Double-frame tomographic PTV at high seeding densities
Double-frame tomographic PTV at high seeding densities
复制标题
高播种密度下的双帧断层扫描 PTV
作者：
P. Cornic;B. Leclaire;F. Champagnat;G. Le Besnerais;A. Cheminet;C. Illoul;G. Losfeld
提出了一种基于双帧多摄像机图像的三维PTV方法。粒子速度的估计通过以下三个步骤：首先，单独的粒子重建与稀疏基于算法的细网格上执行。其次，它们在执行3D相关的较粗网格上扩展，产生预测器位移场，该预测器位移场允许在两个时刻有效地匹配粒子。由于这些粒子仍然位于体素网格上，第三步，也就是最后一步，通过全局优化过程实现粒子位置细化到其实际子体素位置，同时考虑其强度。由于它强烈地利用了断层重建的原理，该技术被称为双帧断层PTV（DF-TPTV）。对复杂湍流的标准合成测试表明，该方法实现了高颗粒和矢量检测效率，种子密度高达约0.08个颗粒/像素（ppp）。在这些测试中，它还显示出比类似的最先进的方法更高的噪声鲁棒性和更低的速度估计的均方根误差。本文还介绍了雷诺数为4600的过渡圆形空气射流的实验结果。平均播种密度变化的时间从0.06到0.03 ppp在考虑运行期间，与不同的密度和信噪比被观察到的射流和环境空气区域，由两个不同的播种系统提供的时间。强烈的多分散性质的播种，以及共存的两个空间区域的显着不同的颗粒密度和信噪比，被观察到的DF-TPTV性能的限制是最有影响力的来源。然而，该方法仍然成功地重建了大量的颗粒，并与基于时间统计的离群值拒绝计划，如实地重建瞬时射流动力学。进一步的定量性能评估，然后提供通过引入统计执行bin平均，假设统计轴对称的射流。平均和波动的轴向速度分量在射流近场进行了比较，从平面PIV在较高的播种密度，与询问窗口的大小相媲美的箱子的参考结果。结果被发现是在良好的协议彼此，确认DF-TPTV的高性能，以产生可靠的体积矢量场的播种密度通常被认为是层析PIV处理。
A novel method performing 3D PTV from double-frame multi-camera images is introduced. Particle velocities are estimated by following three steps: First, separate particle reconstructions with a sparsity based algorithm are performed on a fine grid. Second, they are expanded on a coarser grid on which 3D correlation is performed, yielding a predictor displacement field that allows to efficiently match particles at the two time instants. As these particles are still located on a voxel grid, the third, final step achieves particle position refinement to their actual subvoxel position by a global optimization process, also accounting for their intensities. As it strongly leverages on principles from tomographic reconstruction, the technique is termed Double-Frame Tomo-PTV (DF-TPTV). Standard synthetic tests on a complex turbulent flow show that the method achieves high particle and vector detection efficiency, up to seeding densities of around 0.08 particles per pixel (ppp). On these tests, it also shows a higher robustness to noise and lower root-mean-square errors on velocity estimation than similar state-of-the-art methods. Results from an experimental campaign on a transitional round air jet at Reynolds number 4600 are also presented. Average seeding density varies in time from 0.06 to 0.03 ppp during the considered run, with different densities and signal-to-noise ratios being observed with time in the jet and ambient air regions, supplied by two different seeding systems. The strong polydisperse nature of the seeding, as well as the coexistence of two spatial zones of significantly different particle densities and signal-to-noise ratios, are observed to be the most influential sources of limitation for DF-TPTV performance. However, the method still successfully reconstructs a large amount of particles, and, associated with an outlier rejection scheme based on temporal statistics, truthfully reconstructs the instantaneous jet dynamics. Further quantitative performance assessment is then provided by introducing statistics performed by bin averaging, upon assuming statistical axisymmetry of the jet. Mean and fluctuating axial velocity components in the jet near-field are compared with reference results obtained from planar PIV at higher seeding density, with an interrogation window of size comparable to that of the bins. Results are found to be in excellent agreement with one another, confirming the high performance of DF-TPTV to yield reliable volumetric vector fields at seeding densities usually considered for tomographic PIV processing.