دورية أكاديمية
Detecting Plasma Detachment in the Wendelstein 7-X Stellarator Using Machine Learning
العنوان: | Detecting Plasma Detachment in the Wendelstein 7-X Stellarator Using Machine Learning |
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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 |
تدمد: | 20763417 |
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DOI: | 10.3390/app12010269 |