U.S. Department of EnergyOffice of Scientific and Technical Information
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 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, and 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 (beta_beam,i / beta). In agreement with the previous work by Fredrickson et al., we find a correlation between beta_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 50200-kHz frequency band is observed along the boundary v_phi ~ (1/4)(v_inj. - 3v_A), where v_phi is the rotation velocity.
- Authors:
-
- Princeton University (PPPL)
- Publication Date:
- DOE Contract Number:
- AC02-09CH11466
- Research Org.:
- Princeton Plasma Physics Laboratory (PPPL), Princeton, NJ (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC)
- Subject:
- machine learning; plasma physics; tokamak physics
- OSTI Identifier:
- 1814933
- DOI:
- https://doi.org/10.11578/1814933
Citation Formats
Woods, B J Q, Duarte, V N, Fredrickson, E D, Gorelenkov, N N, Podestà, M, and Vann, R 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:10.11578/1814933.
Woods, B J Q, Duarte, V N, Fredrickson, E D, Gorelenkov, N N, Podestà, M, & Vann, R G L. Machine Learning Characterization of Alfvénic and Sub-Alfvénic Chirping and Correlation With Fast-Ion Loss at NSTX. United States. doi:https://doi.org/10.11578/1814933
Woods, B J Q, Duarte, V N, Fredrickson, E D, Gorelenkov, N N, Podestà, M, and Vann, R G L. 2020.
"Machine Learning Characterization of Alfvénic and Sub-Alfvénic Chirping and Correlation With Fast-Ion Loss at NSTX". United States. doi:https://doi.org/10.11578/1814933. https://www.osti.gov/servlets/purl/1814933. Pub date:Wed Jan 01 23:00:00 EST 2020
@article{osti_1814933,
title = {Machine Learning Characterization of Alfvénic and Sub-Alfvénic Chirping and Correlation With Fast-Ion Loss at NSTX},
author = {Woods, B J Q and Duarte, V N and Fredrickson, E D and Gorelenkov, N N and Podestà, M and Vann, R 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 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, and 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 (beta_beam,i / beta). In agreement with the previous work by Fredrickson et al., we find a correlation between beta_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 50200-kHz frequency band is observed along the boundary v_phi ~ (1/4)(v_inj. - 3v_A), where v_phi is the rotation velocity.},
doi = {10.11578/1814933},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Wed Jan 01 23:00:00 EST 2020},
month = {Wed Jan 01 23:00:00 EST 2020}
}