Journal
SCIENTIFIC REPORTS
Volume 12, Issue 1, Pages -Publisher
NATURE PORTFOLIO
DOI: 10.1038/s41598-022-21906-w
Keywords
-
Categories
Funding
- U.S. Department of Energy [DE-AC05-00OR22725]
- Shull Wollan Center Graduate Research Fellowship program
- Graduate Advancement, Training and Education program of University of Tennessee
- National Science Foundation [DMR-1644779]
- National High Magnetic Field Laboratory - National Science Foundation [DMR-1644779]
- state of Florida
Ask authors/readers for more resources
Time dependent observations of point-to-point correlations of the velocity vector field are important for modeling and understanding fluid flow around complex objects. This study used thermal gradients to observe fluid flow through fluorescence recording, and applied unsupervised machine learning algorithms and particle displacement determination algorithms to analyze the recorded fluorescence.
Time dependent observations of point-to-point correlations of the velocity vector field (structure functions) are necessary to model and understand fluid flow around complex objects. Using thermal gradients, we observed fluid flow by recording fluorescence of He-2* excimers produced by neutron capture throughout a similar to cm(3) volume. Because the photon emitted by an excited excimer is unlikely to be recorded by the camera, the techniques of particle tracking (PTV) and particle imaging (PIV) velocimetry cannot be applied to extract information from the fluorescence of individual excimers. Therefore, we applied an unsupervised machine learning algorithm to identify light from ensembles of excimers (clusters) and then tracked the centroids of the clusters using a particle displacement determination algorithm developed for PTV.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available