Algebraic multigrid methods for saddle point systems arising from mortar contact formulations. (5th May 2021)

• Record Type: Journal Article
• Authors: Wiesner, Tobias A.; Mayr, Matthias; Popp, Alexander; Gee, Michael W.; Wall, Wolfgang A.
• Is Part Of: International journal for numerical methods in engineering. Volume 122:Number 15(2021)
• Page Start: 3749
• Page End: 3779
• Publication Date: 2021-05-05
• Subjects: algebraic multigrid methods -- contact mechanics -- iterative linear solvers -- mortar methods -- preconditioning; Numerical analysis -- Periodicals; Engineering mathematics -- Periodicals; 620.001518
• DOI: 10.1002/nme.6680
• Languages: English
• ISSNs: 0029-5981

Abstract

Abstract: In this article, a fully aggregation‐based algebraic multigrid strategy is developed for nonlinear contact problems of saddle point type using a mortar finite element approach. While the idea of extending multigrid methods to saddle point systems can already be found, for example, in the context of Stokes and Oseen equations in literature, the main contributions of this work are (i) the development and open‐source implementation of an interface aggregation strategy specifically suited for generating Lagrange multiplier aggregates that are required for coupling structural equilibrium equations with contact constraints and (ii) a review of saddle point smoothers in the context of constrained interface problems. The new interface aggregation strategy perfectly fits into an aggregation‐based multigrid framework and can easily be combined with segregated transfer operators, which allow to preserve the saddle point structure on the coarse levels. Further analysis provides insight into saddle point smoothers applied to contact problems, while numerical experiments illustrate the robustness of the new method. We have implemented the proposed algorithm within the MueLu package of the open‐source Trilinos project. Numerical examples demonstrate the robustness of the proposed method in complex dynamic contact problems as well as its scalability up to 23.9 million unknowns on 480 MPI ranks.