Reconstruction of tokamak plasma safety factor profile using deep learning
Xishuo Wei, Shuying Sun, W. M. Tang, Zhihong Lin, Hongfei Du, Ge Dong
Abstract
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.
Topics & Concepts
TokamakSafety factorPlasmaFactor (programming language)PhysicsNuclear engineeringMaterials scienceNuclear physicsComputer scienceEngineeringProgramming languageMagnetic confinement fusion researchFusion materials and technologiesNuclear Engineering Thermal-Hydraulics