A machine-learning-based tool for last closed-flux surface reconstruction on tokamaks

TL;DR

This paper introduces a machine learning tool for real-time reconstruction of the Last Closed-Flux Surface in tokamaks, achieving over 99% accuracy, which enhances plasma control and fusion research.

Contribution

The work presents a novel machine learning model capable of real-time LCFS reconstruction in tokamaks, trained on experimental EAST data, enabling online control applications.

Findings

Achieves over 99% similarity in LCFS reconstruction

Enables real-time plasma boundary prediction

Supports offline simulation and online control

Abstract

Nuclear fusion represents one of the best alternatives for a sustainable source of clean energy. Tokamaks allow to confine fusion plasma with magnetic fields and one of the main challenges in the control of the magnetic configuration is the prediction/reconstruction of the Last Closed-Flux Surface (LCFS). The evolution in time of the LCFS is determined by the interaction of the actuator coils and the internal tokamak plasma. This task requires real-time capable tools able to deal with high-dimensional data as well as with high resolution in time, where the interaction between a wide range of input actuator coils with internal plasma state responses add additional layer of complexity. In this work, we present the application of a novel state of the art machine learning model to the LCFS reconstruction in the Experimental Advanced Superconducting Tokamak (EAST) that learns automatically from the experimental data of EAST. This architecture allows not only offline simulation and testing of a particular control strategy, but can also be embedded in the real-time control system for online magnetic equilibrium reconstruction and prediction. In the real-time modeling test, our approach achieves very high accuracies, with over 99% average similarity in LCFS reconstruction of the entire discharge process.