Progress in thermonuclear fusion energy research based on deuterium plasmas magnetically confi ned in toroidal tokamak devices requires the development of effi cient current drive methods. Previous experiments have shown that plasma current can be driven effectively by externally launched radio frequency power coupled to lower hybrid plasma waves. However, at the high plasma densities required for fusion power plants, the coupled radio frequency power does not penetrate into the plasma core, possibly because of strong wave interactions with the plasma edge. Here we show experiments performed on FTU (Frascati Tokamak Upgrade) based on theoretical predictions that nonlinear interactions diminish when the peripheral plasma electron temperature is high, allowing signifi cant wave penetration at high density. The results show that the coupled radio frequency power can penetrate into high-density plasmas due to weaker plasma edge effects, thus extending the effective range of lower hybrid current drive towards the domain relevant for fusion reactors.

Third-harmonic ion-cyclotron-resonance heating of 4He-beam ions has produced for the first time on the JET tokamak high-energy populations of 4He ions to simulate 3.5 MeV fusion-born alpha (alpha) particles. Acceleration of 4He ions to the MeV energy range is confirmed by gamma-ray emission from the nuclear reaction 9Be(alpha,ngamma) 12C and excitation of Alfvén eigenmodes. Concomitant electron heating and sawtooth stabilization are observed. The scheme could be used in next-step tokamaks to gain information on trapped alpha particles and to test alpha diagnostics in the early nonactivated phase of operation.

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.

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.

The JET 2019-2020 scientific and technological programme exploited the results of years of concerted scientific and engineering work, including the ITER-like wall (ILW: Be wall and W divertor) installed in 2010, improved diagnostic capabilities now fully available, a major Neutral Beam Injection (NBI) upgrade providing record power in 2019-2020, and tested the technical & procedural preparation for safe operation with tritium. Research along three complementary axes yielded a wealth of new results. Firstly, the JET plasma programme delivered scenarios suitable for high fusion power and alpha particle physics in the coming D-T campaign (DTE2), with record sustained neutron rates, as well as plasmas for clarifying the impact of isotope mass on plasma core, edge and plasma-wall interactions, and for ITER pre-fusion power operation. The efficacy of the newly installed Shattered Pellet Injector for mitigating disruption forces and runaway electrons was demonstrated. Secondly, research on the consequences of long-term exposure to JET-ILW plasma was completed, with emphasis on wall damage and fuel retention, and with analyses of wall materials and dust particles that will help validate assumptions and codes for design & operation of ITER and DEMO. Thirdly, the nuclear technology programme aiming to deliver maximum technological return from operations in D, T and D-T benefited from the highest D-D neutron yield in years, securing results for validating radiation transport and activation codes, and nuclear data for ITER.