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Neural-network-based semi-empirical turbulent particle transport modelling founded on gyrokinetic analyses of JT-60U plasmas

E. Narita, M. Honda, M. Nakata, M. Yoshida, N. Hayashi, H. Takenaga2019年被引用 12Nuclear FusionIF 3出版社

Novel turbulent particle transport modelling has been proposed following joint analyses with gyrokinetic calculations and JT-60U experimental data. Here the diagonal (diffusion) and off-diagonal (pinch) transport mechanisms are treated individually. Besides the decomposition, realistic particle sources from neutral-beam fuelling, which have not been discussed in earlier gyrokinetic studies on particle transport, are taken into account. Taking advantage of the features offered by the modelling, the contribution from each transport mechanism to the turbulent particle flux has been quantitatively clarified. Furthermore, a framework has been developed to calculate the turbulent particle flux driven by each transport mechanism accurately and quickly, taking a neural-network-based approach. The framework can be used for fast prediction of density profiles and for investigating the effects of the transport mechanisms on density profile formation.

日本語訳

ジャイロ運動論計算とJT-60U実験データとの共同解析に基づき、新規の乱流粒子輸送モデリングが提案された。ここでは、対角(拡散)および非対角(ピンチ)輸送機構が個別に扱われる。この分解に加えて、これまでの粒子輸送に関するジャイロ運動論研究では議論されていない、中性粒子ビーム入射による現実的な粒子源が考慮されている。本モデリングが提供する特徴を活かし、各輸送機構の乱流粒子束への寄与が定量的に明らかにされた。さらに、ニューラルネットワークに基づくアプローチを採用し、各輸送機構によって駆動される乱流粒子束を正確かつ高速に計算する枠組みが開発された。本枠組みは、密度分布の高速予測や、密度分布形成に対する輸送機構の影響の調査に利用できる。

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GyrokineticJT-60UParticle transport
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