Woods, BJQ orcid.org/0000-0003-2628-3873, Duarte, VN, Fredrickson, ED 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, 48 (1). pp. 71-81. ISSN 0093-3813
Abstract
Abrupt large events in the Alfvénic and subAlfvénic 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 behavior of magnetic perturbations from corresponding frequency spectrograms that are typically identified by humans. The analysis allows for comparison between different mode character (such as quiescent, fixed frequency, chirping, and avalanching) and plasma parameters obtained from the TRANSP code, such as the ratio of the neutral beam injection (NBI) velocity and the Alfvén velocity (v inj ./v A ), the q-profile, and the ratio of the neutral beam beta and the total plasma beta (ß beam,i /ß). In agreement with the previous work by Fredrickson et al., we find a correlation between ß beam,i and mode character. In addition, previously unknown correlations are found between moments of the spectrograms and mode character. Character transition from quiescent to nonquiescent behavior for magnetic fluctuations in the 50-200 kHz frequency band is observed along the boundary v φ ≲(1/4)(v inj . - 3v A ), where v φ is the rotation velocity.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Keywords: | Machine learning (ML), plasma physics, tokamak physics. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mathematics (Leeds) > Statistics (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 28 Jan 2020 13:27 |
Last Modified: | 21 Feb 2020 10:52 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/TPS.2019.2960206 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:156134 |