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.
High-resolution γ -ray measurements were carried out on the Joint European Torus (JET) in an experiment aimed at accelerating 4 He ions in the MeV range by coupling third harmonic radio frequency heating to an injected 4 He beam. For the first time, Doppler broadening of γ -ray peaks from the 12 C(d, pγ ) 13 C and 9 Be(α, nγ ) 12 C reactions was observed and interpreted with dedicated Monte Carlo codes based on the detailed nuclear physics of the processes. Information on the confined 4 He and deuteron energy distribution was inferred, and confined 4 He ions with energies as high as 6 MeV were assessed. A signature of MHD activity in γ -ray traces was also detected. The reported results have a bearing on diagnostics for fast ions in the MeV range in next step fusion devices.
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.
The fast-ion distribution from third harmonic ion cyclotron resonance frequency (ICRF) heating on the Joint European Torus is studied using neutron emission spectroscopy with the time-of-flight spectrometer TOFOR. The energy dependence of the fast deuteron distribution function is inferred from the measured spectrum of neutrons born in DD fusion reactions, and the inferred distribution is compared with theoretical models for ICRF heating. Good agreements between modelling and measurements are seen with clear features in the fast-ion distribution function, that are due to the finite Larmor radius of the resonating ions, replicated. Strong synergetic effects between ICRF and neutral beam injection heating were also seen. The total energy content of the fast-ion population derived from TOFOR data was in good agreement with magnetic measurements for values below 350 kJ.
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