Spatial pyramidal cross correlation for particle image velocimetry

作     者：WANG HongPing WU Peng GAO Qi WANG JianJie WANG JinJun WANG HongPing;WU Peng;GAO Qi;WANG JianJie;WANG JinJun

作者机构：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 UniversitySuzhou 215006 China 

出 版 物：《Science China(Technological Sciences)》 (中国科学（技术科学英文版）)

年 卷 期：2018年第61卷第6期

页      面：867-878页

核心收录：

学科分类：080704[工学-流体机械及工程] 080103[工学-流体力学] 08[工学] 0807[工学-动力工程及工程热物理] 0801[工学-力学（可授工学、理学学位）] 

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) 

主　　题：particle image velocimetry (PIV) cross correlation spatial resolution moving average 

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.