Three-dimensional particle tracking velocimetry using shallow neural network for real-time analysis

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Three-dimensional particle tracking velocimetry (3D-PTV) technique is widely used to acquire the complicated trajectories of particles and flow fields. It is known that the accuracy of 3D-PTV depends on the mapping function to reconstruct three-dimensional particles locations. The mapping function becomes more complicated if the number of cameras is increased and there is a liquid-vapor interface, which crucially affect the total computation time. In this paper, using a shallow neural network model, we dramatically decrease the computation time with a high accuracy to successfully reconstruct the three-dimensional particle positions, which can be used for real-time particle detection for 3D-PTV. The developed technique is verified by numerical simulations and applied to measure a complex solutal Marangoni flow patterns inside a binary mixture droplet.Graphic abstract
Publisher
SPRINGER
Issue Date
2020-01
Language
English
Article Type
Article
Citation

EXPERIMENTS IN FLUIDS, v.61, no.2

ISSN
0723-4864
DOI
10.1007/s00348-019-2861-8
URI
http://hdl.handle.net/10203/272416
Appears in Collection
ME-Journal Papers(저널논문)
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