粒子图像测速的元不确定度
Meta-Uncertainty for Particle Image Velocimetry
摘要：
粒子图像测速（PIV）的不确定性量化对于比较流场与计算流体动力学（CFD）结果以及模型设计和验证至关重要。然而，PIV具有复杂的测量链，具有耦合的非线性误差源，并且量化不确定性具有挑战性。多项评估表明，目前没有一种方法可以可靠地测量各种实验的实际不确定度。由于目前的方法在测量过程和计算程序的假设上有所不同，因此尚不清楚哪种方法最适合用于实验。为了解决这个问题，我们提出了一种方法来估计不确定性方法的灵敏度和可靠性，称为元不确定性。新的方法是自动的，本地的，和瞬时的，并基于扰动的记录粒子图像。在考虑相关平面信噪比的基础上，提出了一种在询问窗口对中加入随机非匹配粒子的图像扰动方法。每个不确定性计划的响应随机粒子添加的几次试验被用来估计的可靠性度量，定义为随着粒子添加水平的增加的不确定性的四分位数范围（IQR）的变化率。我们还建议将元不确定性作为加权度量，根据共识预测文献中的思想，将各个方案的不确定性估计结合起来。通过对一系列典型流场的PIV测量结果进行分析，结果表明，组合不确定度方法的性能优于单独方法，从而确立了元不确定度作为PIV不确定性量化的可靠性评估工具。
英文摘要：
Uncertainty quantification for Particle Image Velocimetry (PIV) is critical for comparing flow fields with Computational Fluid Dynamics (CFD) results, and model design and validation. However, PIV features a complex measurement chain with coupled, non-linear error sources, and quantifying the uncertainty is challenging. Multiple assessments show that none of the current methods can reliably measure the actual uncertainty across a wide range of experiments. Because the current methods differ in assumptions regarding the measurement process and calculation procedures, it is not clear which method is best to use for an experiment. To address this issue, we propose a method to estimate an uncertainty method's sensitivity and reliability, termed the Meta-Uncertainty. The novel approach is automated, local, and instantaneous, and based on perturbation of the recorded particle images. We developed an image perturbation scheme based on adding random unmatched particles to the interrogation window pair considering the signal-to-noise (SNR) of the correlation plane. Each uncertainty scheme's response to several trials of random particle addition is used to estimate a reliability metric, defined as the rate of change of the inter-quartile range (IQR) of the uncertainties with increasing levels of particle addition. We also propose applying the meta-uncertainty as a weighting metric to combine uncertainty estimates from individual schemes, based on ideas from the consensus forecasting literature. We use PIV measurements across a range of canonical flows to assess the performance of the uncertainty schemes.The results show that the combined uncertainty method outperforms the individual methods, and establish the meta-uncertainty as a useful reliability assessment tool for PIV uncertainty quantification.
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