; Linear computational cost; Preconditioners; NUMERICAL-SOLUTION; SUPG FORMULATION; STABILIZATION; CONVERGENCE; DYNAMICS; SOLVERS;
D O I
10.1016/j.cma.2020.113214
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
In this paper, we introduce the isoGeometric Residual Minimization (iGRM) method. The method solves stationary advection-dominated diffusion problems. We stabilize the method via residual minimization. We discretize the problem using B-spline basis functions. We then seek to minimize the isogeometric residual over a spline space built on a tensor product mesh. We construct the solution over a smooth subspace of the residual. We can specify the solution subspace by reducing the polynomial order, by increasing the continuity, or by a combination of these. The Gramm matrix for the residual minimization method is approximated by a weighted H-1 norm, which we can express as Kronecker products, due to the tensor-product structure of the approximations. We use the Gramm matrix as a preconditional which can be applied in a computational cost proportional to the number of degrees of freedom in 2D and 3D. Building on these approximations, we construct an iterative algorithm. We test the residual minimization method on several numerical examples, and we compare it to the Discontinuous Petrov-Galerkin (DPG) and the Streamline Upwind Petrov-Galerkin (SUPG) stabilization methods. The iGRM method delivers similar quality solutions as the DPG method, it uses smaller grids, it does not require breaking of the spaces, but it is limited to tensor-product meshes. The computational cost of the iGRM is higher than for SUPG, but it does not require the determination of problem specific parameters. (C) 2020 The Author(s). Published by Elsevier B.V.
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页数:16
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