Alfvén eigenmode classification based on ECE diagnostics at DIII-D using deep recurrent neural networks
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Dec 17, 2021
- Source ID
- 10.1088/1741-4326/ac3be7
Entities
People
- A. Nelson
- Alan A. Kaptanoglu
- Alvin V. Garcia
- Azarakhsh Jalalvand
- Egemen Kolemen
- Geert Verdoolaege
- Joseph Abbate
- M. E. Austin
- Steven Brunton
- W. W. Heidbrink
Organizations
- Army Research Office
- Division of Graduate Education
- Ghent University
- Office of Science