Helium line-ratios for electron temperature (Te) and density (ne) plasma diagnostic
in the Scrape-Off-Layer (SOL) and Edge regions of tokamaks are widely used.
Due to their intensities and proximity of wavelengths, the singlet 667.8 and 728.1
nm, and triplet 706.5 nm visible lines have been typically preferred. Time-
dependency of the triplet line (706.5 nm) has been previously analyzed in detail by
including transient effects on line-ratios during gas-puff diagnostic applications. In this work, several line-ratio combinations within each of the two spin systems are
analyzed with the purpose of eliminating transient effects to extend the application
of this powerful diagnostic to high temporal resolution characterization of
plasmas. The analysis is done using synthetic emission modeling and diagnostic
for low electron density NSTX SOL plasma conditions for several visible lines.
This analysis employs both quasi-static equilibrium and time-dependent models in
order to evaluate transient effects of the atomic population levels that may affect
the derived electron temperatures and densities as a helium gas-puff penetrates the
plasma. Ratios between the most intense lines are usually preferred due to their
higher signal to noise ratio. The analysis of a wider range of spectral lines will
help to extend this powerful diagnostic to experiments where the wavelength
range of the measured spectra may be constrained either by limitations of the
spectrometer, or by other conflicting lines from different ions.
We implement unsupervised machine learning techniques to identify characteristic evolution patterns and associated parameter regimes in edge localized mode (ELM) events observed on the National Spherical Torus Experiment. Multi-channel, localized measurements spanning the pedestal region capture the complex evolution patterns of ELM events on Alfven timescales. Some ELM events are active for less than 100~microsec, but others persist for up to 1~ms. Also, some ELM events exhibit a single dominant perturbation, but others are oscillatory. Clustering calculations with time-series similarity metrics indicate the ELM database contains at least two and possibly three groups of ELMs with similar evolution patterns. The identified ELM groups trigger similar stored energy loss, but the groups occupy distinct parameter regimes for ELM-relevant quantities like plasma current, triangularity, and pedestal height. Notably, the pedestal electron pressure gradient is not an effective parameter for distinguishing the ELM groups, but the ELM groups segregate in terms of electron density gradient and electron temperature gradient. The ELM evolution patterns and corresponding parameter regimes can shape the formulation or validation of nonlinear ELM models. Finally, the techniques and results demonstrate an application of unsupervised machine learning at a data-rich fusion facility.
Stotler, D.; F. Scotti; R.E. Bell; A. Diallo; B.P. LeBlanc; M. Podesta; A.L. Roquemore; P.W. Ross
Atomic and molecular density data in the outer midplane of NSTX [Ono et al., Nucl. Fusion 40, 557 (2000)] are inferred from tangential camera data via a forward modeling procedure using the DEGAS 2 Monte Carlo neutral transport code. The observed Balmer-b light emission data from 17 shots during the 2010 NSTX campaign display no obvious trends with discharge parameters such as the divertor Balmer-a emission level or edge deuterium ion density. Simulations of 12 time slices in 7 of these discharges produce molecular densities near the vacuum vessel wall of 2–8 10^17 m3 and atomic densities ranging from 1 to 7 10^16 m3; neither has a clear correlation with other parameters. Validation of the technique, begun in an earlier publication, is continued with an assessment of the sensitivity of the simulated camera image and neutral densities to uncertainties in the data input to the model. The simulated camera image is sensitive to the plasma profiles and virtually nothing else. The neutral densities at the vessel wall depend most strongly on the spatial distribution of the source; simulations with a localized neutral source yield densities within a factor of two of the baseline, uniform source, case. The uncertainties in the neutral densities associated with other model inputs and assumptions are 50%.