题目：Optical Flow and Hybrid Particle Image Velocimetry

时间：2019年8月12日 14:00-16:00

地点：80足球直播吧 F210会议室

邀请人：李磊 副教授（内燃机研究所）

Biography

Dr. Zifeng Yang, is an Associate Professor in the department of Mechanical and Materials Engineering at Wright State University. His research interests are advanced flow diagnostic techniques for complex flows. Dr. Yang received his Ph.D. in Aerospace Engineering from Iowa State University (ISU). His doctoral thesis was focused on the experimental investigations on complex vortex flows using advanced flow diagnostic techniques. With technical expertise on many flow diagnostic techniques, including Particle Image Velocimetry (PIV), Stereo-PIV, Optical Flow Method (OFM), Pressure Sensitive Paint and Temperature Sensitive Paint etc. Dr. Yang has been working in flow measurements, advanced flow diagnostics techniques for more than 13 years. Prior to joining WSU, Dr. Yang was a postdoctoral research associate at ISU. Some of his research on interference of wind turbine wakes over complex terrain, design and measurement on the film cooling over the gas turbine blade was supported by the National Science Foundation (NSF) awards and GE Research Grants. In 2011 he joined Wright State University and established Experimental Fluid Dynamics Laboratory. His research has been supported by National Health Institute, Airforce Research Program, RMD etc. He has published 26 peer-reviewed journal papers and numerous conference papers, including Experiments in Fluids, AIAA Journal of Propulsion and Power, Journal of Aircraft, Applied Optics, Journal of Visualization, Journal of Fluids and Structure, Annals of Biomedical Engineering etc.

Abstract

A divergence compensatory optical flow method (DC-OFM), in which a nonzero divergence of velocity is introduced due to the finite resolution of the image, was explored and applied to the digital subtraction angiography (DSA) images of blood flow. The objective of this study is to examine the applicability and evaluate the accuracy of DC-OFM in assessing the blood flow velocity in vessels. First, an Oseen vortex flow was simulated on the standard particle image to generate an image pair. Then, the DC-OFM was applied on the particle image pair to recover the velocity field for validation. Second, DSA images of intracranial arteries were used to examine the accuracy of the current method. For each set of images, the first image is the in vivo DSA image, and the second image is generated by superimposing a given flow field. The recovered velocity map by DC-OFM agrees well with the exact velocity for both the particle images and the angiographic images. In comparison with the traditional OFM, the present method can provide more accurate velocity estimation. The accuracy of the velocity estimation can also be improved by implementing preprocess techniques including image intensification, Gaussian filtering, and “image-shift.”

Through a combination of cross-correlation and optical flow method (OFM), a novel technique can benefit from the strengths of each method while mitigating the flaws each individual method contains. The hybrid Particle Image Velocimetry (PIV) method utilizes the state-of-the-art cross-correlation method to account for the relatively large displacements of particles and refine the flow field using the high-resolution analysis of OFM. Image processing techniques such as interpolation, image shifting, and Gaussian filtering are crucial for integrating the cross-correlation technique with optical flow analysis. The accuracy of the hybrid PIV method was validated using standard simulated PIV images that encompassed various parameters encountered in PIV measurements. Each set of images was analyzed by the hybrid method and three other widely used correlation techniques to verify the accuracy. Results confirmed that the hybrid method is consistently more accurate than the other methods in generating the flow vectors, especially near the boundaries. Additionally, for cases dealing with large-sized particles or small displacements, the hybrid PIV method also attains more accurate results.