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Title: Machine Learning Characterization of Alfvénic and Sub-Alfvénic Chirping and Correlation With Fast-Ion Loss at NSTX

Abstract

Abrupt large events in the Alfvenic and sub-Alfvenic 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, fixedfrequency, chirping, avalanching) and plasma parameters obtained from the TRANSP code such as the ratio of the neutral beam injection (NBI) velocity and the Alfven velocity (vinj./vA), the q-profile, and the ratio of the neutral beam beta and the total plasma beta (βbeam,i/β). In agreement with previous work by Fredrickson et al. [Nucl. Fusion 2014, 54 093007], we find correlation between βbeam,i 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 vΦ ≲ (1/4) (vinj. - 3vA) where vΦ is the rotation

Authors:
ORCiD logo [1];  [2];  [2];  [2];  [2]; ORCiD logo [3]
  1. Univ. of Leeds, Leeds (United Kingdom); Univ. of York (United Kingdom)
  2. Princeton Plasma Physics Lab. (PPPL), Princeton, NJ (United States)
  3. Univ. of York (United Kingdom)
Publication Date:
Research Org.:
Princeton Plasma Physics Lab. (PPPL), Princeton, NJ (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1643701
Grant/Contract Number:  
AC02-09CH11466
Resource Type:
Accepted Manuscript
Journal Name:
IEEE Transactions on Plasma Science
Additional Journal Information:
Journal Volume: 48; Journal Issue: 1; Journal ID: ISSN 0093-3813
Publisher:
IEEE
Country of Publication:
United States
Language:
English
Subject:
70 PLASMA PHYSICS AND FUSION TECHNOLOGY

Citation Formats

Woods, Benjamin J. Q., Duarte, Vinicius N., Fredrickson, Eric D., Gorelenkov, Nikolai N., Podesta, Mario, and Vann, Roddy G. L. Machine Learning Characterization of Alfvénic and Sub-Alfvénic Chirping and Correlation With Fast-Ion Loss at NSTX. United States: N. p., 2020. Web. https://doi.org/10.1109/tps.2019.2960206.
Woods, Benjamin J. Q., Duarte, Vinicius N., Fredrickson, Eric D., Gorelenkov, Nikolai N., Podesta, Mario, & Vann, Roddy G. L. Machine Learning Characterization of Alfvénic and Sub-Alfvénic Chirping and Correlation With Fast-Ion Loss at NSTX. United States. https://doi.org/10.1109/tps.2019.2960206
Woods, Benjamin J. Q., Duarte, Vinicius N., Fredrickson, Eric D., Gorelenkov, Nikolai N., Podesta, Mario, and Vann, Roddy G. L. Wed . "Machine Learning Characterization of Alfvénic and Sub-Alfvénic Chirping and Correlation With Fast-Ion Loss at NSTX". United States. https://doi.org/10.1109/tps.2019.2960206. https://www.osti.gov/servlets/purl/1643701.
@article{osti_1643701,
title = {Machine Learning Characterization of Alfvénic and Sub-Alfvénic Chirping and Correlation With Fast-Ion Loss at NSTX},
author = {Woods, Benjamin J. Q. and Duarte, Vinicius N. and Fredrickson, Eric D. and Gorelenkov, Nikolai N. and Podesta, Mario and Vann, Roddy G. L.},
abstractNote = {Abrupt large events in the Alfvenic and sub-Alfvenic 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, fixedfrequency, chirping, avalanching) and plasma parameters obtained from the TRANSP code such as the ratio of the neutral beam injection (NBI) velocity and the Alfven velocity (vinj./vA), the q-profile, and the ratio of the neutral beam beta and the total plasma beta (βbeam,i/β). In agreement with previous work by Fredrickson et al. [Nucl. Fusion 2014, 54 093007], we find correlation between βbeam,i 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 vΦ ≲ (1/4) (vinj. - 3vA) where vΦ is the rotation},
doi = {10.1109/tps.2019.2960206},
journal = {IEEE Transactions on Plasma Science},
number = 1,
volume = 48,
place = {United States},
year = {2020},
month = {1}
}

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