Volumetric particle tracking velocimetry (PTV) uncertainty quantification
Volumetric particle tracking velocimetry (PTV) uncertainty quantification
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DOI：
10.1007/s00348-020-03021-6
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发表时间：
2020-08-18
影响因子：
2.4
通讯作者：
Vlachos, Pavlos P.
中科院分区：
文献类型：
--
作者：
Bhattacharya, Sayantan;Vlachos, Pavlos P.
我们介绍了第一个全面的方法来确定体积粒子跟踪测速（PTV）测量的不确定性。体积PTV是一种最先进的非侵入式流量测量技术，它通过使用多摄像机设置记录示踪粒子运动的连续快照来测量速度场。测量链涉及使用校准的相机映射函数通过三角测量过程重建三维颗粒位置。在迭代自校准校正和粒子重建步骤期间的元素误差源的非线性组合增加了任务的复杂性。在这里，我们首先估计粒子图像位置的不确定性，我们将其建模为粒子位置估计不确定性和重投影误差不确定性的组合。后者是通过高斯拟合子体积内的视差估计的直方图来获得的。接下来，我们确定相机校准系数中的不确定性。作为最后一步，使用通过体积重建过程的不确定性传播来组合前两个不确定性。速度矢量的不确定性直接作为重构粒子位置不确定性的函数获得。该框架进行了测试与合成涡环图像。结果表明，预测和预期的RMS不确定度值之间的良好协议。在0.01-0.1颗粒/像素范围内测试的接种密度的预测是一致的。最后，该方法也成功地验证了层流管流速度剖面测量的实验测试情况下，预测的不确定性在流向分量的RMS误差值的9%以内。[图形]。
We introduce the first comprehensive approach to determine the uncertainty in volumetric Particle Tracking Velocimetry (PTV) measurements. Volumetric PTV is a state-of-the-art non-invasive flow measurement technique, which measures the velocity field by recording successive snapshots of the tracer particle motion using a multi-camera set-up. The measurement chain involves reconstructing the three-dimensional particle positions by a triangulation process using the calibrated camera mapping functions. The non-linear combination of the elemental error sources during the iterative self-calibration correction and particle reconstruction steps increases the complexity of the task. Here, we first estimate the uncertainty in the particle image location, which we model as a combination of the particle position estimation uncertainty and the reprojection error uncertainty. The latter is obtained by a gaussian fit to the histogram of disparity estimates within a sub-volume. Next, we determine the uncertainty in the camera calibration coefficients. As a final step, the previous two uncertainties are combined using an uncertainty propagation through the volumetric reconstruction process. The uncertainty in the velocity vector is directly obtained as a function of the reconstructed particle position uncertainty. The framework is tested with synthetic vortex ring images. The results show good agreement between the predicted and the expected RMS uncertainty values. The prediction is consistent for seeding densities tested in the range of 0.01-0.1 particles per pixel. Finally, the methodology is also successfully validated for an experimental test case of laminar pipe flow velocity profile measurement where the predicted uncertainty in the streamwise component is within 9% of the RMS error value.[GRAPHICS].