| Project/Area Number |
17K14912
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Multi-year Fund |
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| Research Field |
Nuclear engineering
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| Research Institution | Tokyo University of Science |
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Principal Investigator |
Suzuki Masaaki 東京理科大学, 理工学部経営工学科, 講師 (10431842)
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| Project Period (FY) |
2017-04-01 – 2021-03-31
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| Project Status |
Completed (Fiscal Year 2020)
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| Budget Amount *help |
¥3,510,000 (Direct Cost: ¥2,700,000、Indirect Cost: ¥810,000)
Fiscal Year 2019: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2018: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2017: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
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| Keywords | 原子力 / 過酷事故 / 事故対応 / スケジューリング / 人工知能 / 機械学習 / リスク / レジリエンス / アクシデントマネジメント / リアルタイムマネージメント |
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| Outline of Final Research Achievements |
This research aims to develop an AI system that can derive optimal accident management procedures from information about the state of the plant and available equipment and personnel in the event of a severe accident at a nuclear power plant. We verified the system by assuming an accident scenario and discussed the critical information to formulate the optimal severe accident management policy. In addition, we applied some of the results obtained in this study to the problem of maintenance schedule optimization for nuclear power plants and evaluated their applicability. The application of the results of this research can also be expected to contribute to the realization of more rational and practical maintenance of nuclear power plants.
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| Academic Significance and Societal Importance of the Research Achievements |
不完全・不確実な情報に基づき極高ストレス下で高度かつ迅速な意思決定を行うという人間にとって極めて困難なタスクを人工知能システムが支援することで、より確度の高い認知と適切な判断ができるようになり、放射性物質による人・環境への影響をより低く抑えられる事故対応の実現に貢献できる。また、様々な事故条件に対してコンピュータ上で人工知能による仮想事故対応を実験することで、事故対応に必要な資源の量や配分の合理化に貢献できる。
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