SimpleMOC - A performance abstraction for 3D MOC
The method of characteristics (MOC) is a popular method for efficiently solving two-dimensional reactor problems. Extensions to three dimensions have been attempted with mitigated success bringing into question the ability of performing efficient full core three-dimensional (3D) analysis. Although t...
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| Format: | Article |
| Language: | en_US |
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American Nuclear Society (ANS)
2017
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| Online Access: | http://hdl.handle.net/1721.1/110238 https://orcid.org/0000-0002-2413-5052 https://orcid.org/0000-0002-5397-4402 https://orcid.org/0000-0003-1459-7672 https://orcid.org/0000-0003-2497-4312 |
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| author | He, Tim Gunow, Geoffrey Alexander Tramm, John Robert Forget, Benoit Robert Yves Smith, Kord S. |
| author2 | Massachusetts Institute of Technology. Department of Nuclear Science and Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Nuclear Science and Engineering He, Tim Gunow, Geoffrey Alexander Tramm, John Robert Forget, Benoit Robert Yves Smith, Kord S. |
| author_sort | He, Tim |
| collection | MIT |
| description | The method of characteristics (MOC) is a popular method for efficiently solving two-dimensional reactor problems. Extensions to three dimensions have been attempted with mitigated success bringing into question the ability of performing efficient full core three-dimensional (3D) analysis. Although the 3D problem presents many computational difficulties, some simplifications can be made that allow for more efficient computation. In this investigation, we present SimpleMOC, a “mini-app” which
mimics the computational performance of a full 3D MOC solver without involving the full physics perspective, allowing for a more straightforward analysis of the computational challenges. A variety of simplifications are implemented that are intended to increase the computational feasibility, including the formation axially-quadratic neutron sources. With the addition of the quadratic approximation to the neutron source, 3D MOC is cast as a CPU-intensive method with the potential for remarkable scalability on next generation computing architectures. |
| first_indexed | 2024-09-23T07:53:02Z |
| format | Article |
| id | mit-1721.1/110238 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T07:53:02Z |
| publishDate | 2017 |
| publisher | American Nuclear Society (ANS) |
| record_format | dspace |
| spelling | mit-1721.1/1102382022-09-30T00:46:28Z SimpleMOC - A performance abstraction for 3D MOC He, Tim Gunow, Geoffrey Alexander Tramm, John Robert Forget, Benoit Robert Yves Smith, Kord S. Massachusetts Institute of Technology. Department of Nuclear Science and Engineering Gunow, Geoffrey Alexander Tramm, John Robert Forget, Benoit Robert Yves Smith, Kord S. The method of characteristics (MOC) is a popular method for efficiently solving two-dimensional reactor problems. Extensions to three dimensions have been attempted with mitigated success bringing into question the ability of performing efficient full core three-dimensional (3D) analysis. Although the 3D problem presents many computational difficulties, some simplifications can be made that allow for more efficient computation. In this investigation, we present SimpleMOC, a “mini-app” which mimics the computational performance of a full 3D MOC solver without involving the full physics perspective, allowing for a more straightforward analysis of the computational challenges. A variety of simplifications are implemented that are intended to increase the computational feasibility, including the formation axially-quadratic neutron sources. With the addition of the quadratic approximation to the neutron source, 3D MOC is cast as a CPU-intensive method with the potential for remarkable scalability on next generation computing architectures. United States. Dept. of Energy. Office of Nuclear Energy (Nuclear Energy University Programs Fellowship) United States. Dept. of Energy. Center for Exascale Simulation of Advanced Reactor United States. Dept. of Energy. Office of Advanced Scientific Computing Research (Contract DE-AC02-06CH11357) 2017-06-23T20:31:25Z 2017-06-23T20:31:25Z 2015-10 2015-04 Article http://purl.org/eprint/type/ConferencePaper 9781510808041 http://hdl.handle.net/1721.1/110238 Gunow, Geoffrey et al. "SimpleMOC - A PERFORMANCE ABSTRACTION FOR 3D MOC." ANS MC2015 - Joint International Conference on Mathematics and Computation (M&C), Supercomputing in Nuclear Applications (SNA) and the Monte Carlo (MC) Method, 19-23 April, 2015, Nashville, Tennessee, American Nuclear Society, 2015. https://orcid.org/0000-0002-2413-5052 https://orcid.org/0000-0002-5397-4402 https://orcid.org/0000-0003-1459-7672 https://orcid.org/0000-0003-2497-4312 en_US http://www.proceedings.com/27010.html Proceedings of ANS MC2015 - Joint International Conference on Mathematics and Computation (M&C), Supercomputing in Nuclear Applications (SNA) and the Monte Carlo (MC) Method Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf American Nuclear Society (ANS) Prof. Forget via Chris Sherratt |
| spellingShingle | He, Tim Gunow, Geoffrey Alexander Tramm, John Robert Forget, Benoit Robert Yves Smith, Kord S. SimpleMOC - A performance abstraction for 3D MOC |
| title | SimpleMOC - A performance abstraction for 3D MOC |
| title_full | SimpleMOC - A performance abstraction for 3D MOC |
| title_fullStr | SimpleMOC - A performance abstraction for 3D MOC |
| title_full_unstemmed | SimpleMOC - A performance abstraction for 3D MOC |
| title_short | SimpleMOC - A performance abstraction for 3D MOC |
| title_sort | simplemoc a performance abstraction for 3d moc |
| url | http://hdl.handle.net/1721.1/110238 https://orcid.org/0000-0002-2413-5052 https://orcid.org/0000-0002-5397-4402 https://orcid.org/0000-0003-1459-7672 https://orcid.org/0000-0003-2497-4312 |
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