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Determining the prediction limits of models and classifiers with applications for disruption prediction in JET
- Source :
- Nuclear Fusion, Nuclear fusion 57 (2017). doi:10.1088/0029-5515/57/1/016024, info:cnr-pdr/source/autori:Murari A.; Peluso E.; Vega J.; Gelfusa M.; Lungaroni M.; Gaudio P.; Martinez F. J./titolo:Determining the prediction limits of models and classifiers with applications for disruption prediction in JET/doi:10.1088%2F0029-5515%2F57%2F1%2F016024/rivista:Nuclear fusion/anno:2017/pagina_da:/pagina_a:/intervallo_pagine:/volume:57
- Publication Year :
- 2017
-
Abstract
- Understanding the many aspects of tokamak physics requires the development of quite sophisticated models. Moreover, in the operation of the devices, prediction of the future evolution of discharges can be of crucial importance, particularly in the case of the prediction of disruptions, which can cause serious damage to various parts of the machine. The determination of the limits of predictability is therefore an important issue for modelling, classifying and forecasting. In all these cases, once a certain level of performance has been reached, the question typically arises as to whether all the information available in the data has been exploited, or whether there are still margins for improvement of the tools being developed. In this paper, a theoretical information approach is proposed to address this issue. The excellent properties of the developed indicator, called the prediction factor (PF), have been proved with the help of a series of numerical tests. Its application to some typical behaviour relating to macroscopic instabilities in tokamaks has shown very positive results. The prediction factor has also been used to assess the performance of disruption predictors running in real time in the JET system, including the one systematically deployed in the feedback loop for mitigation purposes. The main conclusion is that the most advanced predictors basically exploit all the information contained in the locked mode signal on which they are based. Therefore, qualitative improvements in disruption prediction performance in JET would need the processing of additional signals, probably profiles.
- Subjects :
- Nuclear and High Energy Physics
Jet (fluid)
Exploit
ELMs
Mode (statistics)
Feedback loop
disruptions
Condensed Matter Physics
01 natural sciences
Signal on
Settore FIS/04 - Fisica Nucleare e Subnucleare
010305 fluids & plasmas
Reliability engineering
conditional entropy
prediction factor
predictability
0103 physical sciences
Numerical tests
Predictability
010306 general physics
sawteeth
Subjects
Details
- ISSN :
- 00295515, 07413335, and 09570233
- Database :
- OpenAIRE
- Journal :
- Nuclear Fusion
- Accession number :
- edsair.doi.dedup.....0b10822aba2bf3359ec8ab110aae1965
- Full Text :
- https://doi.org/10.1088/0029-5515/57/1/016024