A factor of 4 dimensionless collisionality scan of H-mode plasmas in MAST shows that the thermal energy confinement time scales as . Local heat transport is dominated by electrons and is consistent with the global scaling. The neutron rate is in good agreement with the ν* dependence of τE,th. The gyrokinetic code GYRO indicates that micro-tearing turbulence might explain such a trend. A factor of 1.4 dimensionless safety factor scan shows that the energy confinement time scales as . These two scalings are consistent with the dependence of energy confinement time on plasma current and magnetic field. Weaker qeng and stronger ν* dependences compared with the IPB98y2 scaling could be favourable for an ST-CTF device, in that it would allow operation at lower plasma current.

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.

Gyrokinetic microstability analyses, with and without electromagnetic effects, are presented for a spherical tokamak plasma equilibrium closely resembling that from a high confinement mode (H mode) discharge in the mega-ampere spherical tokamak (MAST) [A. Sykes et al., Nucl. Fusion 41, 1423 (2001)]. Electrostatic ion temperature gradient driven modes (ITG modes) were found to be unstable on all surfaces, though they are likely to be substantially stabilized by equilibrium E ϫ B flow shear. Electron temperature gradient driven modes (ETG modes) have stronger growth rates that substantially exceed the equilibrium flow shearing rates. Mixing length arguments suggest that ITG modes would give rise to significant transport if they are not stabilized by sheared flows, and predict weak transport from ETG turbulence. Significant plasma flows have been neglected in this first analysis, and are probably important in the delicate balance between ITG growth rates and flow shear, and in the formation of internal transport barriers on MAST. Electromagnetic effects are found to be important even in this low ␤ discharge, especially for longer length-scale modes with k Ќ i Ͻ O͑1͒ on the inner surfaces, where tearing parity modes are found to be the fastest growing modes, with growth rates that are sensitive to the electron collision frequency. These tearing parity microinstabilities are highly extended along the magnetic field, and have been reported in a number of spherical tokamak equilibria.