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Fast transport simulations with higher-fidelity surrogate models for ITER

Author
Abstract

A fast and accurate turbulence transport model based on quasilinear gyrokinetics is developed. The model consists of a set of neural networks trained on a bespoke quasilinear GENE dataset, with a saturation rule calibrated to dedicated nonlinear simulations. The resultant neural network is approximately eight orders of magnitude faster than the original GENE quasilinear calculations. ITER predictions with the new model project a fusion gain in line with ITER targets. While the dataset is currently limited to the ITER baseline regime, this approach illustrates a pathway to develop reduced-order turbulence models both faster and more accurate than the current state-of-the-art. https://research.tue.nl/en/publications/fast-transport-simulations-with-higher-fidelity-surrogate-models- 

Year of Publication
2023
Journal
Physics of Plasmas
Volume
30
Issue
6
Number of Pages
062501
URL
https://arxiv.org/abs/2306.00662
DOI
10.1063/5.0136752
Dataset
0.5281/zenodo.7706684
PId
0d2c0af834cb3ee24e5a55effe158102
Alternate Journal
Phys. Plasmas
Label
OA
Journal Article
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