Microtearding mode study in NSTX using machine learning enhanced reduced model
Abstract
This article presents a survey of NSTX cases to study the microtearing mode (MTM) stabilities using the newly developed global reduced model for Slab-Like Microtearing modes (SLiM). A trained neutral network version of SLiM enables rapid assessment (0.05s/mode) of MTM with accuracy providing an opportunity for systemic equilibrium reconstructions based on the matching of experimentally observed frequency bands and SLiM prediction across a wide range of parameters. Such a method finds some success in the NSTX discharges, the frequency observed in the experiment matches with what SLiM predicted. Based on the experience with SLiM analysis, a workflow to estimate the potential MTM frequency for a quick assessment based on experimental observation has been established.
Keywords
Cite
@article{arxiv.2304.08982,
title = {Microtearding mode study in NSTX using machine learning enhanced reduced model},
author = {Max T. Curie and Joel Larakers and Jason Parisi and Gary Staebler and Stefano Munaretto and Walter Guttenfelder and Emily Belli and David R. Hatch and Mate Lampert and Galina Avdeeva and Tom Neiser and Sterling Smith and Ahmed Diallo and Oak Nelson and Stanley Kaye and Eric Fredrickson and Joshua M Manela and Shelly Lei and Michael Halfmoon and Matthew M Tennery and Ehab Hassan},
journal= {arXiv preprint arXiv:2304.08982},
year = {2023}
}