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Journal Article

On-line prediction and mitigation of disruptions in ASDEX Upgrade

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Pautasso,  G.
Tokamak Theory (TOK), Max Planck Institute for Plasma Physics, Max Planck Society;

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Tichmann,  C.
Tokamak Theory (TOK), Max Planck Institute for Plasma Physics, Max Planck Society;

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Zehetbauer,  T.
Experimental Plasma Physics 2 (E2), Max Planck Institute for Plasma Physics, Max Planck Society;

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Gruber,  O.
Tokamak Theory (TOK), Max Planck Institute for Plasma Physics, Max Planck Society;

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Maraschek,  M.
Experimental Plasma Physics 2 (E2), Max Planck Institute for Plasma Physics, Max Planck Society;

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Mertens,  V.
Experimental Plasma Physics 1 (E1), Max Planck Institute for Plasma Physics, Max Planck Society;

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Perchermeier,  I.
Max Planck Institute for Plasma Physics, Max Planck Society;

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Raupp,  G.
Experimental Plasma Physics 2 (E2), Max Planck Institute for Plasma Physics, Max Planck Society;

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Treutterer,  W.
Experimental Plasma Physics 1 (E1), Max Planck Institute for Plasma Physics, Max Planck Society;

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Citation

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.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0027-4160-C
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.