Reconstruction of tokamak plasma safety factor profile using deep learning

Journal Article · 28 June 2023 · Nuclear Fusion

DOI: https://doi.org/10.1088/1741-4326/acdf00 · OSTI ID:1987581

Wei, Xishuo ; Sun, Shuying ; Tang, William; Lin, Zhihong; Du, Hongfei; Dong, Ge

Abstract 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.

• Sponsoring Organization: USDOE
• Grant/Contract Number: FC02-04ER54698
• OSTI ID: 1987581
• Journal Information: Nuclear Fusion, Journal Name: Nuclear Fusion Journal Issue: 8 Vol. 63; ISSN 0029-5515
• Publisher: IOP PublishingCopyright Statement
• Country of Publication: IAEA
• Language: English