Generation and analysis of independent fission yield covariances based on GEF model code Article Swipe
Zerun Lu
,
Tiejun Zu
,
Liangzhi Cao
,
Hongchun Wu
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1051/epjconf/202328100015
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1051/epjconf/202328100015
The fission yield data provided by the evaluated nuclear data files do not contain covariance information, which is not conducive to uncertainty analysis. To generate covariance information, the model parameters of the code GEF which describes the fission process are sampled and the independent fission yield samples are calculated. The covariances of independent fission yields of 235 U, 239 Pu, and 241 Pu thermal neutron-induced fissioning systems are generated individually based on the above samples. This method is verified by comparing the uncertainties of burnup-related responses based on fission yield samples calculated by GEF and based on fission yield samples generated with the covariances. The influence of correlations among fissioning systems is also quantified and the joint covariances among different fissioning systems calculated with GEF are demonstrated correct. In addition, the Bayesian Monte Carlo method is adopted to adjust the model parameters of GEF, and the numerical results prove the effectiveness of the adjustment.
Related Topics
Concepts
Fission
Covariance
Nuclear data
Monte Carlo method
Yield (engineering)
Fission product yield
Fission products
Code (set theory)
Process (computing)
Nuclear physics
Neutron
Statistical physics
Computer science
Mathematics
Physics
Statistics
Thermodynamics
Set (abstract data type)
Programming language
Operating system
Metadata
- Type
- article
- Language
- en
- Landing Page
- http://doi.org/10.1051/epjconf/202328100015
- OA Status
- diamond
- Cited By
- 1
- References
- 11
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W4361808028Canonical identifier for this work in OpenAlex
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https://doi.org/10.1051/epjconf/202328100015Digital Object Identifier
- Title
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Generation and analysis of independent fission yield covariances based on GEF model codeWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-01-01Full publication date if available
- Authors
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Zerun Lu, Tiejun Zu, Liangzhi Cao, Hongchun WuList of authors in order
- Landing page
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https://doi.org/10.1051/epjconf/202328100015Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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diamondOpen access status per OpenAlex
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https://doi.org/10.1051/epjconf/202328100015Direct OA link when available
- Concepts
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Fission, Covariance, Nuclear data, Monte Carlo method, Yield (engineering), Fission product yield, Fission products, Code (set theory), Process (computing), Nuclear physics, Neutron, Statistical physics, Computer science, Mathematics, Physics, Statistics, Thermodynamics, Set (abstract data type), Programming language, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
- References (count)
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11Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.Pu, | 59 |
| abstract_inverted_index.The | 0, 49, 104 |
| abstract_inverted_index.and | 41, 60, 94, 114, 144 |
| abstract_inverted_index.are | 39, 47, 67, 125 |
| abstract_inverted_index.not | 12, 18 |
| abstract_inverted_index.the | 6, 27, 31, 36, 42, 72, 81, 102, 115, 130, 139, 145, 149, 152 |
| abstract_inverted_index.GEF, | 143 |
| abstract_inverted_index.This | 75 |
| abstract_inverted_index.also | 112 |
| abstract_inverted_index.code | 32 |
| abstract_inverted_index.data | 3, 9 |
| abstract_inverted_index.with | 101, 123 |
| abstract_inverted_index.Carlo | 133 |
| abstract_inverted_index.Monte | 132 |
| abstract_inverted_index.above | 73 |
| abstract_inverted_index.among | 108, 118 |
| abstract_inverted_index.based | 70, 86, 95 |
| abstract_inverted_index.files | 10 |
| abstract_inverted_index.joint | 116 |
| abstract_inverted_index.model | 28, 140 |
| abstract_inverted_index.prove | 148 |
| abstract_inverted_index.which | 16, 34 |
| abstract_inverted_index.yield | 2, 45, 89, 98 |
| abstract_inverted_index.adjust | 138 |
| abstract_inverted_index.method | 76, 134 |
| abstract_inverted_index.yields | 54 |
| abstract_inverted_index.adopted | 136 |
| abstract_inverted_index.contain | 13 |
| abstract_inverted_index.fission | 1, 37, 44, 53, 88, 97 |
| abstract_inverted_index.nuclear | 8 |
| abstract_inverted_index.process | 38 |
| abstract_inverted_index.results | 147 |
| abstract_inverted_index.sampled | 40 |
| abstract_inverted_index.samples | 46, 90, 99 |
| abstract_inverted_index.systems | 66, 110, 121 |
| abstract_inverted_index.thermal | 63 |
| abstract_inverted_index.Bayesian | 131 |
| abstract_inverted_index.correct. | 127 |
| abstract_inverted_index.generate | 24 |
| abstract_inverted_index.provided | 4 |
| abstract_inverted_index.samples. | 74 |
| abstract_inverted_index.verified | 78 |
| abstract_inverted_index.addition, | 129 |
| abstract_inverted_index.analysis. | 22 |
| abstract_inverted_index.comparing | 80 |
| abstract_inverted_index.conducive | 19 |
| abstract_inverted_index.describes | 35 |
| abstract_inverted_index.different | 119 |
| abstract_inverted_index.evaluated | 7 |
| abstract_inverted_index.generated | 68, 100 |
| abstract_inverted_index.influence | 105 |
| abstract_inverted_index.numerical | 146 |
| abstract_inverted_index.responses | 85 |
| abstract_inverted_index.calculated | 91, 122 |
| abstract_inverted_index.covariance | 14, 25 |
| abstract_inverted_index.fissioning | 65, 109, 120 |
| abstract_inverted_index.parameters | 29, 141 |
| abstract_inverted_index.quantified | 113 |
| abstract_inverted_index.adjustment. | 153 |
| abstract_inverted_index.calculated. | 48 |
| abstract_inverted_index.covariances | 50, 117 |
| abstract_inverted_index.independent | 43, 52 |
| abstract_inverted_index.uncertainty | 21 |
| abstract_inverted_index.correlations | 107 |
| abstract_inverted_index.covariances. | 103 |
| abstract_inverted_index.demonstrated | 126 |
| abstract_inverted_index.individually | 69 |
| abstract_inverted_index.information, | 15, 26 |
| abstract_inverted_index.effectiveness | 150 |
| abstract_inverted_index.uncertainties | 82 |
| abstract_inverted_index.burnup-related | 84 |
| abstract_inverted_index.neutron-induced | 64 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.74081123 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |