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Three-dimensional particle image velocimetry measurement through three-dimensional U-Net neural network

Cao, Lixia (曹丽霞), Hossain, MD Moinul, Li, Jian (李健), Xu, Chuanlong (许传龙) (2024) Three-dimensional particle image velocimetry measurement through three-dimensional U-Net neural network. Physics of Fluids, 36 (4). Article Number 047136. ISSN 1070-6631. (doi:10.1063/5.0205872) (KAR id:105746)

Abstract

This paper proposes a light field (LF) three-dimensional (3D) particle image velocimetry (PIV) method based on a digital refocused algorithm and 3D U-Net neural network for 3D three-component (3D-3C) velocity measurement. A digital refocused algorithm is used to generate a stack of LF-refocused images of tracer particles for establishing the 3D U-Net. The 3D U-Net is then used for the 3D particle field reconstruction. Based on a pair of 3D particle fields, the 3D-3C velocity field is obtained through a 3D cross correlation algorithm. Numerical simulations and experiments are conducted to analyze the accuracy and efficiency of the proposed method. The simulation results show that the elongation along the depth direction and the efficiency of the 3D particle field reconstruction are improved by the 3D U-Net. The 3D U-Net also provides a better correlation coefficient. The experimental results show that the reconstruction time of the proposed method is ∼220 s which is 10 times faster than the LF tomographic PIV. This further demonstrates that the proposed method improves the reconstruction efficiency without affecting the accuracy of velocity measurement.

Item Type: Article
DOI/Identification number: 10.1063/5.0205872
Subjects: Q Science
Q Science > Q Science (General)
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Funders: China Scholarship Council (https://ror.org/04atp4p48)
National Natural Science Foundation of China (https://ror.org/01h0zpd94)
Depositing User: Moinul Hossain
Date Deposited: 24 Apr 2024 14:24 UTC
Last Modified: 05 Nov 2024 13:11 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/105746 (The current URI for this page, for reference purposes)

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