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 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.展开更多
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)
文摘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.
