Gu, Mengtao, Li, Jian, Hossain, MD Moinul, Xu, Chuanlong (2023) High-resolution microscale velocity field measurement using light field particle image-tracking velocimetry. Physics of Fluids, 35 (11). Article Number 112006. ISSN 1070-6631. (doi:10.1063/5.0174937) (KAR id:103889)
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Official URL: https://doi.org/10.1063/5.0174937 |
Abstract
Light field microparticle image velocimetry (LF-μPIV) can realize the three-dimensional (3D) microscale velocity field measurement, but the spatial resolution of the velocity field is low. Therefore, this study proposes a high-resolution LF particle image-tracking velocimetry (PIV–PTV) in combination with a cross-validation matching (CVM) algorithm. The proposed method performs motion compensation for the distribution of particle center position based on the low-resolution velocity field achieved by PIV and then conducts the CVM on tracer particles with the nearest neighbor method. The motion compensation reduces the particle displacement during the matching, while the CVM reduces the impact of missing particles on the matching accuracy. Thus, the proposed method enables precise tracking of individual particles at higher particle concentrations and improves the spatial resolution of the velocity field. Numerical simulations were conducted on the 3D displacement field reconstruction. The influence of interrogation window size, particle diameter, and concentration was analyzed. Experiments were conducted on the microscale 3D velocity field within the microchannel with right-angle bends. Results indicate that the proposed method provides the high-resolution measurement of the microscale 3D velocity field and improves the precision of the velocity field compared to the PTV at higher particle concentrations. It demonstrates that the proposed method outperforms PIV by 26% in resolution and PTV by 76% in precision at a higher particle concentration of 1.5 particles per microlens.
Item Type: | Article |
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DOI/Identification number: | 10.1063/5.0174937 |
Subjects: |
Q Science Q Science > Q Science (General) |
Divisions: | Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts |
Funders: | National Natural Science Foundation of China (https://ror.org/01h0zpd94) |
Depositing User: | Moinul Hossain |
Date Deposited: | 11 Nov 2023 21:18 UTC |
Last Modified: | 09 Jan 2024 03:20 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/103889 (The current URI for this page, for reference purposes) |
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