No abstract

The soft X ray diagnostic at JET views the plasma from six directions with a total of 215 lines of sight. The good coverage of the plasma makes it possible to make detailed tomographic reconstructions of the soft X ray emission during various conditions. One of the tomography methods applied at JET is discussed: a grid based constrained optimization method that uses anisotropic smoothness on flux surfaces as regularization. This method has made it possible to study in detail the transport of heavy trace impurities injected into the plasma by laser blow-off. Impurity injection experiments in hot ion H mode and optimized shear plasmas are presented and discussed. The addition of a number of features to the algorithm, notably a non-negativity constraint, has made it possible to reconstruct very localized soft X ray emission from the wall during edge localized modes (ELMs). The detectors suffer damage from the neutrons produced in deuterium-deuterium (DD) fusion reactions. This damage influences the sensitivity of the detectors, which makes it necessary to cross-calibrate the cameras. A method based on tomographic reconstructions has been developed to achieve the cross-calibration.

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.