The EUROfusion JET-ILW pedestal database is described, with emphasis on three main issues. First, the technical aspects are introduced, including a description of the data selection, the datasets, the diagnostics used, the experimental and theoretical methods implemented and the main definitions. Second, the JET-ILW pedestal structure and stability are described. In particular, the work describes the links between the engineering parameters (power, gas and divertor configuration) and the disagreement with the peeling-ballooning (PB) model implemented with ideal MHD equations. Specifically, the work clarifies why the JET-ILW pedestal tends to be far from the PB boundary at high gas and high power, showing that a universal threshold in power and gas cannot be found but that the relative shift (the distance between the position of the pedestal density and of the pedestal temperature) plays a key role. These links are then used to achieve an empirical explanation of the behavior of the JET-ILW pedestal pressure with gas, power and divertor configuration. Third, the pedestal database is used to revise the scaling law of the pedestal stored energy. The work shows a reasonable agreement with the earlier Cordey scaling in terms of plasma current and triangularity dependence, but highlights some differences in terms of power and isotope mass dependence.

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.

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.

The self-consistent core-pedestal prediction model of a combination of EPED1 type pedestal prediction and a simple stiff core transport model is able to predict type I ELMy pedestals of a large JET-ILW (ITER-like wall) database at the similar accuracy as is obtained when the experimental global plasma  is used as input. The neutral penetration model [1] with corrections that take into account variations due to gas fuelling and plasma triangularity, is able to predict the pedestal density with an average error of 15%. The prediction of the pedestal pressure in hydrogen plasma that has higher core heat diffusivity compared to a deuterium plasma with similar heating and fuelling agrees with the experiment when the isotope effect on the stability, the increased diffusivity and outward radial shift of the pedestal are included in the prediction. However, the neutral penetration model that successfully predicts the deuterium pedestal densities fails to predict the isotope effect on the pedestal density in hydrogen plasmas.