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

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

Journal Article · Tue Jan 21 23:00:00 EST 2020 · IEEE Transactions on Plasma Science

DOI:https://doi.org/10.1109/tps.2019.2960206· OSTI ID:1643701

^[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)

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

View Accepted Manuscript (DOE)

Research Organization:: Princeton Plasma Physics Laboratory (PPPL), Princeton, NJ (United States)

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

Grant/Contract Number:: AC02-09CH11466

OSTI ID:: 1643701

Journal Information:: IEEE Transactions on Plasma Science, Journal Name: IEEE Transactions on Plasma Science Journal Issue: 1 Vol. 48; ISSN 0093-3813; ISSN 1939-9375

Publisher:: IEEECopyright Statement

Country of Publication:: United States

Language:: English

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