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Prediction of Radiative Collapse in Large Helical Device Using Feature Extraction by Exhaustive Search

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Abstract

A predictor model of radiative collapse of stellarator-heliotron plasmas has been developed by means of a machine learning technique and the feature of radiative collapse has been extracted with sparse modeling. The dataset used for training the model is constructed based on density ramp-up experiments in the Large Helical Device. As a result of feature extraction, the line averaged electron density, visible line emissions of CIV and OV, and the electron temperature at the edge have been selected as key parameters of radiative collapse. The likelihood of occurrence of radiative collapse has been quantified by using these parameters and this likelihood has been assessed in terms of predicting capability of the occurrence of radiative collapse. The collapse likelihood also implies the underlying physics of radiative collapse, therefore, the knowledge obtained by this data-driven study is expected to facilitate elucidation of the physics of the radiative collapse. In validation with discharges outside of the dataset, the predictor based on the likelihood has predicted over 85% of radiative collapse about 100 ms prior to this event on average while about 5% of stable discharges have been detected falsely as collapse discharges. The discharges in which the predictor made faults are discussed in order to investigate the cause of failure.

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Acknowledgements

This work is supported by the National Institute for Fusion Science Grant administrative budgets NIFS18KLPP051, and JSPS KAKENHI Grant Nos. JP19J20641 and JP19H05498.

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Authors and Affiliations

  1. Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5, Kashiwanoha, Kashiwa, Chiba, 277-8561, Japan

    Tatsuya Yokoyama & Hiroshi Yamada

  2. Research Fellow of Japan Society for the Promotion of Science, Tokyo, Japan

    Tatsuya Yokoyama

  3. National Institute for Fusion Science, National Institutes of Natural Sciences, 322-6 Oroshi-cho, Toki, Gifu, 509-5292, Japan

    Suguru Masuzaki, Junichi Miyazawa, Kiyofumi Mukai, Byron J. Peterson, Naoki Tamura, Ryuichi Sakamoto, Gen Motojima, Katsumi Ida, Motoshi Goto & Tetsutaro Oishi

  4. Advanced Fusion Research Center, Research Institute for Applied Mechanics, Kyushu University, 6-1 Kasugakoen, Kasuga, Fukuoka, 816-8580, Japan

    Suguru Masuzaki

  5. The Graduate University for Advanced Studies, SOKENDAI, 322-6 Oroshi-cho, Toki, Gifu, 509-5292, Japan

    Suguru Masuzaki, Junichi Miyazawa, Kiyofumi Mukai, Byron J. Peterson, Naoki Tamura, Ryuichi Sakamoto, Gen Motojima, Motoshi Goto & Tetsutaro Oishi

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  1. Tatsuya Yokoyama
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Yokoyama, T., Yamada, H., Masuzaki, S. et al. Prediction of Radiative Collapse in Large Helical Device Using Feature Extraction by Exhaustive Search. J Fusion Energ 39, 500–511 (2020). https://doi.org/10.1007/s10894-020-00272-3

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