http://www.cnr.it/ontology/cnr/individuo/prodotto/ID26036

New information processing methods for control on JET (Articolo in rivista)

Type

Articolo in rivista (Classe)
Prodotto della ricerca (Classe)

Label

New information processing methods for control on JET (Articolo in rivista) (literal)

Anno

2010-01-01T00:00:00+01:00 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi

10.1016/j.fusengdes.2010.04.071 (literal)

Alternative label

Murari A.; Vega J.; Mazon D.; Rattá G. A.; Svensson J.; Vagliasindi G.; Blum J.; Boulbe C.; Faugeras B.; JET-EFDA Contributors (2010)
New information processing methods for control on JET
in Fusion engineering and design; ELSEVIER SCIENCE SA, PO BOX 564, 1001 LAUSANNE (Svizzera)
(literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori

Murari A.; Vega J.; Mazon D.; Rattá G. A.; Svensson J.; Vagliasindi G.; Blum J.; Boulbe C.; Faugeras B.; JET-EFDA Contributors (literal)

Pagina inizio

428 (literal)

Pagina fine

432 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#altreInformazioni

La rivista è pubblicata anche online con ISSN 1873-7196. (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url

http://www.sciencedirect.com/science/article/pii/S0920379610002127 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume

85 (literal)

Rivista

Fusion engineering and design (Rivista)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#note

Issues 3-4 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#pagineTotali

5 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroFascicolo

3-4 (literal)

Note

Scopu (literal)
ISI Web of Science (WOS) (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni

a Associazione EURATOM-ENEA per la Fusione, Consorzio RFX, 4-35127 Padova, Italy / - b Asociación EURATOM/CIEMAT para Fusión, Madrid, Spain / - c Association EURATOM-CEA, CEA Cadarache DSM/IRFM, 13108 Saint-Paul-lez-Durance, France / - d Dipartimento di Ingegneria Elettrica Elettronica e dei Sistemi-Università degli Studi di Catania, 95125 Catania, Italy / - e Max-Planck-Institut für Plasmaphysik, Teilinstitut Greifswald, EURATOM Association, Wendelsteinstr.1, 17491 Greifswald, Germany / - f Laboratoire J-A Dieudonné (UMR 66 21), Université de Nice Sophia-Antipolis, CNRS Parc Valrose, 06108 Nice Cedex 02 France / - 1 See the Appendix of F. Romanelli et al. Proc. 22nd IAEA Fusion Energy Conference, Geneva, 2008, Switzerland. (A. Murari a * ; J. Vega b ; D. Mazon c ; G.A. Rattá b ; J. Svensson e ; G. Vagliasindi d ; J. Blum f ; C. Boulbe f ; B. Faugeras f ; JET-EFDA Contributors 1) (literal)

Titolo

New information processing methods for control on JET (literal)

Abstract

Magnetic confinement fusion devices are very integrated systems, difficult to access for measurement, and therefore they pose particular challenges to identification and to the prediction of undesired events. With regard to system identification, Bayesian statistics is a very promising methodology, which provides for the first time a sound way to include physical information about the diagnostics in the evaluation of the error bars. In a prototypical application, a Bayesian statistical approach determines the magnetic topology without any assumption on the equilibrium and provides clear confidence intervals on all the derived quantities. This technique, which complements another more traditional approach based on the Grad-Shafranov equation, can be implemented to provide results about every few milliseconds and therefore it can be envisaged to exploit for feedback purposes. Some phenomena in the evolution of Tokamak plasmas, like disruptions, are too dangerous and prohibitively difficult to control. For these cases avoidance is the best alternative and therefore specific classifiers have been trained and optimized to predict the occurrence of disruptions. The success rates of these predictors, mainly based on Support Vector Machines, are very often of the order or higher than 90%. The generalisation capability of the method has been confirmed by applying the same predictor to new campaigns without retraining. The success rate remains very high (above 80%) even 12 campaigns after the last one used for training. (literal)

Editore