Real-time capable first principle based modelling of tokamak turbulent transport
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.
Keywords
Cite
@article{arxiv.1502.07402,
title = {Real-time capable first principle based modelling of tokamak turbulent transport},
author = {J. Citrin and S. Breton and F. Felici and F. Imbeaux and T. Aniel and J. F. Artaud and B. Baiocchi and C. Bourdelle and Y. Camenen and J. Garcia},
journal= {arXiv preprint arXiv:1502.07402},
year = {2015}
}
Comments
10 pages, 3 figures