Neural network surrogate of QuaLiKiz using JET experimental data to populate training space

TitleNeural network surrogate of QuaLiKiz using JET experimental data to populate training space
Publication TypeJournal Article
Year of Publication2021
AuthorsA. Ho, J. Citrin, C. Bourdelle, Y. Camenen, F.J. Casson, K.L. van de Plassche, H. Weisen, JET Contributors
JournalPhysics of Plasmas
Volume28
Issue3
Pagination032305
Abstract

Within integrated tokamak plasma modeling, turbulent transport codes are typically the computational bottleneck limiting their routine use outside of post-discharge analysis. Neural network (NN) surrogates have been used to accelerate these calculations while retaining the desired accuracy of the physics-based models. This paper extends a previous NN model, known as QLKNN-hyper-10D, by incorporating the impact of impurities, plasma rotation, and magnetic equilibrium effects. This is achieved by adding a light impurity fractional density (n imp,light/n e) and its normalized gradient, the normalized pressure gradient (α), the toroidal Mach number (M tor), and the normalized toroidal flow velocity gradient. The input space was sampled based on experimental data from the JET tokamak to avoid the curse of dimensionality. The resulting networks, named QLKNN-jetexp-15D, show good agreement with the original QuaLiKiz model, both by comparing individual transport quantity predictions and by comparing its impact within the integrated model, JINTRAC. The profile-averaged RMS of the integrated modeling simulations is

DOI10.1063/5.0038290
Division

FP

Department

IMT

PID

8fd7d2a7f7d24bb26a36f713f523577b

Alternate TitlePhys. Plasmas

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