DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Yeonha | ko |
dc.contributor.author | Song, Seok Ho | ko |
dc.contributor.author | Bae, Joon Young | ko |
dc.contributor.author | Song, Kyusang | ko |
dc.contributor.author | Seo, Mi Ro | ko |
dc.contributor.author | Kim, Sung Joong | ko |
dc.contributor.author | Lee, Jeong-Ik | ko |
dc.date.accessioned | 2024-08-20T06:00:05Z | - |
dc.date.available | 2024-08-20T06:00:05Z | - |
dc.date.created | 2024-08-02 | - |
dc.date.issued | 2024-12 | - |
dc.identifier.citation | ANNALS OF NUCLEAR ENERGY, v.208 | - |
dc.identifier.issn | 0306-4549 | - |
dc.identifier.uri | http://hdl.handle.net/10203/322354 | - |
dc.description.abstract | This paper introduces methods to develop a surrogate model based on deep learning methods and rollingwindow forecast for fast and accurate prediction of severe accidents in a nuclear power plant. The surrogate model was trained using time series data, which represents thermal-hydraulic behavior in the nuclear power plant under multi-component failures while various mitigation strategies are also implemented. The model uses a rolling-window forecast to predict selected thermal-hydraulic variables for each time step using the previous time-step variables. To improve the accuracy, the model was further refined to consider the hysteresis effect of the variables using the previous three-time steps. The value of the performance metrics measured by the mean absolute error was reduced by 64 percent in the three-step model compared to the single-step model. The proposed surrogate model has the potential as a practical severe accident simulator for accident management support tools. | - |
dc.language | English | - |
dc.publisher | PERGAMON-ELSEVIER SCIENCE LTD | - |
dc.title | Surrogate model for predicting severe accident progression in nuclear power plant using deep learning methods and Rolling-Window forecast | - |
dc.type | Article | - |
dc.identifier.wosid | 001283461300001 | - |
dc.identifier.scopusid | 2-s2.0-85199552391 | - |
dc.type.rims | ART | - |
dc.citation.volume | 208 | - |
dc.citation.publicationname | ANNALS OF NUCLEAR ENERGY | - |
dc.identifier.doi | 10.1016/j.anucene.2024.110816 | - |
dc.contributor.localauthor | Lee, Jeong-Ik | - |
dc.contributor.nonIdAuthor | Bae, Joon Young | - |
dc.contributor.nonIdAuthor | Song, Kyusang | - |
dc.contributor.nonIdAuthor | Seo, Mi Ro | - |
dc.contributor.nonIdAuthor | Kim, Sung Joong | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | Severe Accident | - |
dc.subject.keywordAuthor | Surrogate Model | - |
dc.subject.keywordAuthor | Time Series | - |
dc.subject.keywordAuthor | Dynamic Time Warping | - |
dc.subject.keywordAuthor | Rolling-window forecast | - |
dc.subject.keywordAuthor | Deep Learning Method | - |
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