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.

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.

Based on experimental observations using the TUMAN-3M and FT-2 tokamaks, and the results of gyrokinetic modeling of the interplay between turbulence and the geodesic acoustic mode (GAM) in these installations, a simple model is proposed for the analysis of the conditions required for L-H transition triggering by a burst of radial electric field oscillations in a tokamak. In the framework of this model, one-dimensional density evolution is considered to be governed by an anomalous diffusion coefficient dependent on radial electric field shear. The radial electric field is taken as the sum of the oscillating term and the quasi-stationary one determined by density and ion temperature gradients through a neoclassical formula. If the oscillating field parameters (amplitude, frequency, etc) are properly adjusted, a transport barrier forms at the plasma periphery and sustains after the oscillations are switched off, manifesting a transition into the high confinement mode with a strong inhomogeneous radial electric field and suppressed transport at the plasma edge. The electric field oscillation parameters required for L-H transition triggering are compared with the GAM parameters observed at the TUMAN-3M (in the discharges with ohmic L-H transition) and FT-2 tokamaks (where no clear L-H transition was observed). It is concluded based on this comparison that the GAM may act as a trigger for the L-H transition, provided that certain conditions for GAM oscillation and tokamak discharge are met.

Direct measurements of micro-, meso-, and macroscale transport phenomena in the FT-2 tokamak are shown to be quantitatively reproduced by global full f nonlinear gyrokinetic simulation predictions. A detailed agreement with mean equilibrium E×B flows, oscillating fine-scale zonal flows, and turbulence spectra observed by a set of sophisticated microwave backscattering techniques as well as a good fit of the thermal diffusivity data are demonstrated. A clear influence of the impurity ions on the fluctuating radial electric field is observed.

Alpha particles with energies on the order of megaelectronvolts will be the main source of plasma heating in future magnetic confinement fusion reactors. Instead of heating fuel ions, most of the energy of alpha particles is transferred to electrons in the plasma. Furthermore, alpha particles can also excite Alfvénic instabilities, which were previously considered to be detrimental to the performance of the fusion device. Here we report improved thermal ion confinement in the presence of megaelectronvolts ions and strong fast ion-driven Alfvénic instabilities in recent experiments on the Joint European Torus. Detailed transport analysis of these experiments reveals turbulence suppression through a complex multi-scale mechanism that generates large-scale zonal flows. This holds promise for more economical operation of fusion reactors with dominant alpha particle heating and ultimately cheaper fusion electricity.