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.

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 ITER baseline scenario, with 500 MW of DT fusion power and Q = 10, will rely on a Type I ELMy H-mode, with ∆W = 0.7 MJ mitigated ELMs. Tungsten (W) is the material now decided for the divertor plasma-facing components from the start of plasma operations. W atoms sputtered from divertor targets during ELMs are expected to be the dominant source under the partially detached divertor conditions required for safe ITER operation. W impurity concentration in the plasma core can dramatically degrade its performance and lead to potentially damaging disruptions. Understanding the physics of plasma-wall interaction during ELMs is important and a primary input for this is the energy of incoming ions during an ELM event. In this paper, coupled Infrared thermography and Langmuir Probe (LP) measurements in JET-ITER-Like-Wall unseeded H-mode experiments with ITER relevant ELM energy drop have been used to estimate the impact energy of deuterium ions (D +) on the divertor target. This analysis gives an ion energy of several keV during ELMs, which makes D + responsible for most of the W sputtering in unseeded H-mode discharges. These LP measurements were possible because of the low electron temperature (T e) during ELMs which allowed saturation of the ion current. Although at first sight surprising, the observation of low T e at the divertor target during ELMs is consistent with the "Free-Streaming" kinetic model which predicts a near-complete transfer of parallel energy from electrons to ions in order to maintain quasi-neutrality of the ELM filaments while they are transported to the divertor targets.