Alfvén eigenmode classification based on ECE diagnostics at DIII-D using deep recurrent neural networks. (17th December 2021)

• Record Type: Journal Article
• Authors: Jalalvand, Azarakhsh; Kaptanoglu, Alan A.; Garcia, Alvin V.; Nelson, Andrew O.; Abbate, Joseph; Austin, Max E.; Verdoolaege, Geert; Brunton, Steven L.; Heidbrink, William W.; Kolemen, Egemen
• Is Part Of: Nuclear fusion. Volume 62:Number 2(2022)
• Publication Date: 2021-12-17
• Subjects: DIII-D -- electron cyclotron emission -- Alfvén eigenmodes -- reservoir computing networks -- plasma control; Nuclear fusion -- Periodicals; 621.48405
• DOI: 10.1088/1741-4326/ac3be7
• Languages: English
• ISSNs: 0029-5515

Abstract

Abstract: Modern tokamaks have achieved significant fusion production, but further progress towards steady-state operation has been stymied by a host of kinetic and MHD instabilities. Control and identification of these instabilities is often complicated, warranting the application of data-driven methods to complement and improve physical understanding. In particular, Alfvén eigenmodes are a class of ubiquitous mixed kinetic and MHD instabilities that are important to identify and control because they can lead to loss of confinement and potential damage to the walls of a plasma device. In the present work, we use reservoir computing networks to classify Alfvén eigenmodes in a large labeled database of DIII-D discharges, covering a broad range of operational parameter space. Despite the large parameter space, we show excellent classification and prediction performance, with an average hit rate of 91% and false alarm ratio of 7%, indicating promise for future implementation with additional diagnostic data and consolidation into a real-time control strategy.