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Detecting Plasma Detachment in the Wendelstein 7-X Stellarator Using Machine Learning

التفاصيل البيبلوغرافية
العنوان: Detecting Plasma Detachment in the Wendelstein 7-X Stellarator Using Machine Learning
المؤلفون: Máté Szűcs, Tamás Szepesi, Christoph Biedermann, Gábor Cseh, Marcin Jakubowski, Gábor Kocsis, Ralf König, Marco Krause, Valeria Perseo, Aleix Puig Sitjes, The Team W7-X
المصدر: Applied Sciences, Vol 12, Iss 1, p 269 (2021)
بيانات النشر: MDPI AG, 2021.
سنة النشر: 2021
المجموعة: LCC:Technology
LCC:Engineering (General). Civil engineering (General)
LCC:Biology (General)
LCC:Physics
LCC:Chemistry
مصطلحات موضوعية: fusion plasma physics, plasma detachment, machine learning, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999
الوصف: 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.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2076-3417
Relation: https://www.mdpi.com/2076-3417/12/1/269; https://doaj.org/toc/2076-3417
DOI: 10.3390/app12010269
URL الوصول: https://doaj.org/article/0810aa1ce49548a4bf4672a562a59362
رقم الأكسشن: edsdoj.0810aa1ce49548a4bf4672a562a59362
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:20763417
DOI:10.3390/app12010269