| アイテムタイプ |
学術雑誌論文 / Journal Article(1) |
| 公開日 |
2025-09-19 |
| タイトル |
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タイトル |
Online un-supervised tearing mode detection with sequentially discounting algorithms for JT-60SA |
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言語 |
en |
| 言語 |
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言語 |
eng |
| 資源タイプ |
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資源タイプ識別子 |
http://purl.org/coar/resource_type/c_6501 |
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資源タイプ |
journal article |
| 著者 |
Yokoyama Tatsuya
Inoue Shizuo
Kojima Shinichiro
Wakatsuki Takuma
Yoshida Maiko
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| 抄録 |
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内容記述タイプ |
Abstract |
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内容記述 |
A non-empirical tearing mode detector model based on statistical anomaly detection with sequentially discounting algorithms has been developed, and its prediction performance has been demonstrated with experiment data in the JT-60SA initial operation phase. Detection and prediction of the occurrence of disruptions is an important issue to protect the tokamak device from the damage caused by disruptions, and empirical models using machine learning techniques are being developed. A non-empirical model based on anomaly detection is one possible solution to an inherent problem of uncertainty as to whether such empirical models can be extrapolated to future tokamaks. The developed detector model has shown a good prediction performance, and its processing time was smaller than the control cycle time of the JT-60SA without any advanced devices such as GPUs. The result suggests that the statistical anomaly detection with sequentially discounting algorithms is a good choice to be implemented in the plasma controller not only in the JT-60SA but also in the future devices such as ITER and DEMO. |
| 書誌情報 |
Fusion Engineering and Design
巻 222,
発行日 2025-09
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| 出版者 |
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出版者 |
ELSEVIER |
| DOI |
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識別子タイプ |
DOI |
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関連識別子 |
10.1016/j.fusengdes.2025.115440 |
