Alfven eigenmode classification based on ECE diagnostics at DIII-D using deep recurrent neural networks

doi:10.1088/1741-4326/ac3be7

Alfven eigenmode classification based on ECE diagnostics at DIII-D using deep recurrent neural networks

2021-12-17

22

OA

AI

A

Azarakhsh Jalalvand *

DOI：10.1088/1741-4326/ac3be7

Original

Original request for help

Save

Abstract

En 中文

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, Alfven 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 Alfven 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.

Keywords:

DIII-D

electron cyclotron emission

Alfven eigenmodes

reservoir computing networks

plasma control

Alfven eigenmode classification based on ECE diagnostics at DIII-D using deep recurrent neural networks

Abstract

Journal

Organization