An intelligent optimization method for the HCSB blanket based on an improved multi-objective NSGA-III algorithm and an adaptive BP neural network
To improve the performance of blanket: maximizing the tritium breeding rate (TBR) for tritium self-sufficiency, and minimizing the Dose of backplate for radiation protection, most previous studies are based on manual corrections to adjust the blanket structure to achieve optimization design, but it...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-09-01
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| Series: | Nuclear Engineering and Technology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1738573323002504 |
| _version_ | 1797737368586813440 |
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| author | Wen Zhou Guomin Sun Shuichiro Miwa Zihui Yang Zhuang Li Di Zhang Jianye Wang |
| author_facet | Wen Zhou Guomin Sun Shuichiro Miwa Zihui Yang Zhuang Li Di Zhang Jianye Wang |
| author_sort | Wen Zhou |
| collection | DOAJ |
| description | To improve the performance of blanket: maximizing the tritium breeding rate (TBR) for tritium self-sufficiency, and minimizing the Dose of backplate for radiation protection, most previous studies are based on manual corrections to adjust the blanket structure to achieve optimization design, but it is difficult to find an optimal structure and tends to be trapped by local optimizations as it involves multi-physics field design, which is also inefficient and time-consuming process. The artificial intelligence (AI) maybe is a potential method for the optimization design of the blanket. So, this paper aims to develop an intelligent optimization method based on an improved multi-objective NSGA-III algorithm and an adaptive BP neural network to solve these problems mentioned above. This method has been applied on optimizing the radial arrangement of a conceptual design of CFETR HCSB blanket. Finally, a series of optimal radial arrangements are obtained under the constraints that the temperature of each component of the blanket does not exceed the limit and the radial length remains unchanged, the efficiency of the blanket optimization design is significantly improved. This study will provide a clue and inspiration for the application of artificial intelligence technology in the optimization design of blanket. |
| first_indexed | 2024-03-12T13:27:41Z |
| format | Article |
| id | doaj.art-a27f7aa9b6da4b488375461095ce00d3 |
| institution | Directory Open Access Journal |
| issn | 1738-5733 |
| language | English |
| last_indexed | 2024-03-12T13:27:41Z |
| publishDate | 2023-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Nuclear Engineering and Technology |
| spelling | doaj.art-a27f7aa9b6da4b488375461095ce00d32023-08-25T04:24:00ZengElsevierNuclear Engineering and Technology1738-57332023-09-0155931503163An intelligent optimization method for the HCSB blanket based on an improved multi-objective NSGA-III algorithm and an adaptive BP neural networkWen Zhou0Guomin Sun1Shuichiro Miwa2Zihui Yang3Zhuang Li4Di Zhang5Jianye Wang6Department of Nuclear Engineering and Management, School of Engineering, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8654, Japan; Key Laboratory of Neutronics and Radiation Safety, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui, 230031, China; University of Science and Technology of China, Hefei, Anhui, 230027, ChinaKey Laboratory of Neutronics and Radiation Safety, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui, 230031, China; Corresponding author.Department of Nuclear Engineering and Management, School of Engineering, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8654, JapanKey Laboratory of Neutronics and Radiation Safety, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui, 230031, ChinaKey Laboratory of Neutronics and Radiation Safety, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui, 230031, China; University of Science and Technology of China, Hefei, Anhui, 230027, ChinaKey Laboratory of Neutronics and Radiation Safety, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui, 230031, China; Institutes of Physical Science and Information Technology, Anhui University, Hefei, 230601, ChinaKey Laboratory of Neutronics and Radiation Safety, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui, 230031, ChinaTo improve the performance of blanket: maximizing the tritium breeding rate (TBR) for tritium self-sufficiency, and minimizing the Dose of backplate for radiation protection, most previous studies are based on manual corrections to adjust the blanket structure to achieve optimization design, but it is difficult to find an optimal structure and tends to be trapped by local optimizations as it involves multi-physics field design, which is also inefficient and time-consuming process. The artificial intelligence (AI) maybe is a potential method for the optimization design of the blanket. So, this paper aims to develop an intelligent optimization method based on an improved multi-objective NSGA-III algorithm and an adaptive BP neural network to solve these problems mentioned above. This method has been applied on optimizing the radial arrangement of a conceptual design of CFETR HCSB blanket. Finally, a series of optimal radial arrangements are obtained under the constraints that the temperature of each component of the blanket does not exceed the limit and the radial length remains unchanged, the efficiency of the blanket optimization design is significantly improved. This study will provide a clue and inspiration for the application of artificial intelligence technology in the optimization design of blanket.http://www.sciencedirect.com/science/article/pii/S1738573323002504CFETR HCSB blanketRadial arrangementOptimization designNSGA-III algorithmDE algorithmBP neural Network |
| spellingShingle | Wen Zhou Guomin Sun Shuichiro Miwa Zihui Yang Zhuang Li Di Zhang Jianye Wang An intelligent optimization method for the HCSB blanket based on an improved multi-objective NSGA-III algorithm and an adaptive BP neural network Nuclear Engineering and Technology CFETR HCSB blanket Radial arrangement Optimization design NSGA-III algorithm DE algorithm BP neural Network |
| title | An intelligent optimization method for the HCSB blanket based on an improved multi-objective NSGA-III algorithm and an adaptive BP neural network |
| title_full | An intelligent optimization method for the HCSB blanket based on an improved multi-objective NSGA-III algorithm and an adaptive BP neural network |
| title_fullStr | An intelligent optimization method for the HCSB blanket based on an improved multi-objective NSGA-III algorithm and an adaptive BP neural network |
| title_full_unstemmed | An intelligent optimization method for the HCSB blanket based on an improved multi-objective NSGA-III algorithm and an adaptive BP neural network |
| title_short | An intelligent optimization method for the HCSB blanket based on an improved multi-objective NSGA-III algorithm and an adaptive BP neural network |
| title_sort | intelligent optimization method for the hcsb blanket based on an improved multi objective nsga iii algorithm and an adaptive bp neural network |
| topic | CFETR HCSB blanket Radial arrangement Optimization design NSGA-III algorithm DE algorithm BP neural Network |
| url | http://www.sciencedirect.com/science/article/pii/S1738573323002504 |
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