Iterative multiscale finite-volume method

被引:167
作者
Hajibeygi, Hadi [1 ]
Bonfigli, Giuseppe [1 ]
Hesse, Marc Andre [2 ]
Jenny, Patrick [1 ]
机构
[1] ETH, Inst Fluid Dynam, CH-8092 Zurich, Switzerland
[2] Stanford Univ, Dept Energy Resource Engn, Stanford, CA 94305 USA
关键词
iterative multiscale methods; multiscale finite-volume method; elliptic problem; Poisson equation; line relaxation; multiphase flow; porous media;
D O I
10.1016/j.jcp.2008.06.013
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
The multiscale finite-volume (MSFV) method for the solution of elliptic problems is extended to an efficient iterative algorithm that converges to the fine-scale numerical solution. The localization errors in the MSFV method are systematically reduced by updating the local boundary conditions with global information. This iterative multiscale finite-volume (i-MSFV) method allows the conservative reconstruction of the velocity field after any iteration, and the MSFV method is recovered, if the velocity field is reconstructed after the first iteration. Both the i-MSFV and the MSFV methods lead to substantial computational savings, where an approximate but locally conservative solution of an elliptic problem is required. In contrast to the MSFV method, the i-MSFV method allows a systematic reduction of the error in the multiscale approximation. Line relaxation in each direction is used as an efficient smoother at each iteration. This smoother is essential to obtain convergence in complex, highly anisotropic, heterogeneous domains. Numerical convergence of the method is verified for different test cases ranging from a standard Poisson equation to highly heterogeneous, anisotropic elliptic problems. Finally, to demonstrate the efficiency of the method for multiphase transport in porous media, it is shown that it is sufficient to apply the iterative smoothing procedure for the improvement of the localization assumptions only infrequently, i.e. not every time step. This result is crucial, since it shows that the overall efficiency of the i-MSFV algorithm is comparable with the original MSFV method. At the same time, the solutions are significantly improved, especially for very challenging cases. (C) 2008 Elsevier Inc. All rights reserved.
引用
收藏
页码:8604 / 8621
页数:18
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