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arXiv · 2106.10849

Deep learning based surrogate model for first-principles global simulations of fusion plasmas

Ge Dong·Xishuo Wei·Jian Bao·Guillaume Brochard·Zhihong Lin·William Tang
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Abstract

The accurate identification and control of plasma instabilities is important for successful fusion experiments. First-principles simulations which can provide physics based instability information including the growth rate and mode structure are generally not fast enough for real-time applications. In this work, a deep-learning based surrogate model as an instability simulator has been developed and trained in a supervised manner with data from the gyrokinetic toroidal code (GTC) global electromagnetic simulations of the current driven kink instabilities in DIII-D plasmas. The inference time of the surrogate model of GTC (SGTC) is on the order of milliseconds, which fits the requirement of the DIII-D real-time plasma control system (PCS). SGTC demonstrates strong predictive capabilities for the kink mode instability properties including the growth rate and mode structure.

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Ge Dong, Xishuo Wei, Jian Bao, Guillaume Brochard, Zhihong Lin, William Tang. 2021-06-21. Deep learning based surrogate model for first-principles global simulations of fusion plasmas. https://doi.org/10.1088/1741-4326%2Fac32f1

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