Identifying L-H transition in HL-2A through deep learning

TL;DR

This paper presents a deep learning algorithm using Residual LSTM and TCN to accurately identify the L-H transition in HL-2A tokamak in real-time, enabling timely ELM mitigation measures.

Contribution

The study introduces a novel deep learning model for real-time L-H transition detection, improving early identification over previous slice-based recognition methods.

Findings

The algorithm effectively recognizes L-H transition before ELMs occur.

Evaluation indicators demonstrate high accuracy and reliability.

The method provides a practical reference for ELM mitigation systems.

Abstract

During the operation of tokamak devices, addressing the thermal load issues caused by Edge Localized Modes (ELMs) eruption is crucial. Ideally, mitigation and suppression measures for ELMs should be promptly initiated as soon as the first low-to-high confinement (L-H) transition occurs, which necessitates the real-time monitoring and accurate identification of the L-H transition process. Motivated by this, and by recent deep learning boom, we propose a deep learning-based L-H transition identification algorithm on HL-2A tokamak. In this work, we have constructed a neural network comprising layers of Residual Long Short-Term Memory (LSTM) and Temporal Convolutional Network (TCN). Unlike previous work based on recognition for ELMs by slice, this method implements recognition on L-H transition process before the first ELMs crash. Therefore the mitigation techniques can be triggered in time to suppress the initial ELMs bursts. In order to further explain the effectiveness of the algorithm, we developed a series of evaluation indicators by shots, and the results show that this algorithm can provide necessary reference for the mitigation and suppression system.