Woods, Benjamin J. Q., Duarte, Vinícius N., Fredrickson, Eric D. et al. (3 more authors) (2020) Machine learning characterisation of Alfvénic and sub-Alfvénic chirping and correlation with fast ion loss at NSTX. IEEE transactions on plasma science. pp. 71-81. ISSN 1939-9375
Abstract
Abrupt large events in the Alfv\'{e}nic and sub-Alfv\'{e}nic frequency bands in tokamaks are typically correlated with increased fast ion loss. Here, machine learning is used to speed up the laborious process of characterizing the behaviour of magnetic perturbations from corresponding frequency spectrograms that are typically identified by humans. Analysis allows for comparison between different mode character (such as quiescent, fixed-frequency, chirping, avalanching) and plasma parameters obtained from the TRANSP code (such as $v_{\textrm{inj.}}/v_{\textrm{A}}$, $q$-profile, $\beta_{\textrm{inj.}}/\beta_{\textrm{A}}$). In agreement with previous work by Fredrickson \emph{et al.} [Nucl. Fusion 2014, 54 093007], we find correlation between $\beta_{\textrm{inj.}}$ and mode character. In addition, previously unknown correlations are found between moments of the spectrograms and mode character. Character transition from quiescent to non-quiescent behaviour for magnetic fluctuations in the 50 - 200 kHz frequency band is observed along the boundary $v_{\varphi} \lessapprox \frac{1}{4}(v_{\textrm{inj.}} - 3v_{\textrm{A}})$ where $v_{\textrm{inj.}}$ is the neutral beam injection velocity, $v_{\varphi}$ is the rotation velocity, and $v_{\textrm{A}}$ is the Alfv\'{e}n speed.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Keywords: | physics.comp-ph,physics.plasm-ph |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Physics (York) |
Depositing User: | Pure (York) |
Date Deposited: | 16 Dec 2019 12:10 |
Last Modified: | 16 Oct 2024 15:34 |
Published Version: | https://doi.org/10.1109/TPS.2019.2960206 |
Status: | Published |
Refereed: | Yes |
Identification Number: | 10.1109/TPS.2019.2960206 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:154663 |