摘要
粒子图像测速技术(particle image velocimetry,PIV)中采用的互相关算法就是需要从独立存在的两幅图像通过一定的判别方法得到流场中各点的流速矢量的计算方法.互相关算法的具体实现步骤包括图像前处理、区域离散、匹配原则选取、搜索方法选取和变形预测,最后对结果进行后处理.文中从上述几个方面总结了国内外近年来互相关算法的发展过程,并通过对各种方法精度和效率的比较对其应用发展进行了归纳.
Cross correlation algorithms in particle image velocimetry （PIV） are used to get the full field velocity information of the flow field from the two PIV images. Such an algorithm can be implemented in the following stages： image pre-processing, area discretization, block matching, searching programs setting, image deformation predicting and data post-processing, which are detailed in this paper with related researches during the recent years being reviewed. The spatial resolution and the efficiency of the different algorithms are discussed and compared with each other.
出处
《力学进展》
EI
CSCD
北大核心
2007年第3期443-452,共10页
Advances in Mechanics
基金
国家自然科学基金重点项目(50538050)
国家高技术研究发展863计划项目(2006AA11Z108)
国家自然科学基金项目(50608059)联合资助~~
关键词
PIV
互相关算法
区域离散
匹配原则
变形预测
PIV, cross correlation algorithm, block matching, searching method, image deformation predicting
作者简介
E-mail：samch@mail．tongji．edu．cn。
