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Proof of concept of a fast surrogate model of the VMEC code via neural networks in Wendelstein 7-X scenarios

W7-X Team; Merlo, Andrea; Böckenhoff, Daniel; Schilling, Jonathan; Höfel, Udo; Kwak, Sehyun; Svensson, Jakob; Pavone, Andrea; Lazerson, Samuel Aaron; Pedersen, Thomas Sunn; Gantenbein, Gerd; Huber, Martina; Illy, Stefan; Jelonnek, John; Kobarg, Thorsten; Lang, Rouven; Leonhardt, Wolfgang; Mellein, Daniel; Papenfuß, Daniel; ... mehr


In magnetic confinement fusion research, the achievement of high plasma pressure is key to reaching the goal of net energy production. The magnetohydrodynamic (MHD) model is used to self-consistently calculate the effects the plasma pressure induces on the magnetic field used to confine the plasma. Such MHD calculations—usually done computationally—serve as input for the assessment of a number of important physics questions. The variational moments equilibrium code (VMEC) is the most widely used to evaluate 3D ideal-MHD equilibria, as prominently present in stellarators. However, considering the computational cost, it is rarely used in large-scale or online applications (e.g. Bayesian scientific modeling, real-time plasma control). Access to fast MHD equilibria is a challenging problem in fusion research, one which machine learning could effectively address. In this paper, we present artificial neural network (NN) models able to quickly compute the equilibrium magnetic field of Wendelstein 7-X. Magnetic configurations that extensively cover the device operational space, and plasma profiles with volume-averaged normalized plasma pressure ⟨β⟩ (β = $\frac{2{\mu }_{0}p}{{B}^{2}}$) up to 5% and non-zero net toroidal current are included in the data set. ... mehr

Verlagsausgabe §
DOI: 10.5445/IR/1000137157
Veröffentlicht am 03.09.2021
DOI: 10.1088/1741-4326/ac1a0d
Zitationen: 2
Web of Science
Zitationen: 1
Zitationen: 3
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Angewandte Materialien – Angewandte Werkstoffphysik (IAM-AWP)
Institut für Hochleistungsimpuls- und Mikrowellentechnik (IHM)
Publikationstyp Zeitschriftenaufsatz
Publikationsmonat/-jahr 09.2021
Sprache Englisch
Identifikator ISSN: 0029-5515, 1741-4326
KITopen-ID: 1000137157
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 61
Heft 9
Seiten Art.-Nr.: 096039
Projektinformation EUROfusion_H2020 (EU, H2020, 633053)
Vorab online veröffentlicht am 24.08.2021
Nachgewiesen in Dimensions
Web of Science
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