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Title: MAESTROeX: A Massively Parallel Low Mach Number Astrophysical Solver

Abstract

@e present MAESTROeX, a massively parallel solver for low Mach number astrophysical flows. The underlying low Mach number equation set allows for efficient, long-time integration for highly subsonic flows compared to compressible approaches. MAESTROeX is suitable for modeling full spherical stars as well as well as planar simulations of dynamics within localized regions of a star, and can robustly handle several orders of magnitude of density and pressure stratification. Previously, we have described the development of the predecessor of MAESTROeX, called MAESTRO, in a series of papers. Here, we present a new, greatly simplified temporal integration scheme that retains the same order of accuracy as our previous approaches. We also explore the use of alternative spatial mapping of the one-dimensional base state onto the full Cartesian grid. The code leverages the new AMReX software framework for block-structured adaptive mesh refinement (AMR) applications, allowing for scalability to large fractions of leadership-class machines. Finally, using our previous studies on the convective phase of single-degenerate progenitor models of SNe Ia as a guide, we characterize the performance of the code and validate the new algorithmic features. Like MAESTRO, MAESTROeX is fully open source.

Authors:
ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [2]; ORCiD logo [2]
  1. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
  2. Stony Brook Univ., NY (United States)
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21). Scientific Discovery through Advanced Computing (SciDAC)
OSTI Identifier:
1601217
Grant/Contract Number:  
[AC02-05CH11231; FG02-87ER40317; SC0017955]
Resource Type:
Accepted Manuscript
Journal Name:
The Astrophysical Journal (Online)
Additional Journal Information:
[Journal Name: The Astrophysical Journal (Online); Journal Volume: 887; Journal Issue: 2]; Journal ID: ISSN 1538-4357
Publisher:
Institute of Physics (IOP)
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; 79 ASTRONOMY AND ASTROPHYSICS

Citation Formats

Fan, Duoming, Nonaka, Andrew, Almgren, Ann S., Harpole, Alice, and Zingale, Michael. MAESTROeX: A Massively Parallel Low Mach Number Astrophysical Solver. United States: N. p., 2019. Web. doi:10.3847/1538-4357/ab4f75.
Fan, Duoming, Nonaka, Andrew, Almgren, Ann S., Harpole, Alice, & Zingale, Michael. MAESTROeX: A Massively Parallel Low Mach Number Astrophysical Solver. United States. doi:10.3847/1538-4357/ab4f75.
Fan, Duoming, Nonaka, Andrew, Almgren, Ann S., Harpole, Alice, and Zingale, Michael. Thu . "MAESTROeX: A Massively Parallel Low Mach Number Astrophysical Solver". United States. doi:10.3847/1538-4357/ab4f75.
@article{osti_1601217,
title = {MAESTROeX: A Massively Parallel Low Mach Number Astrophysical Solver},
author = {Fan, Duoming and Nonaka, Andrew and Almgren, Ann S. and Harpole, Alice and Zingale, Michael},
abstractNote = {@e present MAESTROeX, a massively parallel solver for low Mach number astrophysical flows. The underlying low Mach number equation set allows for efficient, long-time integration for highly subsonic flows compared to compressible approaches. MAESTROeX is suitable for modeling full spherical stars as well as well as planar simulations of dynamics within localized regions of a star, and can robustly handle several orders of magnitude of density and pressure stratification. Previously, we have described the development of the predecessor of MAESTROeX, called MAESTRO, in a series of papers. Here, we present a new, greatly simplified temporal integration scheme that retains the same order of accuracy as our previous approaches. We also explore the use of alternative spatial mapping of the one-dimensional base state onto the full Cartesian grid. The code leverages the new AMReX software framework for block-structured adaptive mesh refinement (AMR) applications, allowing for scalability to large fractions of leadership-class machines. Finally, using our previous studies on the convective phase of single-degenerate progenitor models of SNe Ia as a guide, we characterize the performance of the code and validate the new algorithmic features. Like MAESTRO, MAESTROeX is fully open source.},
doi = {10.3847/1538-4357/ab4f75},
journal = {The Astrophysical Journal (Online)},
number = [2],
volume = [887],
place = {United States},
year = {2019},
month = {12}
}

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