Alfvén eigenmode detection using Long-Short Term Memory Networks and CO2Interferometer data on the DIII-D National Fusion Facility

Alvin V. Garcia, Azarakhsh Jalalvand, Peter Steiner, Andrew Rothstein, Michael Van Zeeland, William W. Heidbrink, Egemen Kolemen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

The successful steady-state operation of burning fusion plasmas in planned future devices such as the ITER tokamak requires understanding of fast-ion physics. Alfven eigenmodes are special cases of plasma waves driven by fast ions that are important to identify and control since they can lead to loss of confinement and potential damage to the inner walls of a plasma device. The goal of this work is to compare machine learning-based systems trained to classify Alfven eigenmodes using CO2interferometer data from a labelled database on the DIII-D tokamak. A Long-Short Term Memory (LSTM) network is trained from scratch using simple spectrogram representations of the CO2 phase data. The model is trained using a single chord (sequence) per training step. Results show a total true positive rate of = 90% and a false positive rate of = 18%. This paper demonstrates the potential of applying machine learning models to detect and identify different classes of Alfven eigenmodes for real-time applications in steady-state plasma operations that could potentially drive actuators to mitigate Alfven eigenmode impacts.

Original languageEnglish (US)
Title of host publicationIJCNN 2023 - International Joint Conference on Neural Networks, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665488679
DOIs
StatePublished - 2023
Event2023 International Joint Conference on Neural Networks, IJCNN 2023 - Gold Coast, Australia
Duration: Jun 18 2023Jun 23 2023

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume2023-June

Conference

Conference2023 International Joint Conference on Neural Networks, IJCNN 2023
Country/TerritoryAustralia
CityGold Coast
Period6/18/236/23/23

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence

Keywords

  • Alfven Eigenmodes
  • CO2 Interferometry
  • DIII-D Tokamak
  • Fusion Energy
  • Machine Learning Classification

Fingerprint

Dive into the research topics of 'Alfvén eigenmode detection using Long-Short Term Memory Networks and CO2Interferometer data on the DIII-D National Fusion Facility'. Together they form a unique fingerprint.

Cite this