Multimodal Prediction of Tearing Instabilities in a Tokamak

Jaemin Seo, Rory Conlin, Andrew Rothstein, Sang Kyeun Kim, Joseph Abbate, Azarakhsh Jalalvand, Egemen Kolemen

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

1 Scopus citations

Abstract

Tokamak is a torus-shaped nuclear fusion device that uses magnetic fields to confine fusion fuel in the form of plasma. Tearing instability in plasma is a major issue in which the magnetic field breaks and recombines in tokamak. This instability can lead to plasma disruption that terminates the fusion power generation and damages the plasma-facing wall materials. For a successful steady operation of a large-scale tokamak without disruption, it is required to predict and alarm the tearing instabilities well in advance to avoid them. In this work, we develop and validate a deep neural network-based multimodal prediction system that estimates the future tearing instability likelihood from multi-diagnostics signals in the DIII-D tokamak.

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

  • deep neural network
  • multimodal prediction
  • nuclear fusion
  • tearing instability
  • tokamak

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