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.
Highlights
The previous years brought a considerable increase in the different applications of machine learning in all fields of natural sciences and, in particular, in fusion plasma physics [1,2,3,4]
One of the major aims of this study was to show that power detachment can be detected using the combination of the installed video diagnostics and intelligent algorithms, and how these algorithms can enhance the capabilities of such diagnostics
Detachment can solve the problem of erosion and heat load on the plasma facing components (PFCs), is considered to be the primary operating regime for ITER [12]
Summary
The previous years brought a considerable increase in the different applications of machine learning in all fields of natural sciences and, in particular, in fusion plasma physics [1,2,3,4]. The main advantage of such configuration is that the plasma is only in contact with solid surfaces made for this exact purpose This leads to high concentration of the power flux in a narrow stripe along the line of the separatrix–divertor target intersection, called strike line [11]. We achieve detachment by density increase or by puffing additional gas—typically a lower-Z impurity, such as Nitrogen—in the divertor domain, resulting in a decrease of plasma ion flux, accompanied by a large drop of plasma density and temperature in the edge plasma region, in front of the targets The consequence of these phenomena is the significant reduction of power load on the divertor tiles. Detachment can solve the problem of erosion and heat load on the PFCs, is considered to be the primary operating regime for ITER [12]
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