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

Woods, Benjamin J. Q.; Duarte, Vinicius N.; Fredrickson, Eric D.; Gorelenkov, Nikolai N.; Podesta, Mario; Vann, Roddy G. L.

doi:10.1109/tps.2019.2960206

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

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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 (v_inj./v_A), the q-profile, and the ratio of the neutral beam beta and the total plasma beta (β_beam,i/β). In agreement with previous work by Fredrickson et al., in this work, we find correlation between β_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 non-quiescent behaviour 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

Authors:

^[1]; Duarte, Vinicius N. ^[2]; Fredrickson, Eric D. ^[2]; Gorelenkov, Nikolai N. ^[2]; Podesta, Mario ^[2];

^[3]

Univ. of Leeds, Leeds (United Kingdom); Univ. of York (United Kingdom)
Princeton Plasma Physics Lab. (PPPL), Princeton, NJ (United States)
Univ. of York (United Kingdom)

Publication Date:: 2020-01-22

Research Org.:: Princeton Plasma Physics Lab. (PPPL), Princeton, NJ (United States)

Sponsoring Org.:: USDOE; Biotechnology and Biological Sciences Research Council (BBSRC) Engineering and Physical Sciences Research Council (EPSRC); Euratom

OSTI Identifier:: 1643701

Grant/Contract Number:: AC02-09CH11466; EP/L01663X; 633053

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; 97 MATHEMATICS AND COMPUTING; machine learning (ML); plasma physics; tokamak physics

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.  doi:10.1109/tps.2019.2960206.

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                    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

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                    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.

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@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., in this work, 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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