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Abstract. At the TEXTOR tokamak an external resonant magnetic perturbation is applied with the Dynamic Ergodic Divertor to control the edge transport properties. The approaches to analyze the impact of such kind of edge stochastisation on transport apply mostly a shell like picture which includes a dependence of transport from magnetic field topology in the radial direction only. In this paper multiple experimental evidence is presented that contrary to these approaches the perturbation applied forms a poloidally heterogenous edge layer in which the transport characteristics are determined by the poloidally alternating field line behavior. A thorough analysis of density and temperature profiles and their gradients for base mode spectra with poloidal/toroidal mode numbers of m/n = 12/4 and m/n = 6/2 is worked out in comparison to the modeled magnetic field topology and results from three dimensional transport modeling with EMC3/EIRENE. Hereby two poloidally adjacent transport domains are identified for the first time in such detail. A domain representing a helical scrape off layer (SOL) is formed by field lines with short connection and therefore prevailing parallel transport to the wall elements. Here, the field lines are clustered into extended flux tubes embedded into a long connection length ergodic domain with diffusive transport characteristics and enhanced radial transport.

The impact of carbon and beryllium/tungsten as plasma-facing components on plasma radiation, divertor power and particle fluxes, and plasma and neutral conditions in the divertors has been assessed in JET both experimentally and by simulations for plasmas in low confinement mode. In high-recycling conditions the studies show a 30% reduction in total radiation in the scrape-off layer when replacing carbon with beryllium in the main chamber and tungsten in the divertor. Correspondingly, at the low field side divertor plate a twofold increase in power conducted to the plate and a twofold increase in electron temperature at the strike point were measured. In low-recycling conditions the SOL was found to be nearly identical for both materials configurations. These observations are in qualitative agreement with predictions from the fluid edge code package EDGE2D/EIRENE. The rollover of the ion currents to both plates was measured to occur at 30% higher upstream densities and radiated power fraction in the Be/W configuration. Past rollover, it was possible to reduce the ion currents to the low field side targets by a factor of 2 and to operate in stable, detached conditions in the JET-ILW configuration; in the JET-C configuration the reduction was limited to 50%. Plasmas with low and high triangularity (and thus magnetic separation to the top of the device), and horizontal and vertical target configurations were investigated and compared to EDGE2D/EIRENE predictions.

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.

Experiments on JET with a carbon-fibre composite wall have explored the reduction of steady-state power load in an ELMy H-mode scenario at high Greenwald fraction ∼0.8, constant power and close to the L to H transition. This paper reports a systematic study of power load reduction due to the effect of fuelling in combination with seeding over a wide range of pedestal density ((4–8) × 1019 m−3) with detailed documentation of divertor, pedestal and main plasma conditions, as well as a comparative study of two extrinsic impurity nitrogen and neon. It also reports the impact of steady-state power load reduction on the overall plasma behaviour, as well as possible control parameters to increase fuel purity. Conditions from attached to fully detached divertor were obtained during this study. These experiments provide reference plasmas for comparison with a future JET Be first wall and an all W divertor where the power load reduction is mandatory for operation.