Multimodal Prediction of Tearing Instabilities in a Tokamak

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

doi:10.1109/IJCNN54540.2023.10191359

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 proceeding › Conference contribution

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 language	English (US)
Title of host publication	IJCNN 2023 - International Joint Conference on Neural Networks, Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665488679
DOIs	https://doi.org/10.1109/IJCNN54540.2023.10191359
State	Published - 2023
Event	2023 International Joint Conference on Neural Networks, IJCNN 2023 - Gold Coast, Australia Duration: Jun 18 2023 → Jun 23 2023

Publication series

Name	Proceedings of the International Joint Conference on Neural Networks
Volume	2023-June

Conference

Conference	2023 International Joint Conference on Neural Networks, IJCNN 2023
Country/Territory	Australia
City	Gold Coast
Period	6/18/23 → 6/23/23

All Science Journal Classification (ASJC) codes

Software
Artificial Intelligence

Keywords

deep neural network
multimodal prediction
nuclear fusion
tearing instability
tokamak

Access to Document

10.1109/IJCNN54540.2023.10191359

Cite this

Seo, J., Conlin, R., Rothstein, A., Kim, S. K., Abbate, J., Jalalvand, A., & Kolemen, E. (2023). Multimodal Prediction of Tearing Instabilities in a Tokamak. In IJCNN 2023 - International Joint Conference on Neural Networks, Proceedings (Proceedings of the International Joint Conference on Neural Networks; Vol. 2023-June). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IJCNN54540.2023.10191359

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title = "Multimodal Prediction of Tearing Instabilities in a Tokamak",

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.",

keywords = "deep neural network, multimodal prediction, nuclear fusion, tearing instability, tokamak",

author = "Jaemin Seo and Rory Conlin and Andrew Rothstein and Kim, {Sang Kyeun} and Joseph Abbate and Azarakhsh Jalalvand and Egemen Kolemen",

note = "Funding Information: This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Fusion Energy Sciences, using the DIII-D National Fusion Facility, a DOE Office of Science user facility, under Award DE-FC02-04ER54698. This work was also supported by the National Research Foundation of Korea(NRF) funded by the Korea government. (Ministry of Science and ICT) (RS-2023-00255492) Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 International Joint Conference on Neural Networks, IJCNN 2023 ; Conference date: 18-06-2023 Through 23-06-2023",

year = "2023",

doi = "10.1109/IJCNN54540.2023.10191359",

language = "English (US)",

series = "Proceedings of the International Joint Conference on Neural Networks",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "IJCNN 2023 - International Joint Conference on Neural Networks, Proceedings",

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}

Seo, J, Conlin, R, Rothstein, A, Kim, SK, Abbate, J, Jalalvand, A & Kolemen, E 2023, Multimodal Prediction of Tearing Instabilities in a Tokamak. in IJCNN 2023 - International Joint Conference on Neural Networks, Proceedings. Proceedings of the International Joint Conference on Neural Networks, vol. 2023-June, Institute of Electrical and Electronics Engineers Inc., 2023 International Joint Conference on Neural Networks, IJCNN 2023, Gold Coast, Australia, 6/18/23. https://doi.org/10.1109/IJCNN54540.2023.10191359

Multimodal Prediction of Tearing Instabilities in a Tokamak. / Seo, Jaemin; Conlin, Rory; Rothstein, Andrew et al.
IJCNN 2023 - International Joint Conference on Neural Networks, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2023. (Proceedings of the International Joint Conference on Neural Networks; Vol. 2023-June).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

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AU - Seo, Jaemin

AU - Conlin, Rory

AU - Rothstein, Andrew

AU - Kim, Sang Kyeun

AU - Abbate, Joseph

AU - Jalalvand, Azarakhsh

AU - Kolemen, Egemen

N1 - Funding Information: This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Fusion Energy Sciences, using the DIII-D National Fusion Facility, a DOE Office of Science user facility, under Award DE-FC02-04ER54698. This work was also supported by the National Research Foundation of Korea(NRF) funded by the Korea government. (Ministry of Science and ICT) (RS-2023-00255492) Publisher Copyright: © 2023 IEEE.

PY - 2023

Y1 - 2023

N2 - 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.

AB - 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.

KW - deep neural network

KW - multimodal prediction

KW - nuclear fusion

KW - tearing instability

KW - tokamak

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T3 - Proceedings of the International Joint Conference on Neural Networks

BT - IJCNN 2023 - International Joint Conference on Neural Networks, Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2023 International Joint Conference on Neural Networks, IJCNN 2023

Y2 - 18 June 2023 through 23 June 2023

ER -

Seo J, Conlin R, Rothstein A, Kim SK, Abbate J, Jalalvand A et al. Multimodal Prediction of Tearing Instabilities in a Tokamak. In IJCNN 2023 - International Joint Conference on Neural Networks, Proceedings. Institute of Electrical and Electronics Engineers Inc. 2023. (Proceedings of the International Joint Conference on Neural Networks). doi: 10.1109/IJCNN54540.2023.10191359

Multimodal Prediction of Tearing Instabilities in a Tokamak

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All Science Journal Classification (ASJC) codes

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