@article{8606,
  author = {K. L. van de Plassche and J. Citrin and C. Bourdelle and Y. Camenen and F. J. Casson and V. I. Dagnelie and F. Felici and A. Ho and S. Van Mulders},
  title = {Fast modelling of turbulent transport in fusion plasmas using neural networks},
  abstract = {We present an ultrafast neural network model, QLKNN, which predicts core tokamak transport heat and particle fluxes. QLKNN is a surrogate model based on a database of 3 × 108 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 modeling 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 the 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 ne profiles, but 3-5 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 of 1%-15%. Also the dynamic behavior 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 modeling is a promising route toward enabling accurate and fast tokamak scenario optimization, uncertainty quantification, and control applications. DOI dataset used for paper: 10.5281/zenodo.3497066
},
  year = {2020},
  journal = {Physics of Plasmas},
  volume = {27},
  pages = {022310},
  url = {https://arxiv.org/abs/1911.05617},
  doi = {10.1063/1.5134126},
  language = {eng},
}