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  On-line prediction and mitigation of disruptions in ASDEX Upgrade

Pautasso, G., Tichmann, C., Egorov, S., Zehetbauer, T., Gruber, O., Maraschek, M., et al. (2002). On-line prediction and mitigation of disruptions in ASDEX Upgrade. Nuclear Fusion, 42(1), 100-108.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0027-4160-C Version Permalink: http://hdl.handle.net/11858/00-001M-0000-0027-4161-A
Genre: Journal Article

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 Creators:
Pautasso, G.1, Author              
Tichmann, C.1, Author              
Egorov, S.2, Author
Zehetbauer, T.3, Author              
Gruber, O.4, Author              
Maraschek, M.3, Author              
Mast, K. F.5, Author              
Mertens, V.6, Author              
Perchermeier, I.7, Author              
Raupp, G.3, Author              
Treutterer, W.6, Author              
Windsor, C. G.2, Author
ASDEX Upgrade Team2, Author
Affiliations:
1Tokamak Theory (TOK), Max Planck Institute for Plasma Physics, Max Planck Society, ou_1856309              
2Tech Univ St Petersburg, St Petersburg, Russia; UKAEA Euratom Fus Assoc, Abingdon, Oxon, England, ou_persistent22              
3Experimental Plasma Physics 2 (E2), Max Planck Institute for Plasma Physics, Max Planck Society, ou_1856292              
4Tokamak Theory (TOK), Max Planck Institute for Plasma Physics, Max Planck Society
5External Organizations, ou_persistent22              
6Experimental Plasma Physics 1 (E1), Max Planck Institute for Plasma Physics, Max Planck Society, ou_1856295              
7Max Planck Institute for Plasma Physics, Max Planck Society, ou_1856284              

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 Abstract: An on-line predictor of the time to disruption has been installed on the ASDEX Upgrade tokamak. It is suitable either for avoidance of disruptions or for mitigation of those that are unavoidable. The prediction uses a neural network trained on eight plasma parameters and their time derivatives extracted from 99 disruptive discharges. The network was tested off-line over 500 discharges and was found to work reliably and to be able to predict the majority of the disruptions. The trained network was installed on-line, tested over 128 discharges and used to inject killer pellets to mitigate the disruption loads.

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Language(s): eng - English
 Dates: 2002-01
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: eDoc: 21123
ISI: 000175213800014
 Degree: -

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Title: Nuclear Fusion
  Alternative Title : Nucl. Fusion
Source Genre: Journal
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Publ. Info: -
Pages: - Volume / Issue: 42 (1) Sequence Number: - Start / End Page: 100 - 108 Identifier: ISSN: 0029-5515