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.

Experimental evidence from the JET tokamak is presented supporting the predictions of a recent theory (Graves et al 2009 Phys. Rev. Lett. 102 065005) on sawtooth instability control by toroidally propagating ion cyclotron resonance waves. Novel experimental conditions minimized a possible alternate effect of magnetic shear modification by ion cyclotron current drive, and enabled the dependence of the new energetic ion mechanism to be tested over key variables. The results have favourable implications on sawtooth control by ion cyclotron resonance waves in a fusion reactor.

Ion cyclotron resonance frequencies (ICRF) mode conversion has been developed for localized on-axis and off-axis bulk electron heating on the JET tokamak. The fast magnetosonic waves launched from the low-field side ICRF antennas are mode-converted to short-wavelength waves on the high-field side of the 3 He ion cyclotron resonance layer in D and 4 He plasmas and subsequently damped on the bulk electrons. The resulting electron power deposition, measured using ICRF power modulation, is narrow with a typical full-width at half-maximum of ≈30 cm (i.e. about 30% of the minor radius) and the total deposited power to electrons comprises at least up to 80% of the applied ICRF power. The ICRF mode conversion power deposition has been kept constant using 3 He bleed throughout the ICRF phase with a typical duration of 4-6 s, i.e. 15-40 energy confinement times. Using waves propagating in the counter-current direction minimizes competing ion damping in the presence of co-injected deuterium beam ions.

A power-balance model, with radiation losses from impurities and neutrals, gives a unified description of the density limit (DL) of the stellarator, the L-mode tokamak, and the reversed field pinch (RFP). The model predicts a Sudo-like scaling for the stellarator, a Greenwald-like scaling, , for the RFP and the ohmic tokamak, a mixed scaling, , for the additionally heated L-mode tokamak. In a previous paper (Zanca et al 2017 Nucl. Fusion 57 056010) the model was compared with ohmic tokamak, RFP and stellarator experiments. Here, we address the issue of the DL dependence on heating power in the L-mode tokamak. Experimental data from high-density disrupted L-mode discharges performed at JET, as well as in other machines, are taken as a term of comparison. The model fits the observed maximum densities better than the pure Greenwald limit.