Reconstruction of tokamak plasma safety factor profile using deep learning

Nuclear Fushion

article

Xishuo Wei¹  Shuying Sun²  William Tang³  Zhihong Lin¹  Hongfei Du⁴  Ge Dong³ 

[1] University of California;Fusion Simulation Center, Peking University;Princeton Plasma Physics Laboratory, Princeton;Energy Singularity

关键词: electron-scale turbulence;    magnetic shear;    neon seeding;    confinement;    tokamak;   

DOI  :  10.1088/1741-4326/acdf00

来源: Institute of Physics Publishing Ltd.

【 摘 要 】

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.

【 授权许可】

Unknown