Practical methods for GPU-based whole-core Monte Carlo depletion calculation Article Swipe
Kyung Min Kim
,
Namjae Choi
,
Han Gyu Lee
,
Han Gyu Joo
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1016/j.net.2023.04.021
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1016/j.net.2023.04.021
Several practical methods for accelerating the depletion calculation in a GPU-based Monte Carlo (MC) code PRAGMA are presented including the multilevel spectral collapse method and the vectorized Chebyshev rational approximation method (CRAM). Since the generation of microscopic reaction rates for each nuclide needed for the construction of the depletion matrix of the Bateman equation requires either enormous memory access or tremendous physical memory, both of which are quite burdensome on GPUs, a new method called multilevel spectral collapse is proposed which combines two types of spectra to generate microscopic reaction rates: an ultrafine spectrum for an entire fuel pin and coarser spectra for each depletion region. Errors in reaction rates introduced by this method are mitigated by a hybrid usage of direct online reaction rate tallies for several important fissile nuclides. The linear system to appear in the solution process adopting the CRAM is solved by the Gauss-Seidel method which can be easily vectorized on GPUs. With the accelerated depletion methods, only about 10% of MC calculation time is consumed for depletion, so an accurate full core cycle depletion calculation for a commercial power reactor (BEAVRS) can be done in 16 h with 24 consumer-grade GPUs.
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- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.net.2023.04.021
- OA Status
- diamond
- Cited By
- 2
- References
- 22
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- OpenAlex ID
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https://openalex.org/W4366088244Canonical identifier for this work in OpenAlex
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https://doi.org/10.1016/j.net.2023.04.021Digital Object Identifier
- Title
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Practical methods for GPU-based whole-core Monte Carlo depletion calculationWork title
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
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2023-04-17Full publication date if available
- Authors
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Kyung Min Kim, Namjae Choi, Han Gyu Lee, Han Gyu JooList of authors in order
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https://doi.org/10.1016/j.net.2023.04.021Publisher landing page
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
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https://doi.org/10.1016/j.net.2023.04.021Direct OA link when available
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Nuclide, Monte Carlo method, Computer science, Computational science, Computational physics, Algorithm, Parallel computing, Applied mathematics, Physics, Mathematics, Nuclear physics, StatisticsTop concepts (fields/topics) attached by OpenAlex
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2Total citation count in OpenAlex
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2025: 1, 2024: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| corresponding_author_ids | https://openalex.org/A5028603574 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| corresponding_institution_ids | https://openalex.org/I139264467 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/7 |
| sustainable_development_goals[0].score | 0.6000000238418579 |
| sustainable_development_goals[0].display_name | Affordable and clean energy |
| citation_normalized_percentile.value | 0.8095254 |
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| citation_normalized_percentile.is_in_top_10_percent | False |