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 high-k () wavenumber spectrum of density fluctuations has been measured for the first time in MAST (Lloyd et al 2003 Nucl. Fusion 43 1665). This was accomplished with the first implementation of Doppler backscattering (DBS) for core measurements in a spherical tokamak. DBS has become a well-established and versatile diagnostic technique for the measurement of intermediate- k (, and higher) density fluctuations and flows in magnetically confined fusion experiments. Previous implementations of DBS for core measurements have been in standard, large aspect ratio tokamaks. A novel implementation with two-dimensional (2D) steering was necessary to enable DBS measurements in MAST, where the large variation of the magnetic field pitch angle presents a challenge. We report on the scattering considerations and ray tracing calculations used to optimize the design and present data demonstrating measurement capabilities. Initial results confirm the applicability of the design and implementation approaches, showing the strong dependence of scattering alignment on the toroidal launch angle and demonstrating that DBS is sensitive to the local magnetic field pitch angle. We also present comparisons of DBS plasma velocity measurements with charge exchange recombination and beam emission spectroscopy measurements, which show reasonable agreement over most of the minor radius, but imply large poloidal flows approaching the magnetic axis in a discharge with an internal transport barrier. The 2D steering is shown to enable high-k measurements with DBS, at cm () for launch frequencies less than 75 GHz; this capability is used to measure the wavenumber spectrum of turbulence and we find for –11, which is similar to the expectation for the turbulent kinetic cascade of .

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.

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.