Automatic Identification of Edge Localized Modes in the DIII-D Tokamak

TL;DR

This paper introduces an algorithm that automatically labels Edge Localized Modes in tokamak data with high precision, facilitating the development of machine learning models for real-time mode prediction and control.

Contribution

The paper presents a parameter-free, robust algorithm achieving 97.7% precision for labeling ELMs in DIII-D data, enabling improved machine learning-based control.

Findings

Achieved 97.7% precision in ELM labeling.

Algorithm is robust across different tokamak configurations.

Automatically labeled dataset supports future ML-based ELM control.

Abstract

Fusion power production in tokamaks uses discharge configurations that risk producing strong Type I Edge Localized Modes. The largest of these modes will likely increase impurities in the plasma and potentially damage plasma facing components such as the protective heat and waste divertor. Machine learning-based prediction and control may provide for online mitigation of these damaging modes before they grow too large to suppress. To that end, large labeled datasets are required for supervised training of machine learning models. We present an algorithm that achieves 97.7% precision when automatically labeling Edge Localized Modes in the large DIII-D tokamak discharge database. The algorithm has no user controlled parameters and is largely robust to tokamak and plasma configuration changes. This automatically-labeled database of events can subsequently feed future training of machine learning models aimed at autonomous Edge Localized Mode control and suppression.