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Automated W7-X sawtooth crashes detection and characterization

W7-X Team 1; Zanini, M.; Aymerich, E.; Böckenhoff, D.; Merlo, A.; Aleynikova, K.; Brandt, C.; Braune, H.; Brunner, K. J.; Hirsch, M.; Höfel, U.; Knauer, J.; Laqua, H. P.; Marsen, S.; Pavone, A.; Rahbarnia, K.; Schilling, J.; Smith, T.; Stange, T.; ... mehr

Abstract (englisch):

Sawtooth crashes are observed during ECCD experiments at the superconducting optimized stellarator Wendelstein 7-X. The study and the characterization are necessary in order to understand under which condition ECCD can be driven without posing a risk to experimental operations. The development of automatic tools is crucial to speed up the analysis of extensive datasets. In this work, we report on the first attempt of using a data-driven approach to automatically characterize the sawtooth crashes. Cluster algorithms are applied to the dataset, confirming the existence of two distinct types of crashes. This approach allows to study the two groups separately and underlines the different plasma parameters that influence the sawtooth crash parameters, for instance crash amplitude and period.


Verlagsausgabe §
DOI: 10.5445/IR/1000171469
Veröffentlicht am 10.06.2024
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Hochleistungsimpuls- und Mikrowellentechnik (IHM)
Publikationstyp Zeitschriftenaufsatz
Publikationsmonat/-jahr 07.2024
Sprache Englisch
Identifikator ISSN: 0029-5515, 1741-4326
KITopen-ID: 1000171469
HGF-Programm 31.13.02 (POF IV, LK 01) Plasma Heating & Current Drive Systems
Erschienen in Nuclear Fusion
Verlag International Atomic Energy Agency (IAEA)
Band 64
Heft 7
Seiten Art.-Nr.: 076027
Projektinformation EUROfusion (EU, EU 9. RP, 101052200)
Vorab online veröffentlicht am 03.06.2024
Schlagwörter stellarator, sawtooth, ECCD, data clustering
Nachgewiesen in Scopus
Dimensions
Web of Science
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