Integrated deep learning framework for unstable event identification and disruption prediction of tokamak plasmas. (1st March 2023)

• Record Type: Journal Article
• Authors: Zhu, J.X.; Rea, C.; Granetz, R.S.; Marmar, E.S.; Sweeney, R.; Montes, K.; Tinguely, R.A.
• Is Part Of: Nuclear fusion. Volume 63:Number 4(2023)
• Publication Date: 2023-03-01
• Subjects: tokamak -- disruption -- disruption prediction -- disruption avoidance -- machine learning -- plasma instabilities; Nuclear fusion -- Periodicals; 621.48405
• DOI: 10.1088/1741-4326/acb803
• Languages: English
• ISSNs: 0029-5515

Abstract

Abstract: The ability to identify underlying disruption precursors is key to disruption avoidance. In this paper, we present an integrated deep learning (DL) based model that combines disruption prediction with the identification of several disruption precursors like rotating modes, locked modes, H-to-L back transitions and radiative collapses. The first part of our study demonstrates that the DL-based unstable event identifier trained on 160 manually labeled DIII-D shots can achieve, on average, 84% event identification rate of various frequent unstable events (like H-L back transition, locked mode, radiative collapse, rotating MHD mode, large sawtooth crash), and the trained identifier can be adapted to label unseen discharges, thus expanding the original manually labeled database. Based on these results, the integrated DL-based framework is developed using a combined database of manually labeled and automatically labeled DIII-D data, and it shows state-of-the-art (AUC = 0.940) disruption prediction and event identification abilities on DIII-D. Through cross-machine numerical disruption prediction studies using this new integrated model and leveraging the C-Mod, DIII-D, and EAST disruption warning databases, we demonstrate the improved cross-machine disruption prediction ability and extended warning time of the new model compared with a baseline predictor. In addition, the trained integrated model shows qualitatively good cross-machine event identification ability. Given a labeled dataset, the strategy presented in this paper, i.e. one that combines a disruption predictor with an event identifier module, can be applied to upgrade any neural network based disruption predictor. The results presented here inform possible development strategies of machine learning based disruption avoidance algorithms for future tokamaks and highlight the importance of building comprehensive databases with unstable event information on current machines.