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

Azarakhsh Jalalvand

doi:10.1088/1741-4326/ac3be7

2021-12-17

Azarakhsh Jalalvand *

DOI：10.1088/1741-4326/ac3be7

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摘要

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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.

Keyword:

DIII-D

electron cyclotron emission

Alfven eigenmodes

reservoir computing networks

plasma control

期刊

Nuclear Fusion

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论文数:

9.4K

被引数:

2.2W

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Ghent University

学者数:

5.2W

论文数: 4.5W

被引数: 5.5W

Princeton University

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2.1W

论文数: 2.3W

被引数: 5.1W

University of Washington

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论文数: 7.0W

被引数: 12.5W

Princeton Plasma Physics Laboratory

学者数:

919

论文数: 646

被引数: 1.1K

united states department of energy (doe)

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11.3W

论文数: 9.6W

被引数: 246

University of California System

学者数:

37.7W

论文数: 33.8W

被引数: 6.6K

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