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

TL;DR

This paper develops an advanced neural network surrogate model, QLKNN-jetexp-15D, trained on JET experimental data, to significantly accelerate turbulent transport calculations in tokamak plasma modeling while maintaining high accuracy.

Contribution

It extends previous neural network models by incorporating impurity, rotation, and magnetic effects, trained on experimental data to improve speed and accuracy in plasma transport simulations.

Findings

The new model achieves less than 10% profile-averaged RMS error in simulations.

Evaluation time per output quantity is approximately 0.1 ms, 10,000 times faster than original models.

Speed increase in integrated modeling is 60-100 times, enabling more efficient plasma simulations.

Abstract

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 <10% for each of the 5 scenarios tested. This is non-trivial given the potential numerical instabilities present within the highly nonlinear system of equations governing plasma transport, especially considering the novel addition of momentum flux predictions to the model proposed here. An evaluation of all 25 NN output quantities at one radial location takes $\sim$0.1 ms, $10^4$ times faster than the original QuaLiKiz model. Within the JINTRAC integrated modelling tests performed in this study, using QLKNN-jetexp-15D resulted in a speed increase of only 60 - 100 as other physics modules outside of turbulent transport become the bottleneck.