This paper presents a method which allows for a reduced portion of a particle image velocimetry (PIV) image to be analysed, without introducing numerical artefacts near the edges of the reduced region. Based on confidence intervals of statistics of interest, such a region can be determined automatically depending on user-imposed confidence requirements, allowing for already satisfactorily converged regions of the field of view to be neglected in further analysis, offering significant computational benefits. Temporal fluctuations of the flow are unavoidable even for very steady flows, and the magnitude of such fluctuations will naturally vary over the domain. Moreover, the non-linear modulation effects of the cross-correlation operator exacerbate the perceived temporal fluctuations in regions of strong spatial displacement gradients. It follows, therefore, that steady, uniform, flow regions will require fewer contributing images than their less steady, spatially fluctuating, counterparts within the same field of view, and hence the further analysis of image pairs may be solely driven by small, isolated, non-converged regions. In this paper, a methodology is presented which allows these non-converged regions to be identified and subsequently analysed in isolation from the rest of the image, while ensuring that such localised analysis is not adversely affected by the reduced analysis region, i.e. does not introduce boundary effects, thus accelerating the analysis procedure considerably. Via experimental analysis, it is shown that under typical conditions a 44% reduction in the required number of correlations for an ensemble solution is achieved, compared to conventional image processing routines while maintaining a specified level of confidence over the domain.

中文翻译：

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基于置信统计的选择性空间相关加速PIV图像分析收敛的可行性

本文提出了一种方法，该方法允许分析粒子图像测速 (PIV) 图像的缩小部分，而不会在缩小区域的边缘附近引入数值伪影。基于感兴趣统计的置信区间，可以根据用户强加的置信要求自动确定这样的区域，允许在进一步分析中忽略视野中已经令人满意地收敛的区域，从而提供显着的计算优势。即使对于非常稳定的流动，流动的时间波动也是不可避免的，这种波动的幅度自然会在整个域内变化。此外，互相关算子的非线性调制效应加剧了强空间位移梯度区域中感知的时间波动。它跟随，因此，稳定、均匀的流动区域将需要的贡献图像少于相同视场内不太稳定、空间波动的对应图像，因此对图像对的进一步分析可能仅由小的、孤立的、非收敛的驱动地区。在本文中，提出了一种方法，该方法允许识别这些非收敛区域并随后与图像的其余部分隔离地进行分析，同时确保这种局部分析不会受到减少的分析区域的不利影响，即不会引入边界效应，从而大大加快了分析过程。通过实验分析，表明在典型条件下，集成解决方案所需的相关数减少了 44%，