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首页> 外文期刊>Experiments in Fluids: Experimental Methods and Their Applications to Fluid Flow >Generalised cross-correlation functions for engineering applications. Application to experimental data
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Generalised cross-correlation functions for engineering applications. Application to experimental data

机译：适用于工程应用的通用互相关函数。适用于实验数据

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A recent generalisation of cross-correlation (GC-C) makes it possible to model transformations between data such as Fairs of images by sets of parameterised functions as opposed to constant shifts, rotations etc, as employed in conventional cross-correlation. Typical applications of GC-C are in areas such as particle image velocimetry (PIV), or two-dimensional or three-dimensional surface strain field determinations. As the flow, strain etc, descriptions developed by GC-C are global or zonal, the parameters required are estimated using all or a large fraction of the information in the images typically used to provide the basic data in such techniques. This is in complete contrast to traditional cross-correlation methods used in PIV, where the image domains are segmented into small sub-regions and a constant shift, rotation etc. is determined separately in each local cell. Such local cellular methods inevitably introduce a compromise between spatial. resolution and the statistical confidence that can be placed in the estimates of the shifts, rotations etc. GC-C removes the need for such compromises. This paper examines the application of the small perturbation form of GC-C to real experimental data sets with special. emphasis on showing the effects of the analytical approximations employed in the perturbation scheme. In particular, the key issue of the effects of the bandwidth of the images used are explored and a very simple procedure is described for checking that optimal results are being obtained. [References: 19]
机译：互相关（GC-C）的最新发展使得可以通过与常规互相关中采用的常数位移，旋转等相反的参数化函数集来对诸如图像Fairs之类的数据之间的转换进行建模。 GC-C的典型应用是在诸如颗粒图像测速（PIV）或二维或三维表面应变场测定等领域。由于GC-C开发的流量，应变等描述是全局的或区域性的，因此需要使用通常用于提供此类技术中基本数据的图像中的全部或大部分信息来估算所需的参数。这与PIV中使用的传统互相关方法形成了鲜明对比，在传统的互相关方法中，图像域被分割为较小的子区域，并且在每个局部像元中分别确定了恒定的位移，旋转等。这样的局部细胞方法不可避免地在空间之间引入折衷。分辨率和统计信度，可以将其放在偏移，旋转等的估计中。GC-C消除了这种妥协的需要。本文考察了小扰动形式的GC-C在具有特殊条件的真实实验数据集中的应用。强调显示扰动方案中使用的解析近似的效果。特别是，探讨了所使用图像带宽影响的关键问题，并描述了一种非常简单的过程来检查是否获得了最佳结果。 [参考：19]

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