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 and Radiological Science and Technology.
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For accelerating a transport simulation with an advanced physics turbulent transport model like TGLF, we have been developing a surrogate model that mimics the behavior of the model based on a neural network model. With a steady-state transport solver GOTRESS used, the surrogate model has shown its ability to successfully predict temperature profiles almost equivalent to those by TGLF. The performance of the surrogate model is improved by optimizing hyperparameters and eliminating outliers from training data. Extrapolability of the optimized model is examined by changing the normalized temperature gradient. The objective is to better investigate the nature of the model in addition to measuring its utility in transport simulations. The versatile model, which has been trained with data of multiple cases, is developed applicable to many situations. It shows the same reproducibility as the model specific to each individual case, a fact which unveils great potential of the surrogate model in transport simulations.
雑誌名
Plasma and Fusion Research
巻
16
ページ
2403002-1 - 2403002-8
発行年
2021-02
出版者
The Japan Society of Plasma Science and Nuclear Fusion Research
Development of a Surrogate Turbulent Transport Model and Its Usefulness in Transport Simulations
