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.
All Science Journal Classification (ASJC) codes
- Civil and Structural Engineering
- Materials Science(all)
- Nuclear Energy and Engineering
- Mechanical Engineering
Cannas, B., Fanni, A., Pautasso, G., Sias, G., & Sonato, P. (2009). Criteria and algorithms for constructing reliable databases for statistical analysis of disruptions at ASDEX Upgrade. Fusion Engineering and Design, 84(2-6), 534 - 539. https://doi.org/10.1016/j.fusengdes.2008.12.036