تقرير
Fast modeling of turbulent transport in fusion plasmas using neural networks
| العنوان: | Fast modeling of turbulent transport in fusion plasmas using neural networks |
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| المؤلفون: | van de Plassche, Karel Lucas, Citrin, Jonathan, Bourdelle, Clarisse, Camenen, Yann, Casson, Francis J., Dagnelie, Victor I., Felici, Federico, Ho, Aaron, Van Mulders, Simon, Contributors, JET |
| سنة النشر: | 2019 |
| المجموعة: | Physics (Other) |
| مصطلحات موضوعية: | Physics - Plasma Physics |
| الوصف: | We present an ultrafast neural network (NN) model, QLKNN, which predicts core tokamak transport heat and particle fluxes. QLKNN is a surrogate model based on a database of 300 million flux calculations of the quasilinear gyrokinetic transport model QuaLiKiz. The database covers a wide range of realistic tokamak core parameters. Physical features such as the existence of a critical gradient for the onset of turbulent transport were integrated into the neural network training methodology. We have coupled QLKNN to the tokamak modelling framework JINTRAC and rapid control-oriented tokamak transport solver RAPTOR. The coupled frameworks are demonstrated and validated through application to three JET shots covering a representative spread of H-mode operating space, predicting turbulent transport of energy and particles in the plasma core. JINTRAC-QLKNN and RAPTOR-QLKNN are able to accurately reproduce JINTRAC-QuaLiKiz T i,e and n e profiles, but 3 to 5 orders of magnitude faster. Simulations which take hours are reduced down to only a few tens of seconds. The discrepancy in the final source-driven predicted profiles between QLKNN and QuaLiKiz is on the order 1%-15%. Also the dynamic behaviour was well captured by QLKNN, with differences of only 4%-10% compared to JINTRAC-QuaLiKiz observed at mid-radius, for a study of density buildup following the L-H transition. Deployment of neural network surrogate models in multi-physics integrated tokamak modelling is a promising route towards enabling accurate and fast tokamak scenario optimization, Uncertainty Quantification, and control applications. Comment: 18 pages, 11 figures, Physics of Plasmas, ICDDPS 2019 conference paper |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.1063/1.5134126 |
| URL الوصول: | http://arxiv.org/abs/1911.05617 |
| رقم الأكسشن: | edsarx.1911.05617 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.1063/1.5134126 |
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