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Title: Predicting disruptive instabilities in controlled fusion plasmas through deep learning

Abstract

Nuclear fusion power via magnetic confinement tokamak reactors carries the promise of sustainable and clean energy production for the future [1]. The avoidance of large-scale plasma instabilities called disruptions [2, 3] is one of the most pressing challenges [4, 5] towards this goal. Disruptions are particularly deleterious for large reactors such as the multi-billion dollar international ITER project [6] currently under construction, whose reaction for the first time aims to produce more power from fusion than is injected to heat the plasma. Here, we present a new approach to forecast disruptions based on deep learning that for the first time overcomes the crucial limitations of past work, such as first-principles based [5] and classical machine learning approaches [7, 8, 9, 10, 11]. In particular, our method for the first time (i) delivers reliable predictions on machines other than the one it was trained on — a crucial requirement for large future reactors that cannot afford “training” disruptions; (ii) utilizes high-dimensional training data such as profiles to add new physics information and boost predictive performance; and (iii) engages supercomputing at the largest scale to deliver solutions with improved accuracy and speed. Trained on experimental data from the largest tokamaks in themore » US (DIII-D [12]) and the world (JET [13]), our method moreover can be tuned for physics-specific tasks such as prediction with long warning times, and opens up possible avenues for moving forward the goal from passive disruption prediction to active reactor control and optimization. These results illustrate the potential for deep learning to accelerate progress in fusion energy science and in the understanding and prediction of complex systems in the physical sciences in general.« less

Authors:
 [1];  [2];  [3]
  1. Harvard Univ., Cambridge, MA (United States); Princeton Plasma Physics Lab. (PPPL), Princeton, NJ (United States)
  2. Princeton Univ., NJ (United States)
  3. Princeton Plasma Physics Lab. (PPPL), Princeton, NJ (United States); Princeton Univ., NJ (United States)
Publication Date:
Research Org.:
Princeton Plasma Physics Lab. (PPPL), Princeton, NJ (United States); General Atomics, San Diego, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Fusion Energy Sciences (FES)
OSTI Identifier:
1507546
Alternate Identifier(s):
OSTI ID: 1566845
Grant/Contract Number:  
FG02-97ER25308; FC02-04ER54698
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Nature (London)
Additional Journal Information:
Journal Volume: 568; Journal Issue: 7753; Journal ID: ISSN 0028-0836
Publisher:
Nature Publishing Group
Country of Publication:
United States
Language:
English
Subject:
70 PLASMA PHYSICS AND FUSION TECHNOLOGY

Citation Formats

Kates-Harbeck, Julian, Svyatkovskiy, Alexey, and Tang, William. Predicting disruptive instabilities in controlled fusion plasmas through deep learning. United States: N. p., 2019. Web. doi:10.1038/s41586-019-1116-4.
Kates-Harbeck, Julian, Svyatkovskiy, Alexey, & Tang, William. Predicting disruptive instabilities in controlled fusion plasmas through deep learning. United States. https://doi.org/10.1038/s41586-019-1116-4
Kates-Harbeck, Julian, Svyatkovskiy, Alexey, and Tang, William. 2019. "Predicting disruptive instabilities in controlled fusion plasmas through deep learning". United States. https://doi.org/10.1038/s41586-019-1116-4. https://www.osti.gov/servlets/purl/1507546.
@article{osti_1507546,
title = {Predicting disruptive instabilities in controlled fusion plasmas through deep learning},
author = {Kates-Harbeck, Julian and Svyatkovskiy, Alexey and Tang, William},
abstractNote = {Nuclear fusion power via magnetic confinement tokamak reactors carries the promise of sustainable and clean energy production for the future [1]. The avoidance of large-scale plasma instabilities called disruptions [2, 3] is one of the most pressing challenges [4, 5] towards this goal. Disruptions are particularly deleterious for large reactors such as the multi-billion dollar international ITER project [6] currently under construction, whose reaction for the first time aims to produce more power from fusion than is injected to heat the plasma. Here, we present a new approach to forecast disruptions based on deep learning that for the first time overcomes the crucial limitations of past work, such as first-principles based [5] and classical machine learning approaches [7, 8, 9, 10, 11]. In particular, our method for the first time (i) delivers reliable predictions on machines other than the one it was trained on — a crucial requirement for large future reactors that cannot afford “training” disruptions; (ii) utilizes high-dimensional training data such as profiles to add new physics information and boost predictive performance; and (iii) engages supercomputing at the largest scale to deliver solutions with improved accuracy and speed. Trained on experimental data from the largest tokamaks in the US (DIII-D [12]) and the world (JET [13]), our method moreover can be tuned for physics-specific tasks such as prediction with long warning times, and opens up possible avenues for moving forward the goal from passive disruption prediction to active reactor control and optimization. These results illustrate the potential for deep learning to accelerate progress in fusion energy science and in the understanding and prediction of complex systems in the physical sciences in general.},
doi = {10.1038/s41586-019-1116-4},
url = {https://www.osti.gov/biblio/1507546}, journal = {Nature (London)},
issn = {0028-0836},
number = 7753,
volume = 568,
place = {United States},
year = {2019},
month = {4}
}

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Cited by: 21 works
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Works referencing / citing this record:

Machine learning control for disruption and tearing mode avoidance
journal, February 2020


Progress in disruption prevention for ITER
journal, June 2019


Disruptive plasma simulations in EAST including 3D effects
journal, September 2019


Deep neural network Grad–Shafranov solver constrained with measured magnetic signals
journal, December 2019


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  • Hey, Tony; Butler, Keith; Jackson, Sam
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