Estimation of uncertainty bounds for individual particle image velocimetry measurements from cross-correlation peak ratio
Estimation of uncertainty bounds for individual particle image velocimetry measurements from cross-correlation peak ratio
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DOI：
10.1088/0957-0233/24/6/065301
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发表时间：
2013-06-01
影响因子：
2.4
通讯作者：
Vlachos, Pavlos P.
中科院分区：
文献类型：
--
作者：
Charonko, John J.;Vlachos, Pavlos P.
大量研究已经证实，粒子图像测速(PIV)是一种可靠的非侵入性、定量测量流体速度的方法，如果仔细进行，典型的测量可以准确地检测到分辨率远低于单个像素(在某些情况下低于百分之一像素)的数字图像中的位移。然而，到目前为止，这些估计只能为特定图像质量和流动条件下的平均测量的预期误差提供指导。本文介绍了一种新的方法，用于估计特定个体测量的不确定度在给定的置信度范围内。在这里，无论流动条件或图像质量如何，互相关峰比，即主峰高与副峰高的比率，与给定测量的观测误差值的范围密切相关。对于仅相位广义互相关PIV处理，这种关系明显更强，而标准相关方法表现出较弱的性能。使用从合成数据集得出的关系的分析模型，然后计算了几个人工和实验流场在95%可信区间的不确定度界限，结果表明所产生的误差与预测的不确定度非常匹配。虽然这种方法无法预测给定测量的真实误差，但在将PIV分析的结果应用于工程设计研究和计算流体力学验证工作时，了解PIV实验的不确定度水平应该会带来巨大的好处。此外，这种方法实现起来非常简单，所需的额外计算成本可以忽略不计。
Numerous studies have established firmly that particle image velocimetry (PIV) is a robust method for non-invasive, quantitative measurements of fluid velocity, and that when carefully conducted, typical measurements can accurately detect displacements in digital images with a resolution well below a single pixel (in some cases well below a hundredth of a pixel). However, to date, these estimates have only been able to provide guidance on the expected error for an average measurement under specific image quality and flow conditions. This paper demonstrates a new method for estimating the uncertainty bounds to within a given confidence interval for a specific, individual measurement. Here, cross-correlation peak ratio, the ratio of primary to secondary peak height, is shown to correlate strongly with the range of observed error values for a given measurement, regardless of flow condition or image quality. This relationship is significantly stronger for phase-only generalized cross-correlation PIV processing, while the standard correlation approach showed weaker performance. Using an analytical model of the relationship derived from synthetic data sets, the uncertainty bounds at a 95% confidence interval are then computed for several artificial and experimental flow fields, and the resulting errors are shown to match closely to the predicted uncertainties. While this method stops short of being able to predict the true error for a given measurement, knowledge of the uncertainty level for a PIV experiment should provide great benefits when applying the results of PIV analysis to engineering design studies and computational fluid dynamics validation efforts. Moreover, this approach is exceptionally simple to implement and requires negligible additional computational cost.