1. Detecting Plasma Detachment in the Wendelstein 7-X Stellarator Using Machine Learning
- Author
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W7-X Team, Szűcs, Máté, Szepesi, Tamás, Biedermann, Christoph, Cseh, Gábor, Jakubowski, Marcin, Kocsis, Gábor, König, Ralf, Krause, Marco, Perseo, Valeria, Puig Sitjes, Aleix, Gantenbein, Gerd, Huber, Martina, Illy, Stefan, Jelonnek, John, Kobarg, Thorsten, Lang, Rouven, Leonhardt, Wolfgang, Mellein, Daniel, Papenfuß, Daniel, Thumm, Manfred, Wadle, Simone, Weggen, Jörg, and W7-X Team, Max Planck Institute for Plasma Physics, Max Planck Society
- Subjects
Fluid Flow and Transfer Processes ,Technology ,QH301-705.5 ,Process Chemistry and Technology ,Physics ,QC1-999 ,fusion plasma physics ,plasma detachment ,machine learning ,General Engineering ,Engineering (General). Civil engineering (General) ,01 natural sciences ,010305 fluids & plasmas ,Computer Science Applications ,Chemistry ,0103 physical sciences ,General Materials Science ,TA1-2040 ,Biology (General) ,010306 general physics ,Instrumentation ,ddc:600 ,QD1-999 - Abstract
The detachment regime has a high potential to play an important role in fusion devices on the road to a fusion power plant. Complete power detachment has been observed several times during the experimental campaigns of the Wendelstein 7-X (W7-X) stellarator. Automatic observation and signaling of such events could help scientists to better understand these phenomena. With the growing discharge times in fusion devices, machine learning models and algorithms are a powerful tool to process the increasing amount of data. We investigate several classical supervised machine learning models to detect complete power detachment in the images captured by the Event Detection Intelligent Camera System (EDICAM) at the W7-X at each given image frame. In the dedicated detached state the plasma is stable despite its reduced contact with the machine walls and the radiation belt stays close to the separatrix, without exhibiting significant heat load onto the divertor. To decrease computational time and resources needed we propose certain pixel intensity profiles (or intensity values along lines) as the input to these models. After finding the profile that describes the images best in terms of detachment, we choose the best performing machine learning algorithm. It achieves an F1 score of 0.9836 on the training dataset and 0.9335 on the test set. Furthermore, we investigate its predictions in other scenarios, such as plasmas with substantially decreased minor radius and several magnetic configurations.
- Published
- 2022