Title: Performance of explicit and IMEX MRI multirate methods on complex reactive flow problems within modern parallel adaptive structured grid frameworks

Abstract

Large-scale multiphysics simulations are computationally challenging due to the coupling of multiple processes with widely disparate time scales. The advent of exascale computing systems exacerbates these challenges since these systems enable ever-increasing size and complexity. In recent years, there has been renewed interest in developing multirate methods as a means to handle the large range of time scales, as these methods may afford greater accuracy and efficiency than more traditional approaches of using implicit-explicit (IMEX) and low-order operator splitting schemes. However, to date there have been few performance studies that compare different classes of multirate integrators on complex application problems. In this work, we study the performance of several newly developed multirate infinitesimal (MRI) methods, implemented in the SUNDIALS solver package, on two reacting flow model problems built on structured mesh frameworks. The first model revisits prior work on a compressible reacting flow problem with complex chemistry that is implemented using BoxLib but where we now include comparisons between a new explicit MRI scheme with the multirate spectral deferred correction (SDC) methods in the original paper. The second problem uses the same complex chemistry as the first problem, combined with a simplified flow model, but runs at a large spatialmore » scale where explicit methods become infeasible due to stability constraints. Two recently developed IMEX MRI multirate methods are tested. These methods rely on advanced features of the AMReX framework on which the model is built, such as multilevel grids and multilevel preconditioners. The results from these two problems show that MRI multirate methods can offer significant performance benefits on complex multiphysics application problems and that these methods may be combined with advanced spatial discretization to compound the advantages of both.« less

Authors:

Loffeld, John J.  ^[1];

• Search DOE PAGES for author "Loffeld, John J."
• Search DOE PAGES for ORCID "0000-0001-7720-7155"

Nonaka, Andy  ^[2];

• Search DOE PAGES for author "Nonaka, Andy"
• Search DOE PAGES for ORCID "0000-0003-1791-0265"

Reynolds, Daniel R.  ^[3];

• Search DOE PAGES for author "Reynolds, Daniel R."
• Search DOE PAGES for ORCID "0000-0002-0911-7841"

Gardner, David J.  ^[1];

• Search DOE PAGES for author "Gardner, David J."
• Search DOE PAGES for ORCID "0000-0002-7993-8282"

Woodward, Carol S.  ^[1]

• Search DOE PAGES for author "Woodward, Carol S."
• Search DOE PAGES for ORCID "0000-0002-6502-8659"

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+ Show Author Affiliations

1. Center for Applied Scientific Computing, Lawrence Livermore National Laboratory, Livermore, CA, USA
2. Center for Computational Sciences and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
3. Department of Mathematics, Southern Methodist University, Dallas, TX, USA

Publication Date:

Sun Feb 25 00:00:00 EST 2024

Sponsoring Org.:

USDOE

OSTI Identifier:

2311932

Grant/Contract Number:  

Scientific Discovery through Advanced Computing (SciDAC)

Resource Type:

Published Article

Journal Name:

International Journal of High Performance Computing Applications

Additional Journal Information:

Journal Name: International Journal of High Performance Computing Applications; Journal ID: ISSN 1094-3420

Publisher:

SAGE Publications

Country of Publication:

United States

Language:

English