量研学術機関リポジトリ「QST-Repository」は、国立研究開発法人 量子科学技術研究開発機構に所属する職員等が生み出した学術成果（学会誌発表論文、学会発表、研究開発報告書、特許等）を集積しインターネット上で広く公開するサービスです。 Welcome to QST-Repository where we accumulates and discloses the academic research results(Journal Publications, Conference presentation, Research and Development Report, Patent, etc.) of the members of National Institutes for Quantum Science and Technology.
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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.
Development of a semi-empirical particle and heat transport model and improvement in its turbulent saturation rule
