No abstract

Disruptions are a major operational concern for next generation tokamaks, including ITER. They may generate excessive heat loads on plasma facing components, large electromagnetic forces in the machine structures and several MA of multi-MeV runaway electrons. A more complete understanding of the runaway generation processes and methods to suppress them is necessary to ensure safe and reliable operation of future tokamaks. Runaway electrons were studied at JET-ILW showing that their generation dependencies (accelerating electric field, avalanche critical field, toroidal field, MHD fluctuations) are in agreement with current theories.

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.

We describe a new technique for efficient generation of high-energy ions with electromagnetic ion cyclotron waves in multi-ion plasmas. The discussed 'three-ion' scenarios are especially suited for strong wave absorption by a very low number of resonant ions. To observe this effect, the plasma composition has to be properly adjusted, as prescribed by theory. We demonstrate the potential of this technique on the world-largest plasma magnetic confinement device JET (Joint European Torus, Culham, UK) and the high magnetic field tokamak Alcator C-Mod (MIT, USA). The obtained results demonstrate efficient acceleration of 3 He ions to high energies in dedicated hydrogen-deuterium mixtures. Simultaneously, effective plasma heating is observed, as a result of the slowing-down of the fast 3 He ions. The developed technique is not only limited to laboratory plasmas, but can also be applied to explain observations of energetic ions in space plasma environments, in particular, 3 He-rich solar flares.Accepted version of the paper published in Nature Physics (2017) http://dx

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.