학술논문
Multimodal Prediction of Tearing Instabilities in a Tokamak
Document Type
Conference
Author
Source
2023 International Joint Conference on Neural Networks (IJCNN) Neural Networks (IJCNN), 2023 International Joint Conference on. :1-8 Jun, 2023
Subject
Language
ISSN
2161-4407
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.