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Lessons for adaptive mesh refinement in numerical relativity

Radia, Miren and Sperhake, Ulrich and Drew, Amelia and Clough, Katy and Figueras, Pau and Lim, Eugene A. and Ripley, Justin L. and Aurrekoetxea, Josu C. and França, Tiago and Helfer, Thomas (2022) Lessons for adaptive mesh refinement in numerical relativity. Classical and Quantum Gravity, 39 (13). Art. No. 135006. ISSN 0264-9381. doi:10.1088/1361-6382/ac6fa9. https://resolver.caltech.edu/CaltechAUTHORS:20220606-736224000

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Abstract

We demonstrate the flexibility and utility of the Berger–Rigoutsos adaptive mesh refinement (AMR) algorithm used in the open-source numerical relativity (NR) code GRChombo for generating gravitational waveforms from binary black-hole (BH) inspirals, and for studying other problems involving non-trivial matter configurations. We show that GRChombo can produce high quality binary BH waveforms through a code comparison with the established NR code Lean. We also discuss some of the technical challenges involved in making use of full AMR (as opposed to, e.g. moving box mesh refinement), including the numerical effects caused by using various refinement criteria when regridding. We suggest several ‘rules of thumb’ for when to use different tagging criteria for simulating a variety of physical phenomena. We demonstrate the use of these different criteria through example evolutions of a scalar field theory. Finally, we also review the current status and general capabilities of GRChombo.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1088/1361-6382/ac6fa9DOIArticle
https://arxiv.org/abs/2112.10567arXivDiscussion Paper
ORCID:
AuthorORCID
Radia, Miren0000-0001-8861-2025
Sperhake, Ulrich0000-0002-3134-7088
Drew, Amelia0000-0001-8252-602X
Clough, Katy0000-0001-8841-1522
Figueras, Pau0000-0001-6438-315X
Lim, Eugene A.0000-0002-6227-9540
Ripley, Justin L.0000-0001-7192-0021
Aurrekoetxea, Josu C.0000-0001-9584-5791
França, Tiago0000-0002-1718-151X
Helfer, Thomas0000-0001-6880-1005
Additional Information:© 2022 The Author(s). Published by IOP Publishing Ltd. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Received 19 January 2022, revised 3 May 2022. Accepted for publication 13 May 2022. Published 6 June 2022. We thank the rest of the GRChombo collaboration (www.grchombo.org) for their support and code development work, and for helpful discussions regarding their use of AMR. MR acknowledges support from a Science and Technology Facilities Council (STFC) studentship. KC acknowledges funding from the European Research Council (ERC) under the European Unions Horizon 2020 research and innovation programme (Grant Agreement No. 693024), and an STFC Ernest Rutherford Fellowship. AD is supported by a Junior Research Fellowship (JRF) at Homerton College, University of Cambridge. PF is supported by the European Research Council Grant No. ERC-2014-StG 639022-NewNGR, and by a Royal Society University Research Fellowship Grant Nos. UF140319, RGF∖EA∖180260, URF∖R∖201026 and RF∖ERE∖210291. JCA acknowledges funding from the European Research Council (ERC) under the European Unions Horizon 2020 research and innovation programme (Grant Agreement No. 693024), from the Beecroft Trust and from The Queen's College via an extraordinary Junior Research Fellowship (eJRF). TF is supported by a Royal Society Enhancement Award (Grant No. RGF∖EA∖180260). TH is supported by NSF Grant Nos. PHY-1912550, AST-2006538, PHY-090003 and PHY-20043, and NASA Grant Nos. 17-ATP17-0225, 19-ATP19-0051 and 20-LPS20-0011. JLR and US are supported by STFC Research Grant No. ST/V005669/1; US is supported by the H2020-ERC-2014-CoG Grant 'MaGRaTh' No. 646597. The simulations presented in this paper used DiRAC resources under the projects ACSP218, ACTP186 and ACTP238. The work was performed using the Cambridge Service for Data Driven Discovery (CSD3), part of which is operated by the University of Cambridge Research Computing on behalf of the STFC DiRAC HPC Facility (www.dirac.ac.uk). The DiRAC component of CSD3 was funded by BEIS capital funding via STFC capital Grants ST/P002307/1 and ST/cqgac6fa9bib2452/1 and STFC operations Grant ST/cqgac6fa9bib689X/1. In addition we have used the DiRAC at Durham facility managed by the Institute for Computational Cosmology on behalf of the STFC DiRAC HPC Facility (www.dirac.ac.uk). The equipment was funded by BEIS capital funding via STFC capital Grants ST/P002293/1 and ST/cqgac6fa9bib2371/1, Durham University and STFC operations Grant ST/cqgac6fa9bib0832/1. DiRAC is part of the National e-Infrastructure. XSEDE resources under Grant No. PHY-090003 were used on the San Diego Supercomputing Center's clusters Comet and Expanse and the Texas Advanced Supercomputing Center's (TACC) Stampede2. Furthermore, we acknowledge the use of HPC resources on the TACC Frontera cluster [138]. PRACE resources under Grant No. 2020225359 were used on the GCS Supercomputer JUWELS at the Jülich Supercomputing Center (JCS) through the John von Neumann Institute for Computing (NIC), funded by the Gauss Center for Supercomputing e.V. (www.gauss-centre.eu). The Fawcett supercomputer at the Department of Applied Mathematics and Theoretical Physics (DAMTP), University of Cambridge was also used, funded by STFC Consolidated Grant ST/P000673/1. Data availability statement. The data that support the findings of this study are available upon reasonable request from the authors.
Funders:
Funding AgencyGrant Number
European Research Council (ERC)693024
University of CambridgeUNSPECIFIED
European Research Council (ERC)639022
Royal SocietyUF140319
Science and Technology Facilities Council (STFC)ST/P000673/1
Partnership for Advanced Computing in Europe AISBL2020225359
Royal SocietyRGF∖EA∖180260
Royal SocietyURF∖R∖201026
Royal SocietyRF∖ERE∖210291
Beecroft TrustUNSPECIFIED
University of OxfordUNSPECIFIED
NSFPHY-1912550
NSFAST-2006538
NSFPHY-090003
NSFPHY-20043
NASA17-ATP17-0225
NASA19-ATP19-0051
NASA20-LPS20-0011
Science and Technology Facilities Council (STFC)ST/V005669/1
European Research Council (ERC)646597
Science and Technology Facilities Council (STFC)ST/cqgac6fa9bib2452/1
Science and Technology Facilities Council (STFC)ST/cqgac6fa9bib689X/1
Science and Technology Facilities Council (STFC)ST/P002293/1
Science and Technology Facilities Council (STFC)ST/cqgac6fa9bib2371/1
Durham UniversityUNSPECIFIED
Science and Technology Facilities Council (STFC)ST/cqgac6fa9bib0832/1
Issue or Number:13
DOI:10.1088/1361-6382/ac6fa9
Record Number:CaltechAUTHORS:20220606-736224000
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20220606-736224000
Official Citation:Miren Radia et al 2022 Class. Quantum Grav. 39 135006
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:115040
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:07 Jun 2022 14:32
Last Modified:12 Jul 2022 19:48

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