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

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 b...

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Bibliographic Details
Published in:arXiv.org 2020-02
Main Authors: Gim, Yeonghyeon, Jang, Dong Kyu, Sohn, Dong Kee, Kim, Hyoungsoo, Han Seo Ko
Format: Article
Language:English
Subjects:
Accuracy
Binary mixtures
Computer simulation
Computing time
Liquid-vapor interfaces
Mapping
Mathematical models
Neural networks
Particle tracking
Particle tracking velocimetry
Real time
Three dimensional analysis
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Summary: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 (SNN), 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.
ISSN:2331-8422
DOI:10.48550/arxiv.2002.00517