SMuK 2021 – scientific programme
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P: Fachverband Plasmaphysik
P 5: Poster I
P 5.44: Poster
Tuesday, August 31, 2021, 14:00–16:00, P
Proof of concept of a fast surrogate model of the VMEC code via neural networks in Wendelstein 7-X scenarios — •Andrea Merlo, Daniel Böckenhoff, Jonathan Schilling, Udo Höfel, Sehyun Kwak, Jakob Svensson, Andrea Pavone, Samuel Aaron Lazerson, Thomas Sunn Pedersen, and the W7-X Team — Max-Planck-Institute for Plasma Physics, 17491 Greifswald, Germany
In magnetic confinement fusion research, 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. The VMEC is the most widely used to evaluate 3D ideal-MHD equilibria, however, considering the computational cost, it is rarely used in large-scale or online applications. Access to fast MHD equilbria is a challenging problem in fusion research, one which machine learning could effectively address. In this work, 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 = 2µ0 p/B) up to 5% and non-zero net toroidal current are included in the data set. The achieved normalized root-mean-squared error ranges from 1% to 20% across the different scenarios. The model inference time for a single equilibrium is on the order of milliseconds. Finally, this work shows the feasibility of a fast NN drop-in surrogate model for VMEC, and it opens up new operational scenarios where target applications could make use of magnetic equilibria at unprecedented scales.