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Particle image velocimetry validation for quantifying bedload movement
A Particle Image Velocimetry (PIV) technique was validated for predicting movements of bedload particles. Synthetic images, similar to those from testing a physical model, were created with different image particle densities and particle displacements. A set of surface particles were digitally moved by rotating and translating them on top of a layer of stationary particles to replicate the mobile-bed layer in the physical model. The images were processed using PIV software and performance of the software to measure these movements was evaluated. The effect of image particle density, particle displacement, and interrogation window size on the PIV software performance was investigated. Study results showed that all investigated variables had a significant effect on the predicted velocities. Guidelines are provided for application of PIV to bedload transport based on study results. Optimum bedload velocity predictions were found with the combination of highest particle density and highest particle displacements.
Particle image velocimetry validation for quantifying bedload movement
A Particle Image Velocimetry (PIV) technique was validated for predicting movements of bedload particles. Synthetic images, similar to those from testing a physical model, were created with different image particle densities and particle displacements. A set of surface particles were digitally moved by rotating and translating them on top of a layer of stationary particles to replicate the mobile-bed layer in the physical model. The images were processed using PIV software and performance of the software to measure these movements was evaluated. The effect of image particle density, particle displacement, and interrogation window size on the PIV software performance was investigated. Study results showed that all investigated variables had a significant effect on the predicted velocities. Guidelines are provided for application of PIV to bedload transport based on study results. Optimum bedload velocity predictions were found with the combination of highest particle density and highest particle displacements.
Particle image velocimetry validation for quantifying bedload movement
Mustafa, Muhammed T. (author) / Cox, Amanda L. (author) / Mitchell, Kyle (author)
Journal of Applied Water Engineering and Research ; 7 ; 263-272
2019-10-02
10 pages
Article (Journal)
Electronic Resource
Unknown
Formula for computation of bedload movement
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