MAST is one of the new generation of large, purpose-built spherical tokamaks (STs) now becoming operational, designed to investigate the properties of the ST in large, collisionless plasmas. The first six months of MAST operations have been remarkably successful. Operationally, both merging-compression and the more usual solenoid induction schemes have been demonstrated, the former providing over 400 kA of plasma current with no demand on solenoid flux. Good vacuum conditions and operational conditions, particularly after boronization in trimethylated boron, have provided plasma current of over 1 MA with central plasma temperatures (ohmic) of order 1 keV. The Hugill and Greenwald limits can be exceeded and H mode achieved at modest additional NBI power. Moreover, particle and energy confinement show an immediate increase at the L-H transition, unlike the case of START, where this became apparent only at the highest plasma currents. Halo currents are small, with low toroidal peaking factors, in accordance with theoretical predictions, and there is evidence of a resilience to the major disruption.

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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 tight aspect ratios (typically A≈1.4) and low magnetic field of spherical tokamak (ST) plasmas, when combined with densities approaching the Greenwald limit, provide a significant challenge for all currently available auxiliary heating and current drive schemes. NBI heating and current drive are difficult to interpret in sub-megampere machines, as in order to achieve suitable penetration into the plasma core, fast ions have to be highly suprathermal and, as a result of the low magnetic field, can be non-adiabatic (i.e. non-conserving of magnetic moment µ0). The physics of NBI heating in START is discussed. The neutral beam injector deployed on START was clearly successful, having been instrumental in producing a world record tokamak toroidal beta of ≈40%. A fast ion Monte Carlo code (LOCUST) is described that was developed to model non-adiabatic fast ion topologies together with a high level of charge exchange loss and cross-field transport (present in START due to an envelope of high density gas surrounding the plasma). Model predictions compare well with experimental data, collected using a scanning neutral particle analyser, bolometric instruments and equilibrium reconstruction using EFIT. In particular, beta calculations based upon reconstruction of the pressure profile (by combining measurements from Thomson scattering, charge exchange recombination spectroscopy and model predictions for the fast ion distribution function) agree well with beta values calculated using EFIT alone (the routine method for calculation of START beta). These results thus provide increased confidence in the ability of STs to sustain high beta high confinement H mode plasmas and in addition indicate that the injected fast ions in collisional START plasmas evolve mainly due to collisional and charge exchange processes, without driving any significant performance degrading fast particle MHD activity.