Abstract
This paper describes OpenOpticalFlow_PIV, an open source Matlab program integrating the optical flow method with the cross-correlation method for extraction of high-resolution velocity fields from particle images with large displacements. This hybrid method provides an additional tool to process PIV images, which combines the advantages of the optical flow method and cross-correlation method and overcomes the intrinsic issues of the two methods. The principles of the hybrid method are concisely described, including the cross-correlation method for initial coarse-grained estimation and the optical flow method for refined high-resolution estimation. This paper gives more detailed descriptions of the main program, relevant subroutines and selection of the relevant parameters for computation. Examples are given to demonstrate applications of the hybrid method.
Highlights
Particle image velocimetry (PIV) is a standard technique for global velocity measurements [1, 2]
The optical flow method has been applied to PIV images to obtain velocity fields theoretically at a spatial resolution of one vector per a pixel [6,7,8,9,10,11,12,13,14]
The optical flow method is a differential approach based on computations of the time derivative and the spatial gradient of image intensity fields, requiring that displacements should be smaller than the characteristic length scale of tractable features in the image plane
Summary
Particle image velocimetry (PIV) is a standard technique for global velocity measurements [1, 2]. The optical flow method has been applied to PIV images to obtain velocity fields theoretically at a spatial resolution of one vector per a pixel [6,7,8,9,10,11,12,13,14]. The accuracy of the optical flow method for PIV depends on the four parameters: particle displacement, particle velocity gradient, particle image density, and particle image diameter. The error decrease with increasing the particle image density Np. But when Np becomes very large, the PIV images become more uniform, and extraction of the optical flow is not accurate due to the very small intensity gradient. Gaussian filter size Gaussian filter size scale factor for downsampling of original images number of iterations left and upper edge width indicator for regional diagnostics (0 or 1)
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