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

Woods, B. J. Q. ; Duarte, V. N. ; Fredrickson, E. D. ; Gorelenkov, N. N. ; Podesta, M. ; Vann, R. G. L.
Issue date: 2019
Rights:
Creative Commons Attribution 4.0 International (CC BY)
Cite as:
Woods, B. J. Q., Duarte, V. N., Fredrickson, E. D., Gorelenkov, N. N., Podesta, M., & Vann, R. G. L. (2019). Machine Learning Characterization of Alfvénic and Sub-Alfvénic Chirping and Correlation With Fast-Ion Loss at NSTX [Data set]. Princeton Plasma Physics Laboratory, Princeton University. https://doi.org/10.11578/1608254
@electronic{woods_b_j_q_2019,
  author      = {Woods, B. J. Q. and
                Duarte, V. N. and
                Fredrickson, E. D. and
                Gorelenkov, N. N. and
                Podesta, M. and
                Vann, R. G. L.},
  title       = {{Machine Learning Characterization of Alf
                vénic and Sub-Alfvénic Chirping and Corr
                elation With Fast-Ion Loss at NSTX}},
  publisher   = {{Princeton Plasma Physics Laboratory, Pri
                nceton University}},
  year        = 2019,
  url         = {https://doi.org/10.11578/1608254}
}
Description:

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.

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