Composite matrix construction for structured grid adaptive mesh refinement

• Main Authors: Adams, MF, Cornford, SL, Martin, DF, McCorquodale, P
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: eScholarship, University of California 2019
• Subjects: Algebraic multigrid; Preconditioning; PETSc; Adaptive mesh refinement; Mathematical Sciences; Physical Sciences; Information and Computing Sciences; Nuclear & Particles Physics; Ice Sheet
• Online Access: https://escholarship.org/uc/item/88x5c6bp

Structured-grid adaptive mesh refinement (SAMR) is an approach to mesh generation that supports structured access to data and adaptive mesh refinement for discretized partial differential equations (PDEs). Solution algorithms often require that an inverse of an operator be applied, a system of algebraic equations must be solved, and this process is often the primary computational cost in an application. SAMR is well suited to geometric multigrid solvers, which can be effective, but often do not adapt well to complex geometry including material coefficients. Algebraic multigrid (AMG) is more robust in the face of complex geometry, in both boundary conditions and internal material interfaces. AMG requires a stored matrix linearization of the operator. We discuss an approach, and an implementation in the Chombo block-structured AMR framework, for constructing composite grid matrices from a SAMR hierarchy of grids for use in linear solvers in the PETSc numerical library. We consider a case study with the Chombo-based BISICLES ice sheet modeling application.