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.

This paper deals with a study of H − /D − negative ion surface production on diamond in low pressure H 2 /D 2 plasmas. A sample placed in the plasma is negatively biased with respect to plasma potential. Upon positive ion impacts on the sample, some negative ions are formed and detected according to their mass and energy by a mass spectrometer placed in front of the sample. The experimental methods developed to study negative ion surface production and obtain negative ion energy and angle distribution functions are first presented. Different diamond materials ranging from nanocrystalline to single crystal layers, either doped with boron or intrinsic, are then investigated and compared with graphite. The negative ion yields obtained are presented as a function of different experimental parameters such as the exposure time, the sample bias which determines the positive ion impact energy and the sample surface temperature. It is concluded from these experiments that the electronic properties of diamond materials, among them the negative electron affinity, seem to be favourable for negative-ion surface production. However, the negative ion yield decreases with the plasma induced defect density. fusion power-plant prototype producing electrical energy, targeting ∼1 GW of electrical power coupled to the grid [23,24]. In the ITER and DEMO devices, the heating of the plasma will mainly be produced by neutral beam injection (NBI). NBIs systems are key components in achieving high fusion energetic-performances. The ITER NBIs are required to inject 1 MeV beams of neutral deuterium atoms (D) into the tokamak, providing plasma heating and current drive. At such high velocities, much larger than classical electron orbit velocities of hydrogen atoms, the probability of electron capture from D + ions is too low, so that production of D relies on electron detachment from high-intensity D − beams. D − negative-ions are produced in a low-pressure plasma source and subsequently extracted and accelerated.The ITER negative ion source, currently under development at IPP Garching [7,25] in Germany, operates with a high-density, low-pressure inductively coupled plasma. Extracted D − current density of 200 A m −2 , over a large surface of 1.2 m 2 , with 5%-10% uniformity and low co-extracted electron-current (below one electron per negative ion), during long operation period (3600 s) is targeted. To reach such a high D − negative-ion current, the only up-to-date scientific solution is the use of caesium. Deuterium negative-ions are created at the extraction region by backscattering of positive ions or neutrals on the plasma grid. Deposition of caesium on the grid lowers the material work function and allows for high electron-capture efficiency by incident particles and thus, high negative ion yields. Studies conducted at IPP Garching show that the ITER negative-ion source can reach the required high current densities. However, drawbacks to the use of caesium have been identified. First, the caesium is continuously injected in the source a...

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.