A comparative study of five different PIV interrogation algorithms
A comparative study of five different PIV interrogation algorithms
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五种不同PIV询问算法的比较研究
DOI：
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发表时间：
2005
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通讯作者：
R. Karvinen
中科院分区：
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作者：
M. Piirto;H. Eloranta;P. Saarenrinne;R. Karvinen
五种不同的粒子图像测速（PIV）的询问算法进行了测试与数值生成的粒子图像和两个真实的数据集测量湍流与相对较小的粒子图像的大小为1.0-2.5像素。分析合成和真实的数据的颗粒图像的尺寸分布，以评估峰锁定发生的趋势。首先，在平均偏差和均方根误差方面的算法的准确性进行比较，模拟数据。然后，比较了这两种算法处理2：1收缩加速流中峰值锁定效应的能力，并分析了它们在Reτ=510时估算湍流边界层（TBL）近壁区均方根和雷诺剪应力分布的能力。后一种情况下的结果进行了比较，直接数值模拟（DNS）数据的TBL。这些算法是：标准快速傅立叶变换互相关（FFT-CC）、直接归一化互相关（DNCC）、具有离散窗移位迭代FFT-CC（DWS）、具有连续窗移位的迭代FFT-CC（CWS）、以及具有图像变形的迭代FFT-CC CWS（CWD）。所有算法均采用高斯三点峰值拟合进行亚像素估计。根据与非变形算法的测试，DNCC似乎给出了最好的均方根估计的墙壁，和CWS方法给出了稍小的峰值锁定观察比其他方法。在此基础上，提出了一种基于粒子图像尺寸分析的双线性图像插值的偏差补偿方法，并进行了实验验证，得到了与基于基数函数的图像插值相同的效果。利用CWD算法，分析了迭代循环之间的空间滤波器大小的影响，发现它对结果有很大的影响。在近壁区，湍流强度的变化高达4%，这取决于所选择的询问算法。此外，对算法的计算性能进行了测试。
Five different particle image velocimetry (PIV) interrogation algorithms are tested with numerically generated particle images and two real data sets measured in turbulent flows with relatively small particle images of size 1.0–2.5 pixels. The size distribution of the particle images is analyzed for both the synthetic and the real data in order to evaluate the tendency for peak-locking occurrence. First, the accuracy of the algorithms in terms of mean bias and rms error is compared to simulated data. Then, the algorithms’ ability to handle the peak-locking effect in an accelerating flow through a 2:1 contraction is compared, and their ability to estimate the rms and Reynolds shear stress profiles in a near-wall region of a turbulent boundary layer (TBL) at Reτ=510 is analyzed. The results of the latter case are compared to direct numerical simulation (DNS) data of a TBL. The algorithms are: standard fast Fourier transform cross-correlation (FFT-CC), direct normalized cross-correlation (DNCC), iterative FFT-CC with discrete window shift (DWS), iterative FFT-CC with continuous window shift (CWS), and iterative FFT-CC CWS with image deformation (CWD). Gaussian three-point peak fitting for sub-pixel estimation is used in all the algorithms. According to the tests with the non-deformation algorithms, DNCC seems to give the best rms estimation by the wall, and the CWS methods give slightly smaller peak-locking observations than the other methods. With the CWS methods, a bias error compensation method for the bilinear image interpolation, based on the particle image size analysis, is developed and tested, giving the same performance as the image interpolation based on the cardinal function. With the CWD algorithms, the effect of the spatial filter size between the iteration loops is analyzed, and it is found to have a strong effect on the results. In the near-wall region, the turbulence intensity varies by up to 4%, depending on the chosen interrogation algorithm. In addition, the algorithms’ computational performance is tested.