Fast 3D PIV with direct sparse cross-correlations
Fast 3D PIV with direct sparse cross-correlations
复制标题
具有直接稀疏互相关的快速 3D PIV
DOI：
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
T. Astarita
中科院分区：
文献类型：
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作者：
S. Discetti;T. Astarita
将二维二分量粒子图像测速（PIV）的高精度算法扩展到三维（3D）数据的情况涉及计算成本的相当大的增加。层析PIV受此问题的影响很大，依赖于三维互相关来估计速度场。在这项研究中，提出了一些解决方案，使一个更有效的计算速度场没有任何重大损失的准确性。提出了一种快速估计的预测位移场，体素合并的基础上，在第一步的过程。通过限制相关峰的搜索区域，有效地计算了校正位移场。在该过程的初始部分中，建议通过在块上使用快速傅立叶变换来计算减少的互相关图，以便通过避免在重叠询问窗口的情况下的冗余计算来加速处理。最后，直接互相关的搜索半径只有1个像素的估计峰值附近的采用;最终的迭代始终更快，因为直接相关可以更好地享受分布的稀疏性，减少操作的数量要执行。此外，三种不同的方法，以减少重叠窗口的冗余计算的数量，基于沿着段，平面或块的互相关系数的贡献的预先计算。该算法在合成和真实的图像上进行了测试，结果表明，根据待分析流场的复杂性，可以获得高达800倍的潜在加速。在真实的旋转射流上的挑战性应用导致了一个数量级的加速。
The extension of the well-assessed high-accuracy algorithms for two-dimensional-two components particle image velocimetry (PIV) to the case of three-dimensional (3D) data involves a considerable increase of the computational cost. Tomographic PIV is strongly affected by this issue, relying on 3D cross-correlation to estimate the velocity field. In this study, a number of solutions are presented, enabling a more efficient calculation of the velocity field without any significant loss of accuracy. A quick estimation of the predictor displacement field is proposed, based on voxels binning in the first steps of the process. The corrector displacement field is efficiently computed by restricting the search area of the correlation peak. In the initial part of the process, the calculation of a reduced cross-correlation map by using Fast Fourier Transform on blocks is suggested, in order to accelerate the processing by avoiding redundant calculations in case of overlapping interrogations windows. Eventually, direct cross-correlations with a search radius of only 1 pixel in the neighborhood of the estimated peak are employed; the final iterations are consistently faster, since direct correlations can better enjoy the sparsity of the distributions, reducing the number of operations to be performed. Furthermore, three different approaches to reduce the number of redundant calculations for overlapping windows are presented, based on pre-calculations of the contributions to the cross-correlations coefficients along segments, planes or blocks. The algorithms are tested both on synthetic and real images, showing that a potential speed-up of up to 800 times can be obtained, depending on the complexity of the flow field to be analyzed. The challenging application on a real swirling jet results in a speed-up of an order of magnitude.