Ensemble 3D PTV for high resolution turbulent statistics
Ensemble 3D PTV for high resolution turbulent statistics
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DOI：
10.1088/0957-0233/27/12/124011
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发表时间：
2016-12-01
影响因子：
2.4
通讯作者：
Discetti, Stefano
中科院分区：
文献类型：
--
作者：
Aguera, Nereida;Cafiero, Gioacchino;Discetti, Stefano
提出了一种通过系综平均从三维PIV测量数据中提取湍流统计信息的方法。所提出的技术是集合粒子跟踪测速方法的3D扩展，其包括对在低图像密度样本上计算的速度矢量的分布求和，然后从子体积内的速度矢量提取统计矩，与子的大小，体积取决于所需的粒子数量和可用的快照数量。3D测量的扩展带来了额外的稀疏速度矢量分布的困难，因此需要大量的快照来实现具有足够精确度的高分辨率测量。在当前状态下，这阻碍了单体素测量的实现，除非有数百万个样本可用。因此，人们不得不放弃空间分辨率，并且生活在仍然相对较大的（如果与体素相比）子体积中。这导致了进一步的问题，可能发生的残余平均速度梯度内的子体积，这显着污染的计算二阶moment.In这项工作中，我们提出了一种方法来减少残余梯度效应，允许达到高分辨率，即使相对较大的询问点，因此仍然可以获得大量的颗粒，在这些颗粒上可以计算湍流统计。该方法包括在应用多项式拟合的速度分布在每个子体积试图模仿剩余的平均速度梯度。
A method to extract turbulent statistics from three-dimensional (3D) PIV measurements via ensemble averaging is presented. The proposed technique is a 3D extension of the ensemble particle tracking velocimetry methods, which consist in summing distributions of velocity vectors calculated on low image density samples and then extract the statistical moments from the velocity vectors within sub-volumes, with the size of the sub-volume depending on the desired number of particles and on the available number of snapshots.The extension to 3D measurements poses the additional difficulty of sparse velocity vectors distributions, thus requiring a large number of snapshots to achieve high resolution measurements with a sufficient degree of accuracy. At the current state, this hinders the achievement of single-voxel measurements, unless millions of samples are available. Consequently, one has to give up spatial resolution and live with still relatively large (if compared to the voxel) sub-volumes. This leads to the further problem of the possible occurrence of a residual mean velocity gradient within the sub-volumes, which significantly contaminates the computation of second order moments.In this work, we propose a method to reduce the residual gradient effect, allowing to reach high resolution even with relatively large interrogation spots, therefore still retrieving a large number of particles on which it is possible to calculate turbulent statistics. The method consists in applying a polynomial fit to the velocity distributions within each sub-volume trying to mimic the residual mean velocity gradient.