AuthorsE. Ahmed, A. Fumagalli and A. Budiša
TitleA multiscale flux basis for mortar mixed discretizations of reduced Darcy–Forchheimer fracture models
AfilliationScientific Computing
Project(s)No Simula project
StatusPublished
Publication TypeJournal Article
Year of Publication2019
JournalComputer Methods in Applied Mechanics and Engineering
Volume354
Pagination16 - 36
Date Published05/2019
PublisherElsevier
ISSN00457825
Abstract

In this paper, a multiscale flux basis algorithm is developed to efficiently solve a flow problem in fractured porous media. Here, we take into account a mixed-dimensional setting of the discrete fracture matrix model, where the fracture network is represented as lower-dimensional object. We assume the linear Darcy model in the rock matrix and the non-linear Forchheimer model in the fractures. In our formulation, we are able to reformulate the matrix–fracture problem to only the fracture network problem and, therefore, significantly reduce the computational cost. The resulting problem is then a non-linear interface problem that can be solved using a fixed-point or Newton–Krylov methods, which in each iteration require several solves of Robin problems in the surrounding rock matrices. To achieve this, the flux exchange (a linear Robin-to-Neumann co-dimensional mapping) between the porous medium and the fracture network is done offline by pre-computing a multiscale flux basis that consists of the flux response from each degree of freedom (DOF) on the fracture network. This delivers a conserve for the basis that handles the solutions in the rock matrices for each degree of freedom in the fractures pressure space. Then, any Robin sub-domain problems are replaced by linear combinations of the multiscale flux basis during the interface iteration. The proposed approach is, thus, agnostic to the physical model in the fracture network. Numerical experiments demonstrate the computational gains of pre-computing the flux exchange between the porous medium and the fracture network against standard non-linear domain decomposition approaches.

URLhttps://www.sciencedirect.com/science/article/pii/S0045782519303044
DOI10.1016/j.cma.2019.05.034
Citation Key27771