Power exhaust is one of the major challenges for the development of a fusion power plant. Predictions based upon a multi machine database give a scrapeoff layer power fall-off length λ q ≤ 1 mm for large fusion devices such as ITER. The power deposition profile on the target is broadened in the divertor by heat transport perpendicular to the magnetic field lines. This profile broadening is described by the power spreading S. Hence both λ q and S need to be understood in order to estimate the expected divertor heat load for future fusion devices. For the investigation of S and λ q L-Mode discharges with stable divertor conditions in hydrogen and deuterium were conducted in ASDEX Upgrade. A strong dependence of S on the divertor electron temperature and density is found which is the result of the competition between parallel electron heat conductivity and perpendicular diffusion in the divertor region. For high divertor temperatures it is found that the ion gyro radius at the divertor target needs to be considered. The dependence of the in/out asymmetry of the divertor power load on the electron density is investigated. The influence of the main ion species on the asymmetric behaviour is shown for hydrogen, deuterium and helium. A possible explanation for the observed asymmetry behaviour based on vertical drifts is proposed.
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.
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