Research on Reactivity-Equivalent Physical Transformation Method for Double Heterogeneity in Pressurized Water Reactors Based on Machine Learning Article Swipe
Song Li
,
Jiannan Li
,
Lei Liu
,
Bao‐Cheng Huang
,
Ling Chen
,
Yongfa Zhang
,
Jianli Hao
,
Yunfei Zhang
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/pr12112493
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/pr12112493
Traditional computational methods for pressurized water reactors are unable to handle dispersed fuel particles as the double heterogeneity and the direct volumetric homogenization can result in significant errors. In contrast, reactivity-equivalent physical transformation techniques offer high precision for addressing the double heterogeneity introduced by dispersed fuel particles. This approach converts the double heterogeneity problem into a single heterogeneity problem, which is then subsequently investigated by using the conventional pressurized water reactor computational procedure. However, it is currently empirical and takes a lot of time to obtain the right k∞. In this paper, we train the RPT model by using the existing dataset of plate-dispersed fuel and rod-dispersed fuel by a machine learning method based on a linear regression model, and we then use the new data to make predictions and derive the corresponding similarity ratios. The burnup verification, density verification, fission rate verification, and neutron energy spectrum analysis are calculated through the OpenMC program. For plate-type fuel elements, the method maintains an accuracy within 200 pcm during depletion, with deviations in the 235U density and 235U fission rate within 0.1% and neutron energy spectrum errors within 6%. For rod-type fuel elements, the method maintains an accuracy within 100 pcm during depletion, with deviations in 235U and 239Pu density within 1.5% and neutron energy spectrum errors within 1%. The numerical validation indicates that the reactivity-equivalent physical transformation method based on the linear regression model not only greatly improves the computational efficiency, but also ensures a very high accuracy to deal with double heterogeneity in nuclear reactors.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/pr12112493
- OA Status
- gold
- Cited By
- 1
- References
- 20
- Related Works
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- OpenAlex ID
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All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4404243447Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/pr12112493Digital Object Identifier
- Title
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Research on Reactivity-Equivalent Physical Transformation Method for Double Heterogeneity in Pressurized Water Reactors Based on Machine LearningWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-11-09Full publication date if available
- Authors
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Song Li, Jiannan Li, Lei Liu, Bao‐Cheng Huang, Ling Chen, Yongfa Zhang, Jianli Hao, Yunfei ZhangList of authors in order
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https://doi.org/10.3390/pr12112493Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.3390/pr12112493Direct OA link when available
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Reactivity (psychology), Transformation (genetics), Computer science, Biological system, Process engineering, Environmental science, Chemistry, Engineering, Medicine, Biology, Alternative medicine, Biochemistry, Gene, PathologyTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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