Protection of the first wall of Wendelstein 7-X with artificial neural networks

One of the main objectives of the nuclear fusion experiment Wendelstein 7-X is to demonstrate steady state capability of the stellarator confinement concept. To ensure the safety of the first wall and protect the plasma from impurities, heat load pattern control is essential for long term operation. It is demonstrated that artificial neural networks can be used to accurately and efficiently predict details of the magnetic topology at the plasma edge of Wendelstein 7-X, based on simulated as well as measured heat load patterns onto plasma-facing components observed with infrared cameras. The use of a neural network makes it feasible to analyze and control the plasma exhaust in real-time, an important goal for Wendelstein 7-X, and for magnetic confinement fusion research in general.