A scalable preconditioning framework for stabilized contact mechanics with hydraulically active fractures

A preconditioning framework for the coupled problem of frictional contact mechanics and fluid flow in the fracture network is presented. We focus on a blended finite element/finite volume method, where the porous medium is discretized by low-order continuous finite elements with nodal unknowns, cell-centered Lagrange multipliers are used to prescribe the contact constraints, and the fluid flow in the fractures is described by a classical two-point flux approximation scheme. This formulation is consistent, but is not uniformly inf-sup bounded and requires a stabilization. For the resulting 3 x 3 block Jacobian matrix, robust and efficient solution methods are not available, so we aim at designing new scalable preconditioning strategies based on the physically-informed block partitioning of the unknowns and state-of-the-art multigrid techniques. The key idea is to restrict the system to a single-physics problem, approximately solve it by an inner algebraic multigrid approach, and finally prolong it back to the fully-coupled problem. Two different techniques are presented, analyzed and compared by changing the ordering of the restrictions. Numerical results illustrate the algorithmic scalability, the impact of the relative number of fracture-based unknowns, and the performance on a benchmark problem. The objective of the analysis is to identify the most promising solution strategy. (c) 2022 Elsevier Inc. All rights reserved.

Automated summary

This paper presents a preconditioning framework for the coupled problem of frictional contact mechanics and fluid flow in the fracture network. A blended finite element/finite volume method is used to discretize the porous medium, and cell-centered Lagrange multipliers are employed to represent the contact constraints. The fluid flow in the fractures is described by a classical two-point flux approximation scheme. The authors aim to design scalable preconditioning strategies based on block partitioning of the unknowns and multigrid techniques.