Reconstruction of tokamak plasma safety factor profile using deep learning

The motional Stark effect (MSE) diagnostic has been a standard measurement for the magnetic field line pitch angle in tokamaks that are equipped with neutral beams. However, the MSE data are not always available due to experimental constraints, especially in future devices without neutral beams. Her...

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Bibliographic Details
Published in:Nuclear fusion 2023-08, Vol.63 (8), p.86020
Main Authors: Wei, Xishuo, Sun, Shuying, Tang, William, Lin, Zhihong, Du, Hongfei, Dong, Ge
Format: Article
Language:English
Subjects:
artificial intelligence
deep learning
safety profile
tokamak equilibrium reconstruction
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Summary:The motional Stark effect (MSE) diagnostic has been a standard measurement for the magnetic field line pitch angle in tokamaks that are equipped with neutral beams. However, the MSE data are not always available due to experimental constraints, especially in future devices without neutral beams. Here we develop a deep-learning based model (SGTC-QR) that can reconstruct the safety factor profile without the MSE diagnostic to mimic the traditional equilibrium reconstruction with the MSE constraint. The model demonstrates promising performance, and the sub-millisecond inference time is compatible with the real-time plasma control system.
ISSN:0029-5515
1741-4326
DOI:10.1088/1741-4326/acdf00