| アイテムタイプ |
学術雑誌論文 / Journal Article(1) |
| 公開日 |
2024-05-02 |
| タイトル |
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タイトル |
Modification of a machine learning-based semi-empirical turbulent transport model for its versatility |
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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 |
Narita Emi
Honda Mitsuru
Nakata Motoki
Hayashi Nobuhiko
nakayama Tomonari
yoshida Maiko
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| 抄録 |
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内容記述タイプ |
Abstract |
|
内容記述 |
A machine learning-based semi-empirical turbulent transport model DeKANIS has been modified to apply it independently of the device. DeKANIS predicts particle and heat fluxes, distinguishing diffusive and non-diffusive transport processes. DeKANIS consists of a neural network (NN) model, which computes coefficients of the non-diffusive terms and the ratio of the fluxes based on the gyrokinetic calculations, and a scaling formula, which estimates the turbulent saturation level founded on empirical fluxes. The datasets used for NN training have been prepared based on JT-60U plasmas so far, but by exploiting JET plasmas, the datasets have been expanded and the parameter ranges covered by the NN models have become wider. The scaling formula has been rebuilt considering the decrease in the residual zonal flow level due to collisions. The new DeKANIS has demonstrated a reasonable profile prediction of an ITER plasma in the pre-fusion power operation 1 phase with an integrated model GOTRESS+. In validating the prediction results with the gyrokinetic calculations, transport processes causing the fluxes have been exhibited. |
| 書誌情報 |
Contributions to Plasma Physics
巻 63,
号 5-6,
p. e202200152,
発行日 2023-01
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| 出版者 |
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出版者 |
Wiley |
| ISSN |
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収録物識別子タイプ |
ISSN |
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収録物識別子 |
0863-1042 |
| DOI |
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識別子タイプ |
DOI |
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関連識別子 |
10.1002/ctpp.202200152 |
