This study describes a multi-camera photogrammetric approach to measure the 3D velocity of free surface flow. The properties of the camera system and particle tracking velocimetry (PTV) algorithm were first investigated in a measurement of a laboratory open channel flow to prepare for field measurements. The in situ camera calibration methods corresponding to the two measurement situations were applied to mitigate the instability of the camera mechanism and camera geometry. There are two photogrammetry-based PTV algorithms presented in this study regarding different types of surface particles employed on the water flow. While the first algorithm uses the particle tracking method applied for individual particles, the second algorithm is based on correlation-based particle clustering tracking applied for clusters of small size particles. In the laboratory, reference data are provided by particle image velocimetry (PIV) and laser Doppler velocimetry (LDV). The differences in velocities measured by photogrammetry and PIV, photogrammetry and LDV are 0.1% and 3.6%, respectively. At a natural river, the change of discharges between two measurement times is found to be 15%, and the corresponding value reported regarding mass flow through a nearby hydropower plant is 20%. The outcomes reveal that the method can provide a reliable estimation of 3D surface velocity with sufficient accuracy.

中文翻译：

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自由表面流速测量的摄影测量：从实验室到现场测量

本研究描述了一种用于测量自由表面流动的 3D 速度的多相机摄影测量方法。首先在实验室明渠流量的测量中研究了相机系统和粒子跟踪测速 (PTV) 算法的特性，以准备现场测量。应用对应于两种测量情况的原位相机标定方法来减轻相机机构和相机几何结构的不稳定性。本研究中提出了两种基于摄影测量的 PTV 算法，涉及水流上采用的不同类型的表面颗粒。第一种算法使用应用于单个粒子的粒子跟踪方法，而第二种算法基于应用于小尺寸粒子簇的基于相关性的粒子聚类跟踪。在实验室中，参考数据由粒子图像测速仪 (PIV) 和激光多普勒测速仪 (LDV) 提供。摄影测量和 PIV、摄影测量和 LDV 测量的速度差异分别为 0.1% 和 3.6%。在一条天然河流中，发现两次测量时间之间的流量变化为 15%，而通过附近水电站的质量流量报告的相应值为 20%。结果表明，该方法可以以足够的精度提供可靠的 3D 表面速度估计。发现两次测量时间之间的流量变化为 15%，报告的关于附近水电站质量流量的相应值为 20%。结果表明，该方法可以以足够的精度提供可靠的 3D 表面速度估计。发现两次测量时间之间的流量变化为 15%，报告的关于附近水电站质量流量的相应值为 20%。结果表明，该方法可以以足够的精度提供可靠的 3D 表面速度估计。