Large Scale Particle Tracking Velocimetry Research Articles

An Evaluation of Image Velocimetry Techniques under Low Flow Conditions and High Seeding Densities Using Unmanned Aerial Systems

Image velocimetry has proven to be a promising technique for monitoring river flows using remotely operated platforms such as Unmanned Aerial Systems (UAS). However, the application of various image velocimetry algorithms has not been extensively assessed. Therefore, a sensitivity analysis has been conducted on five different image velocimetry algorithms including Large Scale Particle Image Velocimetry (LSPIV), Large-Scale Particle Tracking Velocimetry (LSPTV), Kanade–Lucas Tomasi Image Velocimetry (KLT-IV or KLT), Optical Tracking Velocimetry (OTV) and Surface Structure Image Velocimetry (SSIV), during low river flow conditions (average surface velocities of 0.12–0.14 m s − 1 , Q60) on the River Kolubara, Central Serbia. A DJI Phantom 4 Pro UAS was used to collect two 30-second videos of the surface flow. Artificial seeding material was distributed homogeneously across the rivers surface, to enhance the conditions for image velocimetry techniques. The sensitivity analysis was performed on comparable parameters between the different algorithms, including the particle identification area parameters (such as Interrogation Area (LSPIV, LSPTV and SSIV), Block Size (KLT-IV) and Trajectory Length (OTV)) and the feature extraction rate. Results highlighted that KLT and SSIV were sensitive to changing the feature extraction rate; however, changing the particle identification area did not affect the surface velocity results significantly. OTV and LSPTV, on the other hand, highlighted that changing the particle identification area presented higher variability in the results, while changing the feature extraction rate did not affect the surface velocity outputs. LSPIV proved to be sensitive to changing both the feature extraction rate and the particle identification area. This analysis has led to the conclusions that for surface velocities of approximately 0.12 m s − 1 image velocimetry techniques can provide results comparable to traditional techniques such as ADCPs. However, LSPIV, LSPTV and OTV require additional effort for calibration and selecting the appropriate parameters when compared to KLT-IV and SSIV. Despite the varying levels of sensitivity of each algorithm to changing parameters, all configuration image velocimetry algorithms provided results that were within 0.05 m s − 1 of the ADCP measurements, on average.

Aerodynamic drag determination of a full-scale cyclist mannequin from large-scale PTV measurements

The aerodynamic drag of a human-scale wind tunnel model is obtained from large-scale particle tracking velocimetry measurements invoking the conservation of momentum in a control volume surrounding the model. Lagrangian particle tracking is employed to obtain the velocity and static pressure statistics in a thin volume in the wake of a cyclist mannequin at freestream velocities between 12.5 and 15 m/s, corresponding to Reynolds numbers from 5 times 105 to 6 times 105 based on the torso length. The spatial distributions of the time-average streamwise velocity and pressure coefficient match well with previous works reported in literature. The streamwise velocity fluctuations in the wake of the cyclist’s model are presented, clearly demonstrating the unsteady nature of the main wake flow structures. Furthermore, the obtained aerodynamic drag follows the expected quadratic increase with increasing freestream velocity. The accuracy of this drag estimation is evaluated by comparison to force balance data and corresponds to 30 drag counts. The three terms composing the overall drag force, ascribed to the mean and fluctuating streamwise velocity and the mean pressure, are also evaluated separately, demonstrating that the resistive force is dominated by the contribution of the mean streamwise momentum deficit, whereas the contribution of the pressure term is negligible.Graphical abstract

Rectification of Image Velocity Results (RIVeR): A simple and user-friendly toolbox for large scale water surface Particle Image Velocimetry (PIV) and Particle Tracking Velocimetry (PTV)

LSPIV (Large Scale Particle Image Velocimetry) and LSPTV (Large Scale Particle Tracking Velocimetry) are used as relatively low-cost and non-intrusive techniques for water-surface velocity analysis and flow discharge measurements in rivers or large-scale hydraulic models. This paper describes a methodology based on state-of-the-art tools (for example, that apply classical PIV/PTV analysis) resulting in large-scale surface-flow characterization according to the first operational version of the RIVeR (Rectification of Image Velocity Results). RIVeR is developed in Matlab and is designed to be user-friendly. RIVeR processes large-scale water-surface characterization such as velocity fields or individual trajectories of floating tracers. This work describes the wide range of application of the techniques for comparing measured surface flows in hydraulic physical models to flow discharge estimates for a wide range of flow events in rivers (for example, low and high flows).

RAPTOR‐UAV: Real‐time particle tracking in rivers using an unmanned aerial vehicle

AbstractRiver system measurement and mapping using UAVs is both lean and agile, with the added advantage of increased safety for the surveying crew. A common parameter of fluvial geomorphological studies is the flow velocity, which is a major driver of sediment behavior. Advances in fluid mechanics now include metrics describing the presence and interaction of coherent structures within a flow field and along its boundaries. These metrics have proven to be useful in studying the complex turbulent flows but require time‐resolved flow field data, which is normally unavailable in geomorphological studies. Contactless UAV‐based velocity measurement provides a new source of velocity field data for measurements of extreme hydrological events at a safe distance, and could allow for measurements of inaccessible areas. Recent works have successfully applied large‐scale particle image velocimetry (LSPIV) using UAVs in rivers, focusing predominantly on surficial flow estimation by tracking intensity differences between georeferenced images. The objective of this work is to introduce a methodology for UAV based real‐time particle tracking in rivers (RAPTOR) in a case study along a short test reach of the Brigach River in the German Black Forest. This methodology allows for large‐scale particle tracking velocimetry (LSPTV) using a combination of floating, infrared light‐emitting particles and a programmable embedded color vision sensor in order to simultaneously detect and track the positions of objects. The main advantage of this approach is its ability to rapidly collect and process the position data, which can be done in real time. The disadvantages are that the method requires the use of specialized light‐emitting particles, which in some cases cannot be retrieved from the investigation area, and that the method returns velocity data in unscaled units of px/s. This work introduces the RAPTOR system with its hardware, data processing workflow, and provides an example of unscaled velocity field estimation using the proposed method. First experiences with the method show that the tracking rate of 50 Hz allows for position estimation with sub‐pixel accuracy, even considering UAV self‐motion. A comparison of the unscaled tracks after Savitzky–Golay filtering shows that although the time‐averaged velocities remain virtually the same, the filter reduces the standard deviation by more than 40% and the maxima by 20%. Copyright © 2017 John Wiley & Sons, Ltd.

Application of video imagery techniques for low cost measurement of water surface velocity in open channels

Developments in digital video recording technology make the video imagery tools more popular for velocity measurement in water flows. This has especially been of large interest due to its inherent advantage of non-contact nature which is quite handy in extreme flow conditions. Particle Image Velocimetry (PIV), Particle Tracking Velocimetry (PTV) and Large Scale Particle Tracking Velocimetry (LSPTV) are applied to free surface channel flow for water surface velocity measurement. Experiments are conducted to measure either a single point velocity applying PTV or velocity profiles across the channel width applying PIV on the water surface in a rectang typical velocities of nearly 1 andular tilting flume for various flow conditions. Technical issues regarding tracer particle size and type, travel distance, lighting, recording speed, camera position, image distortion and state of flow are discussed. Measured data is compared to computational results obtained from a numerical model involving a non-linear turbulence model capable of predicting turbulence driven secondary flows. Confirmation of reasonable match between computational and experimental results whereby applying mutual collaboration of them for discharge measurement has been attested. In addition to discharge, boundary roughness has also been predicted as an outcome of the numerical solution.

Characterizing Coherent Wind Structures using Large-Scale Particle Tracking Velocimetry: A Proof-of-Principle Study

The following study proposes a two-dimensional large-scale particle tracking velocimetry (LS-PTV) system to characterize coherent wind structures. Seven minutes of LS-PTV data is collected via an apparatus that seeds fog-filled soap bubbles into the wind at a height of 6m from the ground. The LS-PTV data is compared to 20 minutes of data collected concurrently from a wind mast at the same site. The LS-PTV system recorded a mean streamwise velocity of 1.35m/s with a standard deviation of 0.23m/s at a mean height of 2.50m with a standard deviation of 0.7m, which agrees well with the velocity profile measured by the wind mast. Furthermore, the Reynolds stresses measured by the LS-PTV system are found to compare to those measured by the wind mast and by Klebanoff [1] for a canonical turbulent boundary layer. The current study assumes that the centre-of-curvature trajectories of the particle pathlines are representative of the trajectories followed by the spanwise vortices. As a proof-of-principle study, this work has been successful in accurately describing the vortex distribution very near to the ground. However, the trajectories followed by the centres-of- curvat.ure belonging to pathlines concurrently passing through the field-of-view were sporadic and uncorrelated.

The Development of a Large-Scale Particle Tracking Velocimery System for Wake Analysis of Wind-Loaded Structures

The current study examines the capabilities of large-scale particle tracking velocimet.ry (LS-PTV) in fully resolving the wake behind a wind-loaded structure. LS-PTV measurements within a 16m3 volume were acquired behind a 0.75m diameter sphere. The study uses a spherical obstruction as a base case to prove the feasibility of measuring wind- turbine wakes. The Reynolds number of the flow was approximately Re =1 x 105. The temporal longevity of paths increased at a rate of 0.0073D/Uo per sphere diameter, indicating that the seeding particles have the ability to withstand the shear forces present in the wake. Furthermore, the mean freest.ream-velocity deficit, profiles, the st.reamwise Reynolds stress profile, and the wake-deficit, decay obtained using the LS-PTV system agreed with studies performed by Amoura et. al. [1] and Eames et. al. [2], thereby demonstrating the system's ability to accurately quantify the mean flow. Finally, st.eady-flow, ramp-up and ramp-down events were identified within the data from the time trace of the freest.ream flow. The corresponding wake structures behind the sphere during the three events were characterized using the realtime spatial measurements achievable by the LS-PTV system. During steady-flow conditions, pat.hlines exhibited high mixing and high curvature within one diameter downstream of the sphere, whereas pat.hlines further downstream were comparatively straight. In contrast., the straightening of pathlines occurred further upstream during ramp-up and ramp-down events, indicating that, high freest.ream-velocity events such as gusts have an organizing effect, on the wake, which attenuates shedding structures.

Characterizing the lower log region of the atmospheric surface layer via large-scale particle tracking velocimetry

As a first step toward characterizing coherent structures within the atmospheric surface layer (ASL), measurements obtained via a large-scale particle tracking velocimetry (LS-PTV) system were validated against wind-measurement station data as well as canonical turbulent boundary layer studies. The LS-PTV system resolves three-dimensional, Lagrangian tracks over a 16 m^3 volume. Mean-velocity measurements, as well as vertical and shear Reynolds-stress measurements, generally agreed with wind-measurement station data and Reynolds-stress profiles referenced from literature. The probability distributions for streamwise, spanwise and vertical velocity-fluctuation components appear normally distributed about zero. Furthermore, the probability distributions for all three components of Lagrangian acceleration were exponential and followed the parametrization curve from LaPorta et al. (Lett Nat 409:1017–1019, 2001). Lastly, the vorticity probability distributions were exponential and symmetric about zero, which matches findings from Balint et al. (Fluid Mech 228:53–86, 1991). The vorticity intensity measured by the LS-PTV system was less than values from Priyadarshana et al. (Fluid Mech 570:307–346, 2007), which is attributed to the low spatial resolution. However, the average spacing of 0.5 m between tracer particles is deemed sufficient for the future characterization of vortical structures within the ASL.

Large-scale particle tracking velocimetry with multi-channel CCD cameras

This paper presents a large-scale particle tracking velocimetry (LSPTV) system for measuring surface velocity in vast unsteady flows. The system consists of a flexible number of one-computer-six- camera working units connected via local network or the internet. Multiple zero-tilting cameras are used to increases the field of view with minimum perspective distortions. External synchronization of video outputs of all cameras facilitates uninterrupted image acquisition within the system. Effectiveness and efficiency of the multi-channel LSPTV are enhanced by optimizing illumination, flow seeding, image acquisition and correction, tracer identification, particle matching, and post-processing. The capacity of the LSPTV was demonstrated in the measurement of physical model flows for the Three Gorges Project.

An improved PTV system for large-scale physical river model

To measure the surface flow in a physical river model, an improved system of Large-Scale Particle Tracking Velocimetry (LSPTV) was proposed and the elements of the PTV system were described. Usually the tracer particles of a PTV system seeded on water surface tend to form conglomerates due to surface tension of water. In addition, they can not float on water surface when water flow is shallow. Ellipsoid particles were used to avoid the above problems. Another important issue is particle recognition. In order to eliminate the influence of noise, particles were recognized by the processing of multi-frame images. The kernel of the improved PTV system is the algorithm for particle tracking. A new 3-frame PTV algorithm was developed. The performance of this algorithm was compared with the conventional 4-frame PTV algorithm and 2-frame PTV algorithm by means of computer simulation using synthetically generated images. The results show that the new 3-frame PTV algorithm can recover more velocity vectors and have lower relative error. In addition, in order to attain the whole flow field from individual flow fields, the method of stitching individual flow fields by obvious marks was worked out. Then the improved PTV system was applied to the measurement of surface flow field in Model Yellow River and shows good performance.

Case Study: Particle Velocimetry in a Model of Lake Ogallala

In a case study of Lake Ogallala, a reservoir in central Nebraska, large scale particle tracking velocimetry (LSPTV) is used to measure surface velocities in a physical model of the lake. Knowledge of flow patterns in the lake is essential for predicting the transport of dissolved oxygen (DO). A preliminary comparison with acoustic Doppler velocimetery (ADV) measurements shows that both LSPTV and large scale particle image velocimetry (LSPIV) accurately measure surface velocities. In the present study, LSPTV works better near flow boundaries and in regions with high velocity gradients since smaller sampling areas are possible, and unlike LSPIV measurements, LSPTV measurements are unbiased. Discharges measured at eight different transects using LSPTV were within 6% of the discharge measured with an orifice, the worst correlation occurring where the bathymetry was slightly nonuniform (making application of the 1/7-power law suspect). In the prototype, DO content periodically drops to unacceptable levels throughout most of the Keystone Basin (a subbasin of Lake Ogallala). Predicted flow patterns suggest that low DO problems are exacerbated in regions with low velocities since oxygen consumed by macrophytes during nighttime hours is not quickly replenished.