Fast modeling of turbulent transport in fusion plasmas using neural networks

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 $Ti,e$ and n_e 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.