Reconstruction of tokamak plasma safety factor profile using deep learning

TL;DR

This paper introduces a deep learning model for reconstructing the safety factor profile in tokamaks, enabling real-time control without relying on MSE measurements, which are often unavailable.

Contribution

The authors develop a deep learning surrogate model that reconstructs q profiles without MSE data, improving real-time equilibrium reconstruction in tokamaks.

Findings

Model achieves accurate q profile reconstruction.

Inference time is under one millisecond.

Compatible with real-time plasma control systems.

Abstract

In tokamak operations, accurate equilibrium reconstruction is essential for reliable real-time control and realistic post-shot instability analysis. The safety factor (q) profile defines the magnetic field line pitch angle, which is the central element in equilibrium reconstruction. 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 surrogate model of the gyrokinetic toroidal code for q profile reconstruction (SGTC-QR) that can reconstruct the q profile with the measurements without MSE 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.