Latest Developments in Data Analysis Tools for Disruption Prediction and for the Exploration of Multimachine Operational Spaces. (Contributo in atti di convegno)

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  • Latest Developments in Data Analysis Tools for Disruption Prediction and for the Exploration of Multimachine Operational Spaces. (Contributo in atti di convegno) (literal)
Anno
  • 2012-01-01T00:00:00+01:00 (literal)
Alternative label
  • A. Murari; J. Vega; P. Boutot; B. Cannas; S. Dormido - Canto; A. Fanni; J. M. López; R. Moreno; A. Pau; G. Sias; J. M. Ramírez; G. Verdoolaege 7; ASDEX Upgrade Team and JET - EFDA Contributors (2012)
    Latest Developments in Data Analysis Tools for Disruption Prediction and for the Exploration of Multimachine Operational Spaces.
    in 24th IAEA Fusion Energy Conference (IAEA FEC 2012), San Diego, California, USA, October 8-13, 2012
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • A. Murari; J. Vega; P. Boutot; B. Cannas; S. Dormido - Canto; A. Fanni; J. M. López; R. Moreno; A. Pau; G. Sias; J. M. Ramírez; G. Verdoolaege 7; ASDEX Upgrade Team and JET - EFDA Contributors (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#altreInformazioni
  • P8-04. http://www-naweb.iaea.org/napc/physics/FEC/FEC2012/html/proceedings.pdf (IAEA Proceedings Series Fusion Energy Conference 2012). FUSION ENERGY CONFERENCE 2012 IAEA (January 2013) (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
  • http://www-naweb.iaea.org/napc/physics/FEC/FEC2012/papers/112_EXP804.pdf (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#titoloVolume
  • Programme, Abstracts, Presentations & Proceedings 24th IAEA Fusion Energy Conference October 8-13, 2012, San Diego, USA (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#pagineTotali
  • 8 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • JET - EFDA, Culham Science Centre, OX14 3DB, Abingdon, UK / - 1 Consorzio RFX - Associazione EURATOM/ ENEA per la Fusione, I -35127 Padova, Italy; / - 2 Asociación EURATOM/CIEMAT para Fusión. Avda. Complutense, 22. 28040 Madrid, Spain; / - 3 Ecole Polytechnique de Palaiseau, Paris, France; / - 4 Department of Electrical and Electronic Engineering - University of Cagliari, Italy; / - 5 Dpto. Informática y Automática, UNED. Madrid, Spain; / - 6 Universidad Politécnica de Madrid. CAEND UPM - CSIC. Madrid. Spain; / - 7 Department of Applied Physics, Ghent University, Ghent, Belgium; / - a See the Appendix of F. Romanelli et al., Proceedings of the 24th IAEA Fusion Energy Conference 2012, San Diego, US. (A. Murari 1; J. Vega 2; P. Boutot 3; B. Cannas 4; S. Dormido - Canto 5; A. Fanni 4; J. M. López 6; R. Moreno 2; A. Pau 4; G. Sias 4; J. M. Ramí rez 5; G. Verdoolaege 7; ASDEX Upgrade Team and JET - EFDA Contributors a) (literal)
Titolo
  • Latest Developments in Data Analysis Tools for Disruption Prediction and for the Exploration of Multimachine Operational Spaces. (literal)
Abstract
  • In the last years significant efforts have been devoted to the development of advanced data analysis tools to both predict the occurrence of disruptions and to investigate the operational spaces of devices, with the long term goal of advancing the underst anding of the physics of these events and to prepare for ITER. On JET the latest generation of the disruption predictor called APODIS has been deployed in the real time network during the last campaigns with the new metallic wall. Even if it was trained on ly with discharges with the carbon wall, it has reached very good performance, with both missed alarms and false alarms in the order of a few percent (and strategies to improve the performance have already been identified). Since for the optimisation of th e mitigation measures, predicting also the type of disruption is considered to be also very important, a new clustering method, based on the geodesic distance on a probabilistic manifold, has been developed. This technique allows automatic classification o f an incoming disruption with a success rate of better than 85%. Various other manifold learning tools, particularly Principal Component Analysis and Self Organised Maps, are also producing very interesting results in the comparative analysis of JET and AS DEX Upgrade (AUG) operational spaces, on the route to developing predictors capable of extrapolating from one device to another. (literal)
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