Two-Dimensional Space-Time Image Velocimetry for Surface Flow Field of Mountain Rivers Based on UAV Video

Space-time image velocimetry (STIV) is a promising technique for river surface flow field measurement with the development of unmanned aerial vehicles (UAVs). STIV can give the magnitude of the velocity along the search line set manually thus the application of the STIV needs to determine the flow direction in advance. However, it is impossible to judge the velocity direction at any points before measurement in most mountainous rivers due to their complex terrain. A two-dimensional STIV is proposed in this study to obtain the magnitude and direction of the velocity automatically. The direction of river flow is independently determined by rotating the search line to find the space-time image which has the most prominent oblique stripes. The performance of the two-dimensional STIV is examined in the simulated images and the field measurements including the Xiasi River measurements and the Kuye River measurements, which prove it is a reliable method for the surface flow field measurement of mountain rivers.

Accuracy Verification of 2-Dimensional Velocity Field Derived From Optical Flow With Respect to PIV Flow Field Measurement

As a mature flow field measurement technology, particle image velocimetry (PIV) has been widely used to calculate the instantaneous motion of particles by identifying and comparing the positions of tracer particles between successive image frames. For example, the ambient air velocity distributions around fuel spray can be measured using LIF-PIV However, due to the need to accurately identify the tracer particles, PIV is limited in its applications, such as measuring the flow field in turbid media. Optical flow calculates apparent velocities of movement of brightness patterns in an image. Different from PIV, optical flow segments images into regions that corresponds to different objects without requirement of tracer particles. Since it is based on brightness patterns, optical flow may be widely used in measuring flow field in the area of fuel spray and in-cylinder combustion. To check the accuracy and robustness of optical flow, this study will calculate the 2-dimensional velocity fields of the same PIV image sequences using iterative optical flow and PIV respectively. Using PIV results as criterions, precision and accuracy of optical flow are studied.

Comparison of Methods for Bed Shear Stress Estimation in Complex Flow Field of Bend

Bed shear stress is closely related to sediment transport in rivers. Bed shear stress estimation is very difficult, especially for complex flow fields. In this study, complex flow field measurement experiments in a 60° bend with a groyne were performed. The feasibility and reliability of bed shear stress estimations using the log-law method in a complex flow field were analyzed and compared with those associated with the Reynolds, Turbulent Kinetic Energy (TKE), and TKE-w′ methods. The results show that the TKE, Reynolds, and log-law methods produced similar bed shear stress estimates, while the TKE-w′ method produced larger estimates than the other methods. The TKE-w′ method was found to be more suitable for bed shear stress estimation than the TKE method, but the value of its constant C2 needed to be re-estimated. In a complex, strong, three-dimensional flow field, the height of the measurement point (relative or absolute) should be re-estimated when a single point measurement is used to estimate the bed shear stress. The results of this study provide guidance for experimental measurement of bed shear stress in a complex flow field.

Hybrid Adaptive Wavelet-Based Optical Flow Algorithm for Background Oriented Schlieren (BOS) Experiments

Quantitative analysis of the flow field is an effective method to study hydrodynamics. As a flow field measurement technology, the Background Oriented Schlieren (BOS) is widely used. However, it is difficult to measure the complex transparent flow field (flow field with large refractive index gradient) using the BOS experiment. In order to overcome this difficulty and improve the accuracy of the BOS experiment, this paper presents a hybrid adaptive wavelet-based optical flow algorithm for the BOS. The current algorithm is a combination of the traditional optical flow algorithm and the wavelet-based optical flow algorithm. By adding the initial value constraints, the adaptive scale constraints, and the adaptive regularization constraints, the algorithm can effectively overcome the above-mentioned difficulty and also improve its accuracy. To further illustrate the feasibility of the proposed method, this paper uses the simulation data, the data of the DNS datasets, and the data of the BOS experiment to verify the performance of the algorithm. The experiment of comparing the proposed algorithm with the existing ones is carried out on the DNS datasets and the data of the BOS experiment. Finally, the proposed method is verified by a practical BOS experiment. The results show that the proposed algorithm can effectively improve the measurement accuracy of displacements.