Tokamak disruption alarm based on a neural network model of the high- beta limit
Open accessAbstract
An artificial neural network, combining signals from a large number of plasma diagnostics, was used to estimate the high- beta disruption boundary in the DIII-D tokamak. It is shown that inclusion of many diagnostic measurements results in a much more accurate prediction of the disruption boundary than that provided by the traditional Troyon limit. A trained neural network constitutes a non-linear, non-parametric model of the disruption boundary. Through the analysis of the input-output sensitivities, the relative statistical significance of various diagnostic measurements (plasma parameters) for the determination of the disruption boundary is directly assessed and the number of diagnostics used by the neural network model is reduced to the necessary minimum. The neural network is trained to map the disruption boundary throughout most of the discharge. As a result, it can predict the high- beta disruption boundary on a time-scale of the order of 100 ms (much longer than the precursor growth time), which makes this approach ideally suitable for real time application in a disruption avoidance scheme. Owing to the relative simplicity of the required computations, the neural network is easily implemented in a real time system. A prototype of the neural network disruption alarm was installed within the DIII-D digital plasma control system, and its real time operation, with a typical time resolution of 10 ms, was demonstrated
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Prior work has tried to preemptively detect disruptions for tokamak reactors, using supervised learning methods such as support vector machines, adaptive fuzzy logic, decision trees, and deep learning on previous disruption data.
“Prior work has tried to preemptively detect disruptions for tokamak reactors, using supervised learning methods such as supportvector machines, adaptive fuzzy logic, decision trees, and deep learning [110,413,567,811,846,850] on previous disruption data.”
From Tackling climate change with machine learning (2023) · cites this paper as [850]
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- Authors
- Wroblewski, D., Jahns, G. L., & Leuer, J. A.
- Affiliations
- Lockheed Martin Orincon (United States) (US)
- ORCID
- Leuer, J.A
- Venue
- Nuclear Fusion
- Publisher
- IOP Publishing
- Published
- 1997-06
- Volume
- 37
- Issue
- 6
- Pages
- 725-741
- ISSN
- 0029-5515 (ISSN-L)
- OpenAlex
- W2014096208
- Cited by
- 135
- References
- 22
- Citations by year
- 2026: 3 · 2025: 6 · 2024: 6 · 2023: 8 · 2022: 5 · 2021: 14 · 2020: 17 · 2019: 7 · 2018: 5 · 2017: 1 · 2016: 4 · 2015: 3 · 2014: 3 · 2013: 3
- FWCI
- 2.82
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Nuclear Fusion (publishedVersion)
- Funders
- U.S. Department of Energy
- Small Business Innovation Research
- Princeton Plasma Physics Laboratory
- Topics
- Physical Sciences › Physics and Astronomy › Nuclear and High Energy Physics › Magnetic confinement fusion research
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Fusion and Plasma Physics Studies
- Physical Sciences › Materials Science › Materials Chemistry › Fusion materials and technologies
- Keywords
- tokamak disruptions, artificial neural networks, DIII-D tokamak, plasma diagnostics, disruption avoidance, plasma control system
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