PIV uncertainty quantification by image matching
PIV uncertainty quantification by image matching
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DOI：
10.1088/0957-0233/24/4/045302
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发表时间：
2013-04-01
影响因子：
2.4
通讯作者：
Scarano, Fulvio
中科院分区：
文献类型：
--
作者：
Sciacchitano, Andrea;Wieneke, Bernhard;Scarano, Fulvio
提出了一种新的量化PIV数据不确定性的方法。这种方法是后验的，即利用速度场本身和原始图像一起作为输入来估计测量速度场的未知实际误差。该方法的原理依赖于超分辨率的概念：根据互相关分析对图像进行匹配，并测量由于两次曝光不完全匹配而匹配的粒子图像对之间的剩余距离(粒子视差向量)。对询问窗口内的视差向量集合进行统计分析。视差向量的离散度返回随机误差的估计，而视差的平均值指示系统误差的发生。首先通过蒙特卡罗仿真验证了该工作原理的有效性。考虑了两种不同的解调算法，即具有离散窗偏移的互相关算法和具有窗口变形的多程解调算法。在模拟记录中，考虑了粒子图像的位移、梯度、离面运动、播种密度和粒子图像直径等因素的影响。在所有情况下都获得了良好的一致性，表明误差估计器能够以令人满意的精度跟踪实际误差的趋势。在实际测量条件下，使用可获得时间分辨PIV数据的实验来验证这一概念。在这种情况下，“准确的”速度场是未知的；然而，利用高度时间过采样数据的冗余信息的高级询问算法获得了高精度的估计(金字塔相关，Sciacitano等人(2012 Exp.流体53 1087-105))。图像匹配估计器返回估计的速度测量误差的瞬时分布。在高剪切区和三维湍流区，其空间分布与实际误差极大值吻合得很好。估计误差与实际误差之间的高度相关性表明，这种新方法可以用来直接从PIV数据中推断测量不确定度。给出了利用误差估计的结果通过选择最佳询问窗口大小来最小化测量不确定性的过程。
A novel method is presented to quantify the uncertainty of PIV data. The approach is a posteriori, i.e. the unknown actual error of the measured velocity field is estimated using the velocity field itself as input along with the original images. The principle of the method relies on the concept of super-resolution: the image pair is matched according to the cross-correlation analysis and the residual distance between matched particle image pairs (particle disparity vector) due to incomplete match between the two exposures is measured. The ensemble of disparity vectors within the interrogation window is analyzed statistically. The dispersion of the disparity vector returns the estimate of the random error, whereas the mean value of the disparity indicates the occurrence of a systematic error. The validity of the working principle is first demonstrated via Monte Carlo simulations. Two different interrogation algorithms are considered, namely the cross-correlation with discrete window offset and the multi-pass with window deformation. In the simulated recordings, the effects of particle image displacement, its gradient, out-of-plane motion, seeding density and particle image diameter are considered. In all cases good agreement is retrieved, indicating that the error estimator is able to follow the trend of the actual error with satisfactory precision. Experiments where time-resolved PIV data are available are used to prove the concept under realistic measurement conditions. In this case the 'exact' velocity field is unknown; however a high accuracy estimate is obtained with an advanced interrogation algorithm that exploits the redundant information of highly temporally oversampled data (pyramid correlation, Sciacchitano et al (2012 Exp. Fluids 53 1087-105)). The image-matching estimator returns the instantaneous distribution of the estimated velocity measurement error. The spatial distribution compares very well with that of the actual error with maxima in the highly sheared regions and in the 3D turbulent regions. The high level of correlation between the estimated error and the actual error indicates that this new approach can be utilized to directly infer the measurement uncertainty from PIV data. A procedure is shown where the results of the error estimation are employed to minimize the measurement uncertainty by selecting the optimal interrogation window size.