Prototype of an adaptive disruption predictor for JET based on fuzzy logic and regression trees
Abstract
Disruptions remain one of the most hazardous events in the operation of a tokamak device, since they can cause damage to the vacuum vessel and surrounding structures. Their potential danger increases with the plasma volume and energy content and therefore they will constitute an even more serious issue for the next generation of machines. For these reasons, in the recent years a lot of attention has been devoted to devise predictors, capable of foreseeing the imminence of a disruption sufficiently in advance, to allow time for undertaking remedial actions. In this paper, the results of applying fuzzy logic and classification and regression trees (CART) to the problem of predicting disruptions at JET are reported. The conceptual tools of fuzzy logic, in addition to being well suited to accommodate the opinion of experts even if not formulated in mathematical but linguistic terms, are also fully transparent, since their governing rules are human defined. They can therefore help not only in forecasting disruptions but also in studying their behaviour. The analysis leading to the rules of the fuzzy predictor has been complemented with a systematic investigation of the correlation between the various experimental signals and the imminence of a disruption. This has been performed with an exhaustive, non-linear and unbiased method based on decision trees. This investigation has confirmed that the relative importance of various signals can change significantly depending on the plasma conditions. On the basis of the results provided by CART on the information content of the various quantities, the prototype of an adaptive fuzzy logic predictor was trained and tested on JET database. Its performance is significantly better than the previous static one, proving that more flexible prediction strategies, not uniform over the whole discharge but tuned to the operational region of the plasma at any given time, can be very competitive and should be investigated systematically.
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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 [567]
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Predicting disruptive instabilities in controlled fusion plasmas through deep learning
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- Authors
- Murari, A., Vagliasindi, G., Arena, P., Fortuna, L., Barana, O., & Johnson, M.
- Affiliations
- National Agency for New Technologies, Energy and Sustainable Economic Development (IT)
- University of Catania (IT)
- Culham Centre for Fusion Energy (GB)
- Venue
- Nuclear Fusion
- Publisher
- IOP Publishing
- Published
- 2008-02-07
- Volume
- 48
- Issue
- 3
- Pages
- 035010
- ISSN
- 0029-5515 (ISSN-L)
- 1741-4326
- OpenAlex
- W2036137984
- Cited by
- 72
- References
- 19
- Citations by year
- 2026: 3 · 2025: 5 · 2024: 9 · 2023: 6 · 2022: 4 · 2021: 6 · 2020: 5 · 2019: 6 · 2018: 5 · 2017: 1 · 2016: 1 · 2015: 3 · 2014: 4 · 2013: 1 · 2012: 1
- FWCI
- 2.14
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Nuclear Fusion (publishedVersion)
- 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, disruption prediction, JET tokamak, fuzzy logic, classification and regression trees, adaptive predictor, expert knowledge
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