摘要
A spatial pyramidal cross-correlation based on interrogation area sub-division is introduced to improve the measurement resolution in particle image velocimetry(PIV). The high-resolution velocity can be achieved with a velocity prediction model via coarse cross-correlation. The prediction formula is deduced from the frequency response of the moving average(MA). The performance of this method was assessed using synthetically generated images of sinusoidal shear flow, two-dimensional vortical cellular flow, and homogeneous turbulence. A real PIV experiment of turbulent boundary layer was used to evaluate the new method. The results indicate that the spatial pyramid cross-correlation can robustly increase the spatial resolution.
A spatial pyramidal cross-correlation based on interrogation area sub-division is introduced to improve the measurement resolution in particle image velocimetry （PIV）. The high-resolution velocity can be achieved with a velocity prediction model via coarse cross-correlation. The prediction formula is deduced from the frequency response of the moving average （MA）. The performance of this method was assessed using synthetically generated images of sinusoidal shear flow, two-dimensional vortical cellular flow, and homogeneous turbulence. A real PIV experiment of turbulent boundary layer was used to evaluate the new method. The results indicate that the spatial pyramid cross-correlation can robustly increase the spatial resolution.
作者
WANG HongPing
WU Peng
GAO Qi
WANG JianJie
WANG JinJun
WANG HongPing;WU Peng;GAO Qi;WANG JianJie;WANG JinJun(Key Laboratory of Fluid Mechanics, Ministry of Education, Beihang University, Beoing 100083, China;State Key Laboratory of Nonlinear Mechanics, Institute of Mechanics, Chinese Academy of Sciences, Beoing 100190, China;Artificial Organ Technology Laboratory Biomanufacturing Centre, School of Mechanical and Electrical Engineering, Soochow University Suzhou 215006, China)
基金
supported by the National Natural Science Foundation of China(Grant Nos.11702302,51406127&11572331)
the Fundamental Research Funds for Central Universities(YWF-16-JCTD-A-05)
the Natural Science Foundation of Jiangsu Province(Grant No.BK20140344)
