A quantitative study of track initialization of the four-frame best estimate algorithm for three-dimensional Lagrangian particle tracking
A quantitative study of track initialization of the four-frame best estimate algorithm for three-dimensional Lagrangian particle tracking
复制标题
三维拉格朗日粒子跟踪四帧最佳估计算法轨迹初始化的定量研究
DOI：
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发表时间：
2019
影响因子：
2.4
通讯作者：
A. Aliseda
中科院分区：
文献类型：
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作者：
Alicia Clark;N. Machicoane;A. Aliseda
介绍了一种改进三维粒子跟踪测速算法的方法。3D-PTV是一种用于测量单个粒子随时间变化的拉格朗日轨迹的实验方法。轨迹是通过链接来自一系列图像的粒子位置来构建的。在文献中已经提出了不同的3D-PTV算法，从简单地获取下一帧中的最近邻居到使用多帧方案。本文主要研究了Ouellette等人(2006 Exp.液体40 301-13)。以前，跟踪算法是通过使用粒子在下一帧中的最近邻居(S)或通过使用速度猜测来预测粒子在下一帧中的位置来初始化的。我们提出了一种更健壮的初始化，加上4BE，在产生更多正确轨迹的意义上，它比文献中现有的方法执行得更好。通过对来自Johns Hopkins湍流数据库的直接数值模拟数据的应用，比较了所提出的初始化方法和使用最近邻初始化的4BE方法的性能。我们发现，在均匀各向同性湍流和湍流通道流(非均匀和各向异性)这两种典型情况下，改进的初始化大大改善了跟踪，即使在具有挑战性的播种/粒子置换条件下，也极大地提高了找到正确轨迹的百分比。
We introduce a method to improve three-dimensional particle tracking velocimetry (3D-PTV) algorithms. 3D-PTV is an experimental method used to measure the Lagrangian trajectories of individual particles over time. The trajectories are constructed by linking the particle positions from a sequence of images. Different 3D-PTV algorithms have been proposed in the literature, ranging from simply taking the nearest neighbor in the next frame to using multiframe schemes. This work focuses on the initialization of the four-frame best estimate (4BE) method introduced by Ouellette et al (2006 Exp. Fluids 40 301–13). Previously, tracking algorithms have been initialized by using the particle’s nearest neighbor(s) in the next frame or by using a velocity guess to predict the particle’s location in the next frame. We propose a more robust initialization, coupled with 4BE, that performs better than existing methods in the literature, in the sense of yielding a higher number of correct tracks. The performance of the proposed initialization method is compared to the 4BE method that uses nearest neighbor initialization by applying both methods on direct numerical simulation data from the Johns Hopkins turbulence databases. We show that the modified initialization greatly improves tracking in two canonical cases, homogeneous isotropic turbulence and turbulent channel flow (inhomogenous and anisotropic), greatly increasing the percentage of correct tracks found even under challenging seeding/particle displacement conditions.