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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Suprathermal fuel ions from alpha-particle knock-on collisions in fusion DT plasmas are predicted to cause a weak feature in the neutron spectrum of d+t-->alpha+n. The knock-on feature has been searched for in the neutron emission of high ( >1 MW) fusion-power plasmas produced at JET and was found using a magnetic proton recoil type neutron spectrometer of high performance. Measurement and predictions agree both in absolute amplitude and in plasma-parameter dependence, supporting the interpretation and model. Moreover, the results provide input to projecting alpha-particle diagnostics for future self-heated fusion plasmas.

The spectral broadening of characteristic γ-ray emission peaks from the reaction (12)C((3)He,pγ)(14)N was measured in D((3)He) plasmas of the JET tokamak with ion cyclotron resonance heating tuned to the fundamental harmonic of (3)He. Intensities and detailed spectral shapes of γ-ray emission peaks were successfully reproduced using a physics model combining the kinetics of the reacting ions with a detailed description of the nuclear reaction differential cross sections for populating the L1-L8 (14)N excitation levels yielding the observed γ-ray emission. The results provide a paradigm, which leverages knowledge from areas of physics outside traditional plasma physics, for the development of nuclear radiation based methods for understanding and controlling fusion burning plasmas.

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.