Monolithic Multigrid for a Reduced-Quadrature Discretization of Poroelasticity

Adler, James H.; He, Yunhui; Hu, Xiaozhe; MacLachlan, Scott; Ohm, Peter

doi:10.1137/21m1429072

Monolithic Multigrid for a Reduced-Quadrature Discretization of Poroelasticity

Journal Article · 24 June 2022 · SIAM Journal on Scientific Computing

DOI:https://doi.org/10.1137/21m1429072· OSTI ID:1883184

^[1]; ^[2]; ^[1]; ^[2]; Ohm, Peter ^[3]

Tufts Univ., Medford, MA (United States)
Memorial Univ. of Newfoundland, St. John's, NL (Canada)
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

Advanced finite-element discretizations and preconditioners for models of poroelasticity have attracted significant attention in recent years. The equations of poroelasticity offer significant challenges in both areas, due to the potentially strong coupling between unknowns in the system, saddle-point structure, and the need to account for wide ranges of parameter values, including limiting behavior such as incompressible elasticity. This paper was motivated by an attempt to develop monolithic multigrid preconditioners for the discretization developed in [C. Rodrigo et al., Comput. Methods App. Mech. Engrg, 341 (2018), pp. 467--484]; we show here why this is a difficult task and, as a result, we modify the discretization in [Rodrigo et al.] through the use of a reduced-quadrature approximation, yielding a more “solver-friendly” discretization. Local Fourier analysis is used to optimize parameters in the resulting monolithic multigrid method, allowing a fair comparison between the performance and costs of methods based on Vanka and Braess--Sarazin relaxation. Further, numerical results are presented to validate the local Fourier analysis predictions and demonstrate efficiency of the algorithms. Finally, a comparison to existing block-factorization preconditioners is also given.

View Accepted Manuscript (DOE)

Research Organization:: Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)

Sponsoring Organization:: National Science Foundation (NSF); USDOE National Nuclear Security Administration (NNSA); USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)

Grant/Contract Number:: NA0003525

OSTI ID:: 1883184

Report Number(s):: SAND2022-5421J; 705525

Journal Information:: SIAM Journal on Scientific Computing, Journal Name: SIAM Journal on Scientific Computing Journal Issue: 3 Vol. 45; ISSN 1064-8275

Publisher:: Society for Industrial and Applied Mathematics (SIAM)Copyright Statement

Country of Publication:: United States

Language:: English

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