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Development of a semi-empirical particle and heat transport model and improvement in its turbulent saturation rule
https://repo.qst.go.jp/records/85504
https://repo.qst.go.jp/records/8550460abfa73-0262-4111-8e9a-4d177bdccaa4
| Item type | 会議発表論文 / Conference Paper(1) | |||||
|---|---|---|---|---|---|---|
| 公開日 | 2022-03-21 | |||||
| タイトル | ||||||
| タイトル | Development of a semi-empirical particle and heat transport model and improvement in its turbulent saturation rule | |||||
| 言語 | ||||||
| 言語 | eng | |||||
| 資源タイプ | ||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_5794 | |||||
| 資源タイプ | conference paper | |||||
| アクセス権 | ||||||
| アクセス権 | metadata only access | |||||
| アクセス権URI | http://purl.org/coar/access_right/c_14cb | |||||
| 著者 |
Emi, Narita
× Emi, Narita× Mitsuru, Honda× Motoki, Nakata× Maiko, Yoshida× Nobuhiko, Hayashi× Emi, Narita× Maiko, Yoshida× Nobuhiko, Hayashi |
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| 抄録 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | The gyrokinetic-based turbulent transport models are essential to predict density and temperature profiles, but introducing detailed descriptions of turbulence physics tends to increase the computational cost. To accelerate the profile predictions, a neural-network (NN) based approach has been undertaken. Our study is also developing a NN-based turbulent transport model DeKANIS. A turbulent saturation rule employed in DeKANIS was based on experimental particle fluxes estimated for JT-60U H-mode plasmas, and it was apt to overestimate temperatures. To reduce the overestimation, a different saturation rule is built including experimental heat fluxes. | |||||
| 書誌情報 |
第19回核燃焼プラズマ統合コード研究会 発行日 2022-02 |
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