This paper reports recent progress in the field of γ-ray diagnosis of fast ions in the JET tokamak. The γ-rays, born in nuclear reactions between fast ions and main plasma impurities and/or plasma fuel ions, are analysed with a new modelling tool (the GAMMOD code) that has been developed for a quantitative analysis of the measured γ-ray energy spectra. The analysis of the γ-ray energy spectra identifies the different fast ions giving rise to the γ-ray emission and assesses the effective tail temperatures and relative concentrations of these fast ions. This assessment is possible, since the excitation functions for the different nuclear reactions are well established and exhibit a threshold or/and a resonant nature. The capabilities of the γ-ray spectral analysis are illustrated with the examples from the recent γ-ray diagnostic measurements of 4He, 3He, deuterium and hydrogen ions accelerated by ion-cyclotron resonance frequency heating in JET. Simultaneous measurements of several fast ion species, including highly energetic α-particles, are demonstrated. In addition to the γ-spectroscopy, tomographic reconstructions of the radial profile of the γ-ray emission are performed using the JET neutron profile monitor, thus providing direct measurements of the radial profiles of fast ions in JET.

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Extensive analysis of disruptions in JET has helped advance the understanding of trends of disruption-generated runaway electrons. Tomographic reconstruction of the soft x-ray emission has made possible a detailed observation of the magnetic flux geometry evolution during disruptions. With the aid of soft and hard x-ray diagnostics runaway electrons have been detected at the very beginning of disruptions. A study of runaway electron parameters has shown that an approximate upper bound for the conversion efficiency of pre-disruptive plasma currents into runaways is about 60% over a wide range of plasma currents in JET. Runaway generation has been simulated with a test particle model in order to verify the results of experimental data analysis and to obtain the background for extrapolation of the existing results onto larger devices such as ITER. It was found that close agreement between the modelling results and experimental data could be achieved if in the calculations the post-disruption plasma electron temperature was assumed equal to 10 eV and if the plasma column geometry evolution is taken into account in calculations. The experimental trends and numerical simulations show that runaway electrons are a critical issue for ITER and, therefore, the development of mitigation methods, which suppress runaway generation, is an essential task.

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.