Bayesian Optimization Analysis of Containment-Venting Operation in a Boiling Water Reactor Severe Accident
Containment venting is one of several essential measures to protect the integrity of the final barrier of a nuclear reactor during severe accidents, by which the uncontrollable release of fission products can be avoided. The authors seek to develop an optimization approach to venting operations, fro...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2017-03-01
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| Series: | Nuclear Engineering and Technology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1738573316303084 |
| _version_ | 1828423028455768064 |
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| author | Xiaoyu Zheng Jun Ishikawa Tomoyuki Sugiyama Yu Maruyama |
| author_facet | Xiaoyu Zheng Jun Ishikawa Tomoyuki Sugiyama Yu Maruyama |
| author_sort | Xiaoyu Zheng |
| collection | DOAJ |
| description | Containment venting is one of several essential measures to protect the integrity of the final barrier of a nuclear reactor during severe accidents, by which the uncontrollable release of fission products can be avoided. The authors seek to develop an optimization approach to venting operations, from a simulation-based perspective, using an integrated severe accident code, THALES2/KICHE. The effectiveness of the containment-venting strategies needs to be verified via numerical simulations based on various settings of the venting conditions. The number of iterations, however, needs to be controlled to avoid cumbersome computational burden of integrated codes. Bayesian optimization is an efficient global optimization approach. By using a Gaussian process regression, a surrogate model of the “black-box” code is constructed. It can be updated simultaneously whenever new simulation results are acquired. With predictions via the surrogate model, upcoming locations of the most probable optimum can be revealed. The sampling procedure is adaptive. Compared with the case of pure random searches, the number of code queries is largely reduced for the optimum finding. One typical severe accident scenario of a boiling water reactor is chosen as an example. The research demonstrates the applicability of the Bayesian optimization approach to the design and establishment of containment-venting strategies during severe accidents. |
| first_indexed | 2024-12-10T15:56:32Z |
| format | Article |
| id | doaj.art-964d516cbbd349c68999ff7d7e0026a1 |
| institution | Directory Open Access Journal |
| issn | 1738-5733 |
| language | English |
| last_indexed | 2024-12-10T15:56:32Z |
| publishDate | 2017-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Nuclear Engineering and Technology |
| spelling | doaj.art-964d516cbbd349c68999ff7d7e0026a12022-12-22T01:42:38ZengElsevierNuclear Engineering and Technology1738-57332017-03-0149243444110.1016/j.net.2016.12.011Bayesian Optimization Analysis of Containment-Venting Operation in a Boiling Water Reactor Severe AccidentXiaoyu ZhengJun IshikawaTomoyuki SugiyamaYu MaruyamaContainment venting is one of several essential measures to protect the integrity of the final barrier of a nuclear reactor during severe accidents, by which the uncontrollable release of fission products can be avoided. The authors seek to develop an optimization approach to venting operations, from a simulation-based perspective, using an integrated severe accident code, THALES2/KICHE. The effectiveness of the containment-venting strategies needs to be verified via numerical simulations based on various settings of the venting conditions. The number of iterations, however, needs to be controlled to avoid cumbersome computational burden of integrated codes. Bayesian optimization is an efficient global optimization approach. By using a Gaussian process regression, a surrogate model of the “black-box” code is constructed. It can be updated simultaneously whenever new simulation results are acquired. With predictions via the surrogate model, upcoming locations of the most probable optimum can be revealed. The sampling procedure is adaptive. Compared with the case of pure random searches, the number of code queries is largely reduced for the optimum finding. One typical severe accident scenario of a boiling water reactor is chosen as an example. The research demonstrates the applicability of the Bayesian optimization approach to the design and establishment of containment-venting strategies during severe accidents.http://www.sciencedirect.com/science/article/pii/S1738573316303084Adaptive SamplingBayesian OptimizationContainment VentingFission ProductsGaussian ProcessTHALES2/KICHE Code |
| spellingShingle | Xiaoyu Zheng Jun Ishikawa Tomoyuki Sugiyama Yu Maruyama Bayesian Optimization Analysis of Containment-Venting Operation in a Boiling Water Reactor Severe Accident Nuclear Engineering and Technology Adaptive Sampling Bayesian Optimization Containment Venting Fission Products Gaussian Process THALES2/KICHE Code |
| title | Bayesian Optimization Analysis of Containment-Venting Operation in a Boiling Water Reactor Severe Accident |
| title_full | Bayesian Optimization Analysis of Containment-Venting Operation in a Boiling Water Reactor Severe Accident |
| title_fullStr | Bayesian Optimization Analysis of Containment-Venting Operation in a Boiling Water Reactor Severe Accident |
| title_full_unstemmed | Bayesian Optimization Analysis of Containment-Venting Operation in a Boiling Water Reactor Severe Accident |
| title_short | Bayesian Optimization Analysis of Containment-Venting Operation in a Boiling Water Reactor Severe Accident |
| title_sort | bayesian optimization analysis of containment venting operation in a boiling water reactor severe accident |
| topic | Adaptive Sampling Bayesian Optimization Containment Venting Fission Products Gaussian Process THALES2/KICHE Code |
| url | http://www.sciencedirect.com/science/article/pii/S1738573316303084 |
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