Criteria and algorithms for constructing reliable databases for statistical analysis of disruptions at ASDEX Upgrade

B. Cannas, A. Fanni, G. Pautasso, G. Sias, P. Sonato

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

The present understanding of disruption physics has not gone so far as to provide a mathematical model describing the onset of this instability. A disruption prediction system, based on a statistical analysis of the diagnostic signals recorded during the experiments, would allow estimating the probability of a disruption to take place. A crucial point for a good design of such a prediction system is the appropriateness of the data set. This paper reports the details of the database built to train a disruption predictor based on neural networks for ASDEX Upgrade. The criteria of pulses selection, the analyses performed on plasma parameters and the implemented pre-processing algorithms, are described. As an example of application, a short description of the disruption predictor is reported. © 2008 Elsevier B.V. All rights reserved.
Original languageEnglish
Pages (from-to)534 - 539
Number of pages6
JournalFusion Engineering and Design
Volume84
Issue number2-6
DOIs
Publication statusPublished - Jun 2009
Externally publishedYes

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All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Materials Science(all)
  • Nuclear Energy and Engineering
  • Mechanical Engineering

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