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Observing flow of He II with unsupervised machine learning.

ABSTRACT: 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 [Formula: see text] excimers produced by neutron capture throughout a ~ cm³ 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.

SUBMITTER: Wen X

PROVIDER: S-EPMC9701805 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature

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Observing flow of He II with unsupervised machine learning.

Wen X X McDonald L L Pierce J J Guo W W Fitzsimmons M R MR

Scientific reports 20221127 1

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 [Formula: see text] excimers produced by neutron capture throughout a ~ cm<sup>3</sup> 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 ...[more]

PMID: 36437248

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