4.7 Article

A Comparison of Linear Solvers for Resolving Flow in Three-Dimensional Discrete Fracture Networks

期刊

WATER RESOURCES RESEARCH
卷 58, 期 4, 页码 -

出版社

AMER GEOPHYSICAL UNION
DOI: 10.1029/2021WR031188

关键词

fracture flow; discrete fracture network; numerical methods

资金

  1. United States Department of Energy through the Computational Science Graduate Fellowship (DOE CSGF) [DE-SC0019323]
  2. Department of Energy Basic Energy Sciences program [LANLE3W1]
  3. Spent Fuel and Waste Science and Technology Campaign, Office of Nuclear Energy, of the U.S. Department of Energy
  4. National Nuclear Security Administration of U.S. Department of Energy [89233218CNA000001]
  5. U.S. DOE through the Laboratory Directed Research and Development program of Los Alamos National Laboratory [20200575ECR]
  6. agency of the United States Government

向作者/读者索取更多资源

This study compares various methods for solving steady flow in three-dimensional discrete fracture networks. The methods were evaluated based on compute times and scaling of the solution. The results showed that in most cases, a direct solution using Cholesky factorization outperformed other methods, but the conjugate gradient method with an AMG preconditioner also showed good performance.
We compare various methods for resolving steady flow within three-dimensional discrete fracture networks, including direct methods, Krylov subspace methods with and without preconditioning, and multi-grid methods. We compared the performance of the methods based on compute times and scaling of the solution as a function of the number of grid nodes and log-variance of the hydraulic aperture. The methods are applied to three test cases: (a) variable density of networks with a truncated power-law distribution of fracture lengths, (b) a fixed network composed of monodisperse fracture sizes but varied permeability/aperture heterogeneity, (c) and a network based on field site in Nevada, US. We chose these cases to allow us to study the impact of the mesh size and flow properties, as well as to demonstrate our conclusions on a large-scale, realistic problem (more than 40 million mesh nodes). A direct solution using Cholesky factorization outperformed other methods for every example but was closely followed in performance by some algebraic multigrid (AMG) preconditioned Krylov subspace methods. Among the Krylov methods, conjugate gradients (CG) with an AMG preconditioner performs the best. Generally, Cholesky factorization is recommended, but CG with an AMG preconditioner may be suitable for very large problems beyond 40 million nodes where the entire linear system cannot reside in memory.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据