Real-time capable first principle based modelling of tokamak turbulent transport

TL;DR

This paper presents a neural network-based model for tokamak turbulence transport that achieves real-time simulation speeds while maintaining physics accuracy, enabling advanced control and modeling in fusion reactors.

Contribution

A novel neural network model trained on quasilinear gyrokinetic outputs that significantly accelerates transport simulations for tokamaks.

Findings

Reproduces transport fluxes with high accuracy

Achieves five orders of magnitude speedup

Successfully simulates a 300s ITER discharge in 10s

Abstract

A real-time capable core turbulence tokamak transport model is developed. This model is constructed from the regularized nonlinear regression of quasilinear gyrokinetic transport code output. The regression is performed with a multilayer perceptron neural network. The transport code input for the neural network training set consists of five dimensions, and is limited to adiabatic electrons. The neural network model successfully reproduces transport fluxes predicted by the original quasilinear model, while gaining five orders of magnitude in computation time. The model is implemented in a real-time capable tokamak simulator, and simulates a 300s ITER discharge in 10s. This proof-of-principle for regression based transport models anticipates a significant widening of input space dimensionality and physics realism for future training sets. This aims to provide unprecedented computational speed coupled with first-principle based physics for real-time control and integrated modelling applications.