Predicting BWR Criticality with Data-Driven Machine Learning Model
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866910695222673408 |
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| author | Oktavian, Muhammad Rizki Tunga, Anirudh Nistor, Jonathan Tusar, James Gruenwald, J. Thomas Xu, Yunlin |
| author_facet | Oktavian, Muhammad Rizki Tunga, Anirudh Nistor, Jonathan Tusar, James Gruenwald, J. Thomas Xu, Yunlin |
| contents | One of the challenges in operating nuclear power plants is to decide the amount of fuel needed in a cycle. Large-scale nuclear power plants are designed to operate at base load, meaning that they are expected to always operate at full power. Economically, a nuclear power plant should burn enough fuel to maintain criticality until the end of a cycle (EOC). If the reactor goes subcritical before the end of a cycle, it may result in early coastdown as the fuel in the core is already depleted. On contrary, if the reactor still has significant excess reactivity by the end of a cycle, the remaining fuels will remain unused. In both cases, the plant may lose a significant amount of money. This work proposes an innovative method based on a data-driven deep learning model to estimate the excess criticality of a boiling water reactor. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_07425 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Predicting BWR Criticality with Data-Driven Machine Learning Model Oktavian, Muhammad Rizki Tunga, Anirudh Nistor, Jonathan Tusar, James Gruenwald, J. Thomas Xu, Yunlin Machine Learning Artificial Intelligence One of the challenges in operating nuclear power plants is to decide the amount of fuel needed in a cycle. Large-scale nuclear power plants are designed to operate at base load, meaning that they are expected to always operate at full power. Economically, a nuclear power plant should burn enough fuel to maintain criticality until the end of a cycle (EOC). If the reactor goes subcritical before the end of a cycle, it may result in early coastdown as the fuel in the core is already depleted. On contrary, if the reactor still has significant excess reactivity by the end of a cycle, the remaining fuels will remain unused. In both cases, the plant may lose a significant amount of money. This work proposes an innovative method based on a data-driven deep learning model to estimate the excess criticality of a boiling water reactor. |
| title | Predicting BWR Criticality with Data-Driven Machine Learning Model |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2411.07425 |