Determining the prediction limits of models and classifiers with applications for disruption prediction in JET. (8th November 2016)

• Record Type: Journal Article
• Authors: Murari, A.; Peluso, E.; Vega, J.; Gelfusa, M.; Lungaroni, M.; Gaudio, P.; Martínez, F. J.
• Other Names: collab.
• Is Part Of: Nuclear fusion. Volume 57:Number 1(2017:Jan.)
• Publication Date: 2016-11-08
• Subjects: prediction factor -- conditional entropy -- predictability -- disruptions -- ELMs -- sawteeth; Nuclear fusion -- Periodicals; 621.48405
• DOI: 10.1088/0029-5515/57/1/016024
• Languages: English
• ISSNs: 0029-5515

Abstract

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.