Deep modeling of plasma and neutral fluctuations from gas puff turbulence imaging
Name
5.0088216.pdf
Description
Published version
Size
12.73 MB
Format
Adobe PDF
Checksum (MD5)
739c62df3211a2410f9783c33f9130c1
Author(s)
Mathews, A
Terry, JL
Baek, SG
Hughes, JW
Kuang, AQ
LaBombard, B
Miller, MA
Stotler, D
Reiter, D
Zholobenko, W
Date Issued
June 1, 2022
Journal
Review of Scientific Instruments
Publisher
AIP Publishing
Citation
Mathews, A, Terry, JL, Baek, SG, Hughes, JW, Kuang, AQ et al. 2022. "Deep modeling of plasma and neutral fluctuations from gas puff turbulence imaging." Review of Scientific Instruments, 93 (6).
Version
Final published version
Abstract
The role of turbulence in setting boundary plasma conditions is presently a key uncertainty in projecting to fusion energy reactors. To robustly diagnose edge turbulence, we develop and demonstrate a technique to translate brightness measurements of HeI line radiation into local plasma fluctuations via a novel integrated deep learning framework that combines neutral transport physics and collisional radiative theory for the 33 D − 23 P transition in atomic helium with unbounded correlation constraints between the electron density and temperature. The tenets for experimental validity are reviewed, illustrating that this turbulence analysis for ionized gases is transferable to both magnetized and unmagnetized environments with arbitrary geometries. Based on fast camera data on the Alcator C-Mod tokamak, we present the first two-dimensional time-dependent experimental measurements of the turbulent electron density, electron temperature, and neutral density, revealing shadowing effects in a fusion plasma using a single spectral line.
MIT Department
Massachusetts Institute of Technology. Plasma Science and Fusion Center
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1063/5.0088216
