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

Dong, Ge; Wei, X.; Bao, J.; Brochard, G.; Lin, Z.; Tang, W.

doi:10.1088/1741-4326/ac32f1

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

Journal Article · Thu Nov 18 00:00:00 EST 2021 · Nuclear Fusion

DOI:https://doi.org/10.1088/1741-4326/ac32f1· OSTI ID:1835548

^[1]; ^[2]; Bao, J. ^[3]; Brochard, G. ^[2]; Lin, Z. ^[2]; Tang, W. ^[4]

Princeton Plasma Physics Lab. (PPPL), Princeton, NJ (United States); General Atomics, Energy & Advanced Concepts, DIII-D
Univ. of California, Irvine, CA (United States)
Chinese Academy of Sciences (CAS), Beijing (China)
Princeton Plasma Physics Lab. (PPPL), Princeton, NJ (United States)

The accurate identification and control of plasma instabilities is important for successful fusion experiments. First-principle simulations that can provide physics-based instability information such as the mode structure are generally not fast enough for real-time applications. In this work, a workflow has been presented to develop deep-learning based surrogate models for the first-principle simulations using the gyrokinetic toroidal code (GTC). The trained surrogate models of GTC (SGTC) can be used as physics-based fast instability simulators that run on the order of milliseconds, which fits the requirement of the real-time plasma control system. Here, we demonstrate the feasibility of this workflow by first creating a big database from GTC systematic linear global electromagnetic simulations of the current-driven kink instabilities in DIII-D plasmas, and then developing SGTC linear internal kink instability simulators through supervised training. SGTC linear internal kink simulators demonstrate predictive capabilities for the mode instability properties including the growth rate and mode structure.

View Accepted Manuscript (DOE)

Research Organization:: General Atomics, San Diego, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Fusion Energy Sciences (FES)

Grant/Contract Number:: AC02-05CH11231; AC05-00OR22725; FC02-04ER54698

OSTI ID:: 1835548

Alternate ID(s):: OSTI ID: 1856188

Journal Information:: Nuclear Fusion, Journal Name: Nuclear Fusion Journal Issue: 12 Vol. 61; ISSN 0029-5515

Publisher:: IOP ScienceCopyright Statement

Country of Publication:: United States

Language:: English

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