Experiments have been performed on MAST using both external (n=1,2) and internal (n=3) resonant magnetic perturbation coils. ELM suppression has not been achieved even though vacuum modelling shows that either set of coils can produce a region for which the Chirikov parameter is greater than 1 wider than that required to produce ELM suppression in DIII-D. Hence having a certain width of the edge stochastic region may be a necessary criterion but is not a sufficient condition to ensure ELM suppression on any device. Although complete ELM suppression has not been achieved some effects on the ELMs have been observed including triggering ELMs in ELM free H-mode periods and increasing the ELM frequency in regularly ELMing discharges. In addition, large changes to the edge turbulence have been observed in L-mode discharges.

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 dependences of energy confinement on plasma current and toroidal magnetic field have been investigated in the MAST spherical tokamak in H-mode plasmas. Multivariate fits show that the dependence of energy confinement time on plasma current Ip is weaker than linear while the dependence on toroidal magnetic field BT is stronger than linear, in contrast to conventional energy confinement scalings. These Ip and BT dependences have also been confirmed by single parameter scans. Transport analysis indicates that the strong BT scaling of energy confinement could possibly be explained by weaker q and stronger ν* dependence of heat diffusivity in comparison with conventional tokamaks.

Edge localized mode (ELM) characteristics in a large spherical tokamak (ST) with significant auxiliary heating are explored. High confinement is achieved in mega ampere spherical tokamak (MAST) at low ELM frequencies even though the ELMs exhibit many type III characteristics. These ELMs are associated with a reduction in the pedestal density but no significant change in the pedestal temperature or temperature profile, indicating that energy is convected from the pedestal region into the scrape-off layer. Power to the targets during an ELM arrives predominantly at the low field outboard side. ELM effluxes are observed up to 20 cm from the plasma edge at the outboard mid-plane and are associated with the radial motion of a feature at an average velocity of 0.75 km s −1. The target balance observed in MAST is potentially rather favourable for the ST since H-mode access is facilitated in a regime where ELM losses flow mostly to the large wetted area, outboard targets and, in addition, the target heat loads are reduced by an even distribution of power between the upper and lower targets.