Wendelstein 7-X aims at quasi-steady state operation with up to 10 MW of heating power for 30 min. Power exhaust will be handled predominantly via 10 actively water cooled CFC (carbon-fiber-reinforced carbon) based divertor units designed to withstand power loads of 10 MW/m² locally in steady state. If local loads exceed this value, a risk of local delamination of the CFC and failure of entire divertor modules arises. Infrared endoscopes to monitor all main plasma facing components are being prepared, and near real time software tools are under development to identify areas of excessive temperature rise, to distinguish them from non-critical events, and to trigger alarms. Tests with different cameras were made in the recent campaign. Long pulse operation enforces additional diagnostic design constraints: for example, the optics need to be thermally decoupled from the endoscope housing. In the upcoming experimental campaign, a graphite scraper element, in front of the island divertor throat, will be tested as a possible means to protect the divertor pumping gap edges during the transient discharge evolution.

Classical particle drifts are known to have substantial impacts on fluxes of particles and heat through the edge plasmas in both tokamaks and stellarators. Here we present results from the first dedicated investigation of drift effects in the W7-X stellarator. By comparing similar plasma discharges conducted with a forward- and reverse-directed magnetic field, the impacts of drifts could be isolated through the observation of up-down asymmetries in flux profiles on the divertor targets. In low-density plasmas, the radial locations of the strike lines (i.e. peaks in the target heat flux profiles) exhibited discrepancies of up to 3 cm that reversed upon magnetic field reversal. In addition, asymmetric heat loads were observed in regions of the target that are shadowed by other targets from parallel flux from the core plasma. A comparison of these asymmetric features with the footprints of key topological regions of the edge magnetic field on the divertor suggests that the main driver of the asymmetries at low density is poloidal E × B drift due to radial electric fields in the scrape-off layer and private flux region. In higher-density plasmas, upper and lower targets collected non-ambipolar currents with opposite signs that also inverted upon field reversal. Overall, in these experiments, almost all up-down asymmetry could be attributed to the field reversal and, therefore, field-dependent drifts.

The role of error fields in stellarators is explored in this work through simulations of, and experiments performed on, the Wendelstein 7-X (W7-X) stellarator. Previous experimental results, based on limiter plasmas in W7-X, suggest that error fields are small in the experiment based on limiter plasmas. In this work, we document results from the first divertor campaign on W7-X with a focus on error fields resonant with the edge island divertor structure. Measurements obtained using divertor thermocouples corroborate earlier limiter results that the error fields are small and correctable, requiring only 5% of the total rated current of the trim coil system. The effect of these error fields on magnetic geometry and topology are investigated using the flux surface measurement technique. The resulting divertor thermal load asymmetry is modeled via field line diffusion and compared to experimental data.

In view of the future high power nuclear fusion experiments, the early identification of disruptions is a mandatory requirement, and presently the main goal is moving from the disruption mitigation to disruption avoidance and control. In this work, a deep-convolutional neural network (CNN) is proposed to provide early detection of disruptive events at JET. The CNN ability to learn relevant features, avoiding hand-engineered feature extraction, has been exploited to extract the spatiotemporal information from 1D plasma profiles. The model is trained with regularly terminated discharges and automatically selected disruptive phase of disruptions, coming from the recent ITER-like-wall experiments. The prediction performance is evaluated using a set of discharges representative of different operating scenarios, and an in-depth analysis is made to evaluate the performance evolution with respect to the considered experimental conditions. Finally, as real-time triggers and termination schemes are being developed at JET, the proposed model has been tested on a set of recent experiments dedicated to plasma termination for disruption avoidance and mitigation. The CNN model demonstrates very high performance, and the exploitation of 1D plasma profiles as model input allows us to understand the underlying physical phenomena behind the predictor decision.