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

• Published in: arXiv.org 2021-05
• Main Authors: Ho, Aaron, Citrin, Jonathan, Bourdelle, Clarisse, Camenen, Yann, Casson, Francis J, Karel L van de Plassche, Weisen, Henri, Contributors, JET
• Format: Article
• Language: English
• Subjects: Computational fluid dynamics; Flow velocity; Impurities; Joint European Torus; Mach number; Model accuracy; Neural networks; Nonlinear systems; Rotating plasmas; Tokamak devices; Velocity gradient
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.2105.01168

Within integrated tokamak plasma modelling, 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 (\(\alpha\)), 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 as well as comparing its impact within the integrated model, JINTRAC. The profile-averaged RMS of the integrated modelling simulations is