The role of the pedestal position on the pedestal performance has been investigated in AUG, JET-ILW and TCV. When the pedestal is peeling-ballooning (PB) limited, the three machines show a similar behaviour. The outward shift of the pedestal density leads to the outward shift of the pedestal pressure which, in turns, reduces the PB stability, degrades the pedestal confinement and reduces the pedestal width. Once the experimental density position is considered, the EPED model is able to correctly predict the pedestal height. An estimate of the impact of the density position on a ITER baseline scenario shows that the maximum reduction in the pedestal height is 10% while the reduction in the fusion power is between 10% and 40% depending on the assumptions for the core transport model used.When the pedestal is not PB limited, a different behaviour is observed. The outward shift of the density is still empirically correlated with the pedestal degradation but no change in the pressure position is observed and the PB model is not able to correctly predict the pedestal height. On the other hand, the outward shift of the density leads to a significant increase of η e (where η e is the ratio of density to temperature scale lengths, η e = L ne /L Te ) which leads to the increase of the growth rate of microinstabilities (mainly ETG and ITG) by 50%. This suggests that, when the pedestal is not PB limited, the increase in the turbulent transport due to the outward shift of the density might play an important role in the decrease of the pedestal performance.

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.

Effect of the relative shift between the electron density and temperature pedestal position on the pedestal stability in JET-ILW and comparison with JET-C stability, when the relative shift is reduced. This has been ascribed mainly to the increase of the edge bootstrap current, and to minor effects related to the increase of the pedestal pressure gradient and narrowing of the pedestal pressure width. Pedestal predictive model EUROPED shows a qualitative agreement with experiment, especially for low values of the relative shift.

The JET exploitation plan foresees D-T operations in 2020 (DTE2). With respect to the first D-T campaign in 1997 (DTE1), when JET was equipped with a carbon wall, the experiments will be conducted in presence of a berylliumtungsten ITER-like wall (ILW) and will benefit from an extended and improved set of diagnostics and higher additional heating power (32 MW NBI + 8 MW ICRH). Among the challenges presented by operations with the new wall, there are a general deterioration of the pedestal confinement, the risk of heavy impurity accumulation in the core, which, if not controlled, can cause the radiative collapse of the discharge, and the requirement to protect the divertor from excessive heat loads, which may damage it permanently. Therefore, an intense activity of scenario development has been undertaken at JET during the last three years to overcome these difficulties and prepare the plasmas needed to demonstrate stationary high fusion performance and

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.