Integrating cross-correlation and relaxation algorithms for particle tracking velocimetry
Integrating cross-correlation and relaxation algorithms for particle tracking velocimetry
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DOI：
10.1007/s00348-010-0907-z
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发表时间：
2011-01-01
影响因子：
2.4
通讯作者：
Jirka, G. H.
中科院分区：
文献类型：
--
作者：
Brevis, W.;Nino, Y.;Jirka, G. H.
本文提出了一种用于粒子跟踪测速的互相关/松弛综合算法。这种集成的目的是提供一种灵活的方法，能够分析不同的播种和流动条件下的图像。该方法是基于两个匹配方法的个人性能的改善，通过结合它们的特点，在一个两阶段的过程。类似于混合粒子图像测速方法，组合算法开始于通过互相关算法获得的解决方案，该解决方案通过在互相关方法显示出低可靠性的区域中应用松弛算法来进一步细化。利用合成图像和大规模实验图像，对互相关法、松弛法和综合互相关/松弛法三种算法的性能进行了比较和分析。结果表明，在高速度梯度和异构播种的情况下，集成算法提高了其所基于的各个算法的整体性能，在有效恢复向量的数量方面，对各个控制参数的敏感性较低。
An integrated cross-correlation/relaxation algorithm for particle tracking velocimetry is presented. The aim of this integration is to provide a flexible methodology able to analyze images with different seeding and flow conditions. The method is based on the improvement of the individual performance of both matching methods by combining their characteristics in a two-stage process. Analogous to the hybrid particle image velocimetry method, the combined algorithm starts with a solution obtained by the cross-correlation algorithm, which is further refined by the application of the relaxation algorithm in the zones where the cross-correlation method shows low reliability. The performance of the three algorithms, cross-correlation, relaxation method and the integrated cross-correlation/relaxation algorithm, is compared and analyzed using synthetic and large-scale experimental images. The results show that in case of high velocity gradients and heterogeneous seeding, the integrated algorithm improves the overall performance of the individual algorithms on which it is based, in terms of number of valid recovered vectors, with a lower sensitivity to the individual control parameters.