Abstract
Direct estimation of Lagrangian turbulence statistics is essential for the proper modeling of dispersion and transport in highly obstructed canopy flows. However, Lagrangian flow measurements demand very high rates of data acquisition, resulting in bottlenecks that prevented the estimation of Lagrangian statistics in canopy flows hitherto. We report on a new extension to the 3D Particle Tracking Velocimetry (3DPTV) method, featuring realtime particle segmentation that outputs centroids and sizes of tracer particles and performed on dedicated hardware during highspeed digital video acquisition from multiple cameras. The proposed extension results in four orders of magnitude reduction in data transfer rate that enables to perform substantially longer experimental runs, facilitating measurements of convergent statistics. The extended method is demonstrated through an experimental wind tunnel investigation of the Lagrangian statistics in a heterogeneous canopy flow. We observe that acceleration statistics are affected by the mean shear at the top of the canopy layer and that Lagrangian particle dispersion at small scales is dominated by turbulence in the wake of the roughness elements. This approach enables to overcome major shortcomings from Eulerianbased measurements which rely on assumptions such as the Taylor’s frozen turbulence hypothesis, which is known to fail in highly turbulent flows.
Introduction
Understanding turbulent transport and mixing in urban and plant canopy flows is important for modeling urban air pollution^{1} and for correctly estimating mass and momentum exchange rates (e.g., CO_{2}, H_{2}O) between the Earth surface and the atmosphere^{2}. The flow inside and rightabove the canopy is intrinsically inhomogeneous due to the direct interaction of high momentum atmospheric surface flow with large canopy roughness^{3} and dominated by large scale coherent structures^{4}. Strong inhomogeneities in space and time can be resolved by Lagrangian Stochastic Models (LSM)^{5,6}, which have been developed in the Lagrangian framework for describing fluid properties at the positions of fluid particles. These models implicitly rely on Lagrangian parameters and in particular, on the second order Lagrangian structure function. Therefore, directly measured Lagrangian statistics, are required for extending, validating and developing parameterizations for atmospheric LSMs within highly obstructed canopy flows.
The vast majority of canopy flow experiments provide Eulerianbased measurements, estimating statistics at fixed points in space, for example, by hot wire or LDA^{3,7,8,9,10,11,12,13,14,15}, by PIV^{16,17,18,19,20} or stereoscopic PIV^{21}. Obtaining Lagrangian flow parameters from Eulerian measurements requires assumptions with questionable validity in inhomogeneous flows. In particular, the estimation of the Lagrangian particle’s diffusivity based on the frozen turbulence hypothesis was shown to be invalid in canopies^{5,22,23}. Previous Lagrangian measurements in canopy flows tracked neutrally buoyant balloons in an urban street canyon^{24} and more recently implemented 2D particle tracking in a water flume to compare the Lagrangian and Eulerian timescales above a 2D canopy model^{16}. Direct 3D Lagrangian measurements are needed to verify the validity of the key assumptions in a canopy flow for the high fidelity LSM.
Three Dimensional Particle Tracking Velocimetry (3DPTV)^{25} is a flow measurement method developed in the Lagrangian framework. It uses synchronized multiview digital camera imaging and measures 3D trajectories of flow tracers. In order to successfully track multiple particles, 3DPTV requires obtaining images at high frame rates. The time interval between consecutive frames has to be \({\rm{\Delta }}t < {\rm{\Delta }}r\)/u (Δr is the distance between the particles in the frame and u is the typical velocity)^{25}. This parameter restricts the use of 3DPTV in turbulent flows with a significant mean flow to very highspeed imaging and short recording times. However, inhomogeneous canopy flows require long measurement durations for the statistics to be convergent and independent; typical wind tunnel experiments^{3,9,12,14,15} use minutes of recording times at each point of interest, resulting in combined sampling periods on the order of hours.
In the past, particle tracking methods have been used to study decaying quasihomogeneous turbulence in wind tunnels and water flumes^{26,27,28}, turbulent pipe flows^{29}, and turbulent boundary layers^{16,30,31}. In some cases^{26,30}, the camera system was installed on a traversing system moving at the mean flow speed, thus reducing, to some extent, the required data acquisition rate. However, traversing solutions are not applicable to flows that either exhibit strong velocity gradients or that are highly threedimensional or inhomogeneous, such as in mixing layers or canopy flows. Recent algorithms that combine tomographic and tracking methods^{32} are able to perform particle tracking at very high seeding densities (i.e. small Δr), yet they do not solve the data transfer rate bottleneck and are limited for short experimental recordings. Neuromorphic cameras^{33} were recently utilized to track helium filled soap bubbles in a wind tunnel, providing high temporal rates, yet their sensitivity is insufficient for small tracers to be identified over the complex background illumination in the canopy flow. Realtime image compression techniques based on image binarization^{34} or edge detection^{35} were suggested in the past to mitigate the data transfer rates, however, these methods are effective only in ideal cases where tracers can be trivially segmented on a uniformly dark background. The simple image compression is not applicable in experiments with light reflections, unevenly illuminated backgrounds, and in these cases more advanced image analysis algorithms are needed.
We present a novel solution to overcome the 3DPTV limitations of highspeed imaging, long recording times and unwanted bottlenecks by developing a realtime image analysis on hardware for the open source software, OpenPTV^{36}. This new system extends 3DPTV by allowing to perform complex image analysis at difficult experimental conditions. It extracts the centroid positions and sizes of tracer particles from images in realtime, in cases with uneven image backgrounds, reflections, or solid surfaces, just a few to mention. This provides a solution to the central problem of softwarebased methods that require data transfer (stateoftheart systems generate 9.6 Gb/s) to the computer. The new 3DPTV method allows performing unprecedentedly long experimental recordings in rough imaging conditions, that are required to obtain convergent Lagrangian statistics in the canopy flows. The proposed extension can be used to enrich the understanding of Lagrangian dynamics in inhomogeneous flows and to provide the key parameters for LSM.
We have successfully recorded millions of Lagrangians trajectories in an environmental wind tunnel model of a heterogeneous canopy flow, arriving at unprecedented 3D Lagrangian statistics in the canopy layer. Using this dataset, we explored 3D Lagrangian accelerations and singleparticle dispersion. We demonstrate how the mean shear above the canopy^{2,37,38} affects the standard deviation of the Lagrangian acceleration, however, the standardized probability density functions at various heights and across the canopy layer are similar to the results from the zero mean shear turbulent flows. Furthermore, we demonstrate an analogy of the variance of single particle dispersion with the dispersion theory of Taylor^{39}. The diffusivity that we estimate is shown to relate to the intensity of turbulent velocity fluctuations and to a length scale, similar to the previous works^{40}. This length scale does not vary with the position of observation across the heterogeneous canopy, which leads us to infer that it depends on turbulent structure developed in the wake of the roughness elements.
3DPTV Extension
The infographic in Fig. 1 presents the central concept of the proposed solution. It draws the line through the history of 3DPTV, highlighting the past, present and future development milestones. Thus, in traditional 3DPTV, analog or digital video is streamed from an imaging device to a storage media at a maximum possible transfer rate, typically limited by the storage media’s recording speed. First generations of 3DPTV systems were based on 640 × 480 pixel videos^{25}, whereas the present highspeed digital imaging systems reach 4 million pixel frames, recorded at 500 frames per second. The present maximal data transfer rates are about 2.4 Gb/s per camera, if the video is streamed to a digital video recording system, writing in parallel into multiple hard drives using highthroughput cables and dedicated electronics. As shown in red boxes in Fig. 1, 10 hours of recording using a 4camera 3DPTV system, such as in the canopy flow experiment, will transfer and store about ~70 × 10^{6} images, equivalent to about 250 Terabytes of data volume. Postprocessing of these images to reconstruct Lagrangian trajectories can then take many months, including the image reconstruction, image segmentation or object detection analysis (\({\mathscr{O}}(1)\,\sec \) per frame), stereomatching and particle tracking.
In 2007 and 2011, simplified image segmentation in realtime pioneered the compression of transferred images^{34,35} (blue boxes in Fig. 1). With image compression, the 10hour experiment data can be compressed by a factor of 1:1000^{34} to a total of ≈250 Gb, significantly reducing the volume of recorded data. However, image compression does not resolve the timeconsuming postprocessing steps that would take about one month. More importantly, simple image segmentation cannot perform well in complex image environments, with light reflections and uneven background illumination. The currently proposed extended system performs a more complex image analysis (presented in details below and in the Supplementary Materials), streaming only 2D blobcoordinates to the computer. This enables to record data in hard imaging conditions, while directly writing onto an offtheshelf hard drive. In comparison to previous solutions, the presented 10hour (combined) canopy flow experiment yielded a ~20 Gb data set, ending up at a 1:10000 compression ratio, and a corresponding significant reduction in computation times. These features are found essential for successfully tracking and measuring highly obstructed canopy flows.
In order to successfully track particles in time, their translation between consecutive frames should be very small compared to the typical interparticle distance (i.e. the trackability parameter), giving rise to the data bottlenecks^{25}. Since the presented 3DPTV extension enables to record for very long durations of time, a possible solution to the trackability issue is using low tracer seeding densities, while recovering statistics by conducting ensemble averages over time. The drawback is the reduction in spatial resolution of instantaneous flow realizations since it is determined by the number of seeding particles available for tracking per unit volume^{32}. This tradeoff between resolution and trackability was utilized in the present canopy flow experiment, where each frame typically contained 5–10 particles. Furthermore, exploiting the long recording sessions (10–15 minutes each) we obtained a large number of samples at each point in space while making sure that measurements are independent of each other due to the long time separation. In total, we have gathered roughly 10 hours of recorded Lagrangian data, spanned over a volume of 1 × 1.5 × 1 × H^{3} (H = 100 mm is the height of the canopy layer) at two nominal Re_{∞} levels. In terms of statistical convergence, we have gathered ~\({\mathscr{O}}({10}^{4})\) samples per cubical centimeter, which is comparable to single point measurements^{9,12}, and sufficient to estimate statistical moments at each point in space.
In principle, the realtime image analysis extension can record data for days, quantifying the changes in the flow both on very short timescales and for many turnovertimescales. In addition, the system provides an important advantage in the ability to postprocess the data and obtain Lagrangian trajectories on the same day; thus, it allows to improve and optimize the settings for the next day, increasing the repeatability of experiments. Lastly, Fig. 1 also presents the foreseeable future of 3DPTV of fully realtime measurements. In principle, such realtime 3DPTV could be achieved by formulating/operating realtime stereomatching and Lagrangian tracking algorithms, presumably on the same hardware.
Results
The trajectory dataset
We used the 3DPTV extension to conduct flow measurements in a windtunnel based canopy flow model, the result of which form a unique dataset of Lagrangian trajectories both inside and above the modeled canopy layer \(0.5H\le z\le 1.75H\). In the following, we used a coordinate system in which x is aligned with the streamwise direction, y is horizontally aligned with the cross stream, and z is vertical, pointing away from the bottom wall. We conducted measurements at two free stream Reynolds numbers \({R}{{e}}_{\infty }={U}_{\infty }H/\nu =1.6\times {10}^{4}\) and 2.6 × 10^{4}, where H = 100 mm is the height of the top of the canopy, \({U}_{\infty }=2.5\) and 4 m s^{−1} is the velocity at the center of the wind tunnel’s crosssection, and ν is the kinematic viscosity of the air.
The final dataset is composed of \({\mathscr{O}}({10}^{6})\) individual trajectories, which corresponds to \({\mathscr{O}}({10}^{7})\) data points in the 3D positionvelocityaccelerationtime phase space. The dataset was acquired in 40 sampling runs, at different subvolume and Re_{∞}. As an example, a small set of Lagrangian trajectories taken from 10 different subvolumes (i.e., five heights and two streamwisepositions) are shown in Fig. 2. Different colors correspond to different heights, and two streamwise positions are shown for each height. In each subset, the trajectories were recorded during approximately half a second (250–500 frames). The arrow above the roughness elements indicates the streamwise direction. Above the canopy, the trajectories are more straight, roughly aligned in the streamwise direction, whereas inside the canopy they are highly curved since the particles move in all directions. This profound contrast occurred due to the difference in turbulent intensity and highlights the strong inhomogeneity of the canopy flow.
Velocity distributions
In Fig. 3 we present probability distribution functions (PDFs) of the instantaneous streamwise velocity component, normalized with U_{∞}, for the \({R}{{e}}_{\infty }=2.6\times {10}^{4}\) case. Each PDF corresponds to roughly 250,000 samples, measured at five different heights over an entire canopy roughness cell  H × 1.5H in horizontal extent, and thus captures phenomena related to the spatial variation of the velocity field. Therefore, the PDFs are not directly comparable with the more common Eulerian fixedpoint PDFs of the turbulent velocity u′. The modes (most probable values) and the widths of the PDFs increase while rising from within the canopy layer to the roughness sublayer right above the canopy \(z\ge H\), covering velocities higher than the freestream wind velocity, U_{∞}, and demonstrating positive skewness. Positive skewness of the streamwise velocity is characteristic of canopy flows^{2}, and is typically associated with the asymmetry of highvelocity sweeps and lowvelocity ejections^{41,42}. However, the positive skewness in Fig. 3 could arise for different reasons as well, for example, due to spatial averaging, and partly to a wellknown bias that is found in imagingbased techniques that oversample lowvelocity tracers. This oversampling is related to slow particles that remain longer in the field of view, providing longer trajectories, and adding weight to samples of lower velocities^{25}. In addition, the seeding nozzles were not uniformly distributed in the wind tunnel crosssection, but rather, situated closer to the bottom wall; this might have resulted in flow tracers preferential sampling lowmomentum flow from the bottom wall over the highvelocity freestream.
Lagrangian accelerations
The Lagrangian acceleration, \(\overrightarrow{a}=d\overrightarrow{v}\)/dt, is a smallscale turbulent property^{43}, characterized by a short correlation time, on the order of the Kolmogorov time scale^{31,43,44}. Due to the separation of scales in highReynolds number turbulent flows, it was assumed that acceleration statistics are independent of the largescale structures of the flow. We address this hypothesis in the case of the canopy flow model.
We estimated statistics separately for the three components of Lagrangian acceleration, sampled at 450 locations. At each location, we used trajectories that cross a sphere of radius 0.05H. The size of the sample volumes and the number of sample locations were observed not to affect the results considerably, and each sphere contained \({\mathscr{O}}({10}^{4})\) samples on average. The PDFs of acceleration components were constructed from the results of all the sampling locations, utilizing flow stationarity and assuming ergodicity^{45}. In Fig. 4, PDFs of the three components of Lagrangian accelerations, normalized by the respective standard deviations, are presented for the two Re_{∞} cases. The PDFs from random sample locations are presented in scatter plots to represent the width of the distribution. The acceleration component PDFs exhibited long tails characteristic of the intermittent nature of the Lagrangian accelerations^{44,46,47}. The data for all the studied cases collapsed on the same curve such that statistics were remarkably similar despite the inhomogeneity of the flow in the canopy and difference in Re_{∞}. Furthermore, the stretched exponential function
applied previously to the zero mean shear, high Reynolds number turbulence^{47,48}, was fitted to the PDFs of Lagrangian accelerations, demonstrated by a solid curve in Fig. 4. The curve corresponds to Eq. (1) with parameters \(\beta =0.45\), \(\sigma =0.85\), \(\gamma =1.6\) and \(C=0.525\). The obtained exponent, γ, is in agreement with the zero mean shear flow cases^{47,48}, suggesting that the normalized Lagrangian acceleration PDFs are insensitive to the inhomogeneity and strong mean shear at the canopy roughness sublayer.
Following the standard approach to canopy flow analysis^{2}, we obtained horizontally averaged statistics as a function of height. In Fig. 5, we present the horizontally averaged acceleration standard deviation  \({\sigma }_{{a}_{i}}\)  as a function of height for the three acceleration components and the two Re_{∞} cases. The range of uncertainties, presented as error bars, was estimated using the bootstrapping method where the data were divided into subsets and error propagation was integrated with the horizontal averaging. For all the profiles, \({\sigma }_{{a}_{i}}\) reduced with the decrease in height, from 1.5H down to about 1.25H, where there was a local increase with a notable maximum at \(z\approx H\), and then a further decrease inside the canopy \(z < H\). Notably, the local maximum of Lagrangian acceleration variance profile coincided with previously reported peaks in turbulent kinetic energy dissipation profiles^{49} at the height typically associated with the canopy roughness sublayer. The shape of the profiles was similar for the two Re_{∞} cases, yet did not collapse on a single curve with a single normalization factor; this suggests that the dynamical processes inside and outside of the canopy layer do not scale with Re_{∞} at the same proportions, due to the nonlinearity of transport processes across the canopy layer.
In homogeneous isotropic turbulence, the Kolmogorov similarity theory leads to a scaling of the variance of Lagrangian accelerations^{45}  \({\sigma }_{{a}_{i}}^{2}={a}_{0}{\varepsilon }^{3/2}{\nu }^{1/2}\), where \(\varepsilon \) is the mean turbulent kinetic energy dissipation rate, ν is the kinematic viscosity and a_{0} is a Redependent coefficient^{43,47,50,51}. Using \(\varepsilon ={\sigma }_{u}^{3}\)/L with \({\sigma }_{u}^{2}=({\sigma }_{{u}_{x}}^{2}+{\sigma }_{{u}_{y}}^{2}+{\sigma }_{{u}_{z}}^{2})\)/3, and L representing the integral length scale^{52}, one arrives at:
Previous works in zero mean shear turbulent flows presented \({\sigma }_{{a}_{i}}\) versus σ_{u}^{47}, or conditional acceleration variance \(\langle {a}^{2}{\sigma }_{u}\rangle \)^{53} and found good agreement with the 9/2 power law in Eq. (2). Figure 6 presents the streamwise acceleration variance \({\sigma }_{{a}_{x}}^{2}\) plotted against \({\sigma }_{u}^{\mathrm{9/2}}\) for the two Re_{∞} cases. Similarly to the zero mean shear case^{47,53}, we noted that while \({\sigma }_{{a}_{x}}\) scales with \({\sigma }_{u}^{\mathrm{9/2}}\), there were two different slopes identifying two different regions, \(z\le H\) and \(z\ge 1.25H\). The two regions were connected by a transition region where \({\sigma }_{{a}_{x}}^{2}\) locally decreased with σ_{u}. The same behavior was seen in both Re_{∞} cases, and was observed for all acceleration components (not shown). The observation of two different regions where \({\sigma }_{{a}_{x}}^{2}\propto {\sigma }_{u}^{9/2}\) are in line with the proposed “mixing layer” structure of the canopy flows^{2,11,37}. The two trends in Fig. 6 suggest that the two regions of two different turbulent flows interact through a shear layer interface at the top of the canopy, \(H\le z\le 1.25H\). According to Eq. (2), the two slopes provide evidence that the smallscale structure of the turbulence is different inside from outside of the canopy layer, due to different magnitudes of the mean shear and the different degrees of inhomogeneity in these regions.
Dispersion
The Lagrangian dataset enables us to directly estimate turbulent dispersion in the canopy flow model. We examined Lagrangian tracers in respect to their first observed positions, \({\overrightarrow{x}}_{0}({t}_{0})\), and note that for stationary flows the statistics depend on time differences \(\tau =t{t}_{0}\)^{45}. Furthermore, we define the ensemble average 〈·〉 over all trajectories passing through a point \({\overrightarrow{x}}_{0}\), with respect to the same time difference, \(\tau \). Thus, the average particle displacement is defined as:
The transverse dispersion is obtained as the variance in respect to the mean displacement \({\rm{\Delta }}{x^{\prime} }_{i}({\overrightarrow{x}}_{0},\tau )\), with the diffusivity K defined as one half the time derivative of the dispersion^{52}:
We present results for three positions \({\overrightarrow{x}}_{0}\), located on a single vertical line at three different heights for the case of \({R}{{e}}_{\infty }=1.6\times {10}^{4}\). In Fig. 7(a), we present the displacements, \({\rm{\Delta }}x(\tau )=x(\tau ){x}_{0}\), of 1000 randomly chosen trajectories in black lines, and also the ensemble average displacement in the streamwise direction in dashed red lines. The dispersion is reflected in the width covered by trajectories relative to the ensemble average displacement curve (thick dashed line); the slope of each line is proportional to the streamwise velocity of trajectories. The finite size of the measurement volume limited the time range on which dispersion was estimable; this was more severe in regions with high mean velocity (for example the \(z=1.5H\) point seen in Fig. 7(a)).
Figure 7(b) presents the dispersion defined in Eq. (4), at the three locations as a function of \(\tau \). The dispersion is largest at the \(z=1.5H\) and decreases with the height. Interestingly, for all the three heights we observe a similar behavior of initial quadratic growth in time (reflecting a ballistic regime) followed by a linear growth, suggesting a constant diffusivity, Eq. (5), and in analogy with the dispersion theory of Taylor^{39} for homogeneous isotropic turbulence. The slopes of the dispersion curves decrease slightly at the large \(\tau \), due to particles leaving the measurement volume, as discussed above. Note that in canopy flows, diffusion is a consequence of both mechanical and turbulent diffusion^{40}; yet, due to the size of the observation volume, our discussion is relevant to the turbulent diffusion alone.
Canopy flows are inhomogeneous and are characterized by strong mean shear, both affecting Lagrangian dispersion. In a homogeneously sheared turbulent flow (i.e. a uniform mean velocity gradient perpendicular to the streamwise direction), the streamwise dispersion is asymptotically proportional to \(\langle {\rm{\Delta }}{x^{\prime} }^{2}\rangle \propto {\tau }^{3}\) at large times^{45}. Presenting our data in analogy with Taylor’s procedure in Fig. 7(b), we observe that the measured dispersion is not directly affected by the gradient of the mean velocity, at least not during the time intervals available in our measurements. The results point out that Lagrangian dispersion at these short time scales is dominated by turbulence in the wakes of the canopy roughness elements.
The inset of Fig. 7(b) presents the dispersion from three different heights that collapsed on a single curve after multiplying the time differences by the standard deviation of the streamwise velocity at each point  \({\sigma }_{{u}_{x}}({\overrightarrow{x}}_{0})\). This result was robust for measurements at other points and other Re_{∞} case (not shown here for the sake of brevity). Using Eq. (5), one can express the turbulent diffusivity in terms of a length scale \( {\mathcal L} \), \({K}_{T,x}=\frac{1}{2} {\mathcal L} {\sigma }_{{u}_{x}}\)^{40}, noting that the slope in the inset of Fig. 7(b) is equal to \(\frac{1}{2} {\mathcal L} \). The best fit shown in the inset gives \( {\mathcal L} =4.1\pm 0.1\,{\rm{mm}}\). This observation can be seen as an analogy to a previously reported relation between turbulent kinetic energy and diffusivity in twodimensional turbulence^{54}. It can furthermore be interpreted as a constant drag length scale^{40,55}, possibly related to turbulent fluctuations in the wake of the canopy roughness elements whose thickness (5 mm) is close to \( {\mathcal L} \). This can explain the fact that in this canopy the diffusivity increases with the height due to an increase in the turbulent velocity, while the relevant length scale is independent of z.
Conclusions
We report on an extension to the 3DPTV method which is based on an extensive realtime image analysis on dedicated hardware and software. This extended 3DPTV scheme has several advantages. (i) It enables very long and repeatable experimental runs under difficult imaging conditions, as demonstrated here on a canopy flow model in a fullscale environmental wind tunnel. (ii) It reduces the time required for image analysis, thereby increasing by several orders of magnitude, and the volume of recorded data, which enhances analysis. (iii) It supports threedimensional Lagrangian measurements in turbulent flows where high temporal resolutions are essential.
The proposed method has been successfully applied to measure Lagrangian trajectories of tracer particles within a canopy layer modeled in a fullscale environmental wind tunnel. This opened up an extraordinary opportunity to explore the inhomogeneous roughness sublayer in the Lagrangian framework. The intermittent dynamics of turbulence in the canopy layer requires the generation of a large dataset to obtain converged statistics, which is impossible to obtain from short recording sessions; this point is manifested by the heavy tails of the Lagrangian acceleration PDFs shown in Fig. 4 at 450 different locations.
We obtained unique Lagrangian turbulent flow statistics in the canopy flow model. We found that the shape of the standardized Lagrangian acceleration PDFs was independent of position, direction, and the free stream velocity in the range tested and was similar to that of the previously measured homogeneous isotropic turbulent flow cases. The amplitude of the Lagrangian acceleration, described here with its standard deviation, increased with height from the bottom wall, and had a local maximum at \(z\approx H\). Furthermore, the relation between the standard deviation of the Lagrangian acceleration and the root mean square of the turbulent velocity seemed to agree with Kolmogorov scaling but in two distinct regions  inside and above the canopy. Between these, we observed a transition region in which turbulent kinetic energy was not related to the local (in space) dissipation and small scales, in agreement with the mixing layer analogy suggested for the canopy flows^{11,37}. In addition to that, using the Lagrangian dataset we examined dispersion through the direct measurements of particle displacements passing through spatial locations at different heights. Our analysis revealed a Ballistic dispersion regime followed by a constant diffusivity range in a form analogous to Taylor’s^{39} dispersion theory. The diffusivity collapsed after dividing by the standard deviation of the turbulent velocity. Furthermore, it can be presented in a form of a length scale that seems to be predefined by the canopy obstacles and not by the mean shear. These results can be used to extend LSM in the atmospheric surface layer, incorporating corrections using measured Lagrangian accelerations through a Sawford^{56} model. Furthermore, the direct observation of Lagrangian trajectories at various heights can be used to validate existing models and suggest new diffusivity parameterization for contaminant dispersion inside canopies.
The measurements presented here hold additional information that remains to be processed and analyzed. The present results are only the first attempt to highlight the possibilities opened by the presented 3DPTV extension and the importance of threedimensional Lagrangian studies in turbulent canopy flows.
Methods
Realtime image analysis on hardware
The proposed extension was designed to resolve the data bottlenecks using a realtime image analysis system (internally called “Blob Recorded”) based on highend frame grabbers (microEnable v5) with FPGA hardware and software (Silicon Software GmbH, Germany). The system was designed and integrated in collaboration with specialists from 1Vision LTD (Netanya, Israel). The system runs complex image analysis on acquired images in realtime and at a high frame rate. The output is a data stream of the centroid position of each detected object in pixel coordinates, along with its size and a bounding box. This image analysis provides the 3DPTV method with a realtime input simultaneous to the image acquisition, a step that was traditionally performed offline on stored images. Subsequently, the tracer’s positions in pixel units are incorporated with the 3D camera calibration, stereomatching and tracking in order to reconstruct 3D Lagrangian trajectories^{25}. A block diagram of the algorithm used for realtime image analysis, is shown in Fig. 8. It is, in essence, a customized object identification and analyzing method, commonly implemented in image processing software, that could only recently be performed in realtime on FPGA with sufficient memory and computational capabilities. An extensive description of the blobrecorder algorithm and specifications can be found in the Supplementary Materials.
The wind tunnel canopy model
The experiment was performed in the Environmental Wind Tunnel Laboratory at the Israel Institute for Biological Research (IIBR). The facility is an opencircuit wind tunnel featuring a \(14\,{\rm{m}}\times 2\times 2\,{{\rm{m}}}^{2}\) test section^{57}. A surface layer typical of a canopy flow was created by placing “L”shaped rectangular roughness elements along the tunnel’s floor, and four spires^{7} upstream to the measuring section, as shown schematically in Fig. 9. As usual, we set x to coincide with the streamwise direction, y as the horizontal crossstream and z as the the wall’s normal direction, pointing against gravity. We introduced heterogeneity in the canopy by using roughness elements of two heights  0.5H and 1H, where \(H=100\,{\rm{mm}}\). The element’s width was 0.5H and the thickness was 4 mm. We arranged the elements in a staggered configuration. Each lateral row of elements was kept uniform in height, whereas heights were alternated between consecutive rows. In the first few meters of the test section, elements were fitted at a low density with spacing of 2H. Starting 50H upstream from the measurements location, a dens, staggered, doubleheight canopy layer was placed, with lateral spacing of 0.5H and a streamwise distance between consecutive rows of 0.75H. The canopy frontal area density, defined as \({\lambda }_{f}={A}_{f}\)/A_{T}, (where A_{f} is the frontal area of the elements and A_{T} is the lot area of the canopy layer), was \(\frac{9}{16}\). The experiments were conducted at nominal free wind speeds of \({U}_{\infty }=2.5\) and 4 m s^{−1}, corresponding to the Reynolds number of \({R}{{e}}_{\infty }\equiv {U}_{\infty }H\)/ν, approximately 1.6 × 10^{4} and 2.6 × 10^{4}, respectively, where ν is the kinematic viscosity. The wind speed was monitored using a Prandtl tube located at the test section’s inlet.
Seeding
Hollow glass microspheres (Potters Industries, Sphericell) were used as tracers. An inspection with a scanning electron microscope confirmed that the tracers were spherical, with diameters in the range [2–25] μm, and the mean diameter is \({d}_{p}\approx 10\,{\rm{\mu }}\)m. The mean particle response time in the Stokes regime was estimated as \({\tau }_{p}={\rho }_{p}{d}_{p}^{2}/18\mu \approx 0.4\,{\rm{ms}}\), implying a settling velocity of 4 mm s^{−1}, based on d_{p} and \({\rho }_{p}=1000\,{\rm{kg}}\,{{\rm{m}}}^{3}\). Following previous studies^{58}, the highest flow frequency resolved by the particles is 500 Hz (to an error of 5% in velocity RMS). The Kolmogorov timescale was estimated at ~7 ms, thus a comparison of this value with the estimated \({\tau }_{p}\) (\({\rm{Sk}}={\tau }_{p}\)/\({\tau }_{\eta }\approx 0.05\)) suggests that the particles are good flow tracers that resolve the turbulence dynamics, yet a minor filtering effect of the highest frequency content cannot be ruled out. The particles were seeded in the flow from four point sources located at two different heights, 7H and 3H, and placed 60H upstream from the measurement position (see Fig. 9). A great deal of effort was invested in constructing pneumatic seeding devices and in positioning them and setting their airintensity, to ensure well mixed and uniform seeding at the measurement location. The particles were in 24 hours oven drying to reduce agglomerations due to humidity. Dry powder was then first suspended in a pressurized vessel, and the lightest fraction of the particles was conveyed by the pressurecontrolled pneumatic tubing to the wind tunnel, at the ends of which we used filters that were fastened to two poles, 12 mm in diameter. It should be noted that due to these tracers’ size, researchers should be using protective dust masks, and particles release to the environment should be minimized and performed with respect to environmental regulations. The seeding densities in our experiment were sufficiently low in respect to the volume of air moving in the wind tunnel, such that the concentration was below a detectable level. Laskin nozzle generated aerosol droplets, typical for PIV experiments, had insufficient scattering due to the volume illumination and high frame rates of 3DPTV. It is noteworthy that helium filled soap bubbles are environmentally friendly alternative for tracer particles, recently demonstrated sufficiently large production rates, sufficient scattering characteristics and neutral buoyancy^{33,59}.
Given the rather high flow velocities and turbulence intensities in the wind tunnel canopy flow model, tracking of the small tracer particles required that we resort to low seeding densities^{25}. Ten particles were detected on average in each frame, resulting in an average inter particle distance in the order of \({\rm{\Delta }}r\sim {\mathscr{O}}(10\,{\rm{mm}})\). The typical ratio of interframe translation and interparticle distance was 0.5, which facilitated particle tracking through the fourtime step tracking algorithm^{25}. The high frame rate and long recording enables the high number of samples needed for converging turbulent flow statistics at each subvolume, measured over thousands of flow turnovertimescales (H/U_{∞}). Although it is possible to increase laser power and capture images at higher frame rates, this will still require to reduce the camera resolution and introduce an undesired heating of the roughness elements and affect the flow.
Experimental setup
Intense illumination was required to identify the fast moving small tracer particles in the images, for which we used a 10 W, 532 nm, continuous wave laser (CNI lasers, MGLV532). The laser beam was expanded to an ellipsoidal shape with radii 80 × 40 mm through a pair of cylindrical lenses, rotated 90 degrees relative to each other around the beam axis. Under the continuous illumination source, fastest tracers appeared as streaks, up to ~15 pixels length and roughly ~3 pixels in thickness. The real time image analysis algorithm was able to detect them, where the centroid position was used in the PTV analysis. The laser was positioned outside the test section on one side of the wind tunnel. Four highspeed CMOS cameras (Optronis CP804M/C500, 100 mm lenses, magnification ~0.12) were positioned on the opposite side of the test section, pointing towards the measurement location (see Fig. 9). Thus, the tracers were illuminated in the forwardscattering mode, which is stronger than the more commonly used sidescatter mode. A similar coaxial imaging concept was recently discussed in depth in the context of tomographic PIV measurements^{60}. The strong illumination enabled reduction of the shutter time and improved the cameras’ focal depth by using smaller aperture. The increased number of outoffocus tracers were filtered out by the realtime image processing through an adaptive threshold algorithm. The solid angle between the cameras was roughly 10° so that the region between the elements could be visualized by all four of them.
The volume observed by the cameras at the measurement location and the actual spatial resolution were approximately 80 × 40 × 40 mm, (in streamwise, wall normal and spanwise directions) and ~50 μm per pixel, respectively. The measurements were conducted at 20 subvolumes distributed over 5 heights, to ensure sampling of the complete single canopy unitcell within the staggered, doubleheight canopy layer.
Calibration is an essential part of the 3DPTV method that dictates, to a large extent, its uncertainty in the determination of the tracer positions^{25}. In the wind tunnel, we used a threedimensional calibration target composed of 80 white dots, each with a 0.5 mm diameter, at known positions over 3 vertical planes. The dense arrangement of the roughness elements prevented us from accessing the measurement location during the experiment. Thus, the target was mounted on a remotely controlled, tripleaxis, traverse system with a positioning error of less than 40 μm; thereby we achieved repeatable experimental runs and fast calibration cycles (2–3 minutes). Typical calibration errors in this experiment were estimated based on repeated calibration and detection of the calibration targets as 15 μm in the x, z (streamwise  floornormal, see Fig. 9) plane, and 30 μm in the y plane, along the imaging axis (depth). At the highest frame rate of 1000 Hz, this error could propagate into a singlepoint velocity uncertainty of approximately 15 mm s^{−1}, and 30 mm s^{−1}, respectively. Note that generally, in a 3DPTV experiment, the velocity uncertainty is estimated using additional available information in space and time, along the Lagrangian trajectories^{26}.
The recording rate was 500 Hz at the full resolution of 2304 × 1720 pixels inside the canopy surface layer, and 1000 Hz at a reduced resolution 2304 × 860 pixels above the canopy. The settings are a tradeoff between the requirements for illumination, higher recording rate due to large velocities and shorter exposure times (to avoid smearing of tracer images). The cameras were synchronized with an external timing controller (CC320, Gardasoft). The maximal error in timing was significantly shorter than a 1/frame rate (~10 μs). The recording system is responsible for counting the recorded frames as a verification of the data transfer, and each frame is recorded with a unique time stamp.
The data were gathered in 40 individual experimental runs, each lasting 10–15 minutes. These time intervals, being roughly in the order of 2 × 10^{3} turnover times, ensured sufficient sampling of statistically independent trajectories. The turnover time is estimated as H/\(\langle {u}_{H}\rangle \), where for the high flow rate, \(\langle {u}_{H}\rangle =0.28\,{\rm{m}}\,{{\rm{s}}}^{1}\) is the mean (spatial and temporal) streamwise velocity at \(z=H\).
Post processing
We used the predictorcorrector 4frame steps tracking algorithm of OpenPTV^{61}, that has been successfully used in various turbulent flow applications^{26,62,63,64,65}. Due to the flow inhomogeneity, we followed a systematic routine when selecting the different tracking parameters at the different measurement subvolumes, similar to a previous report^{64}. In each region of the flow, we chose a subsample of the data and applied tracking with different tracking parameters. The parameters were then tuned until the estimated root mean square of velocity, the number of trajectories, and the length of trajectories reached a plateau. The software modifications are available from the open source repository, OpenPTV^{36}.
The use of realtime imaging precludes the usage of the spaceimage tracking algorithm^{66}, which requires access to the original images. Instead, we postprocessed the trajectories using the positionvelocity spacelinking algorithm^{67}. The trajectory data were filtered and differentiated using the standard method of OpenPTV^{63,68}. This included fitting a secondorder polynomial to a window of N frames for each threedimensional trajectory. The polynomial was also used to estimate first and secondorder time derivatives of the positions, i.e velocity and acceleration. The data at the edges of the trajectories, where the Nsized window cannot be fitted symmetrically, were discarded. The cutoff frequency of this lowpass filter was estimated to be \({f}_{c}=150\,{\rm{Hz}}\). Postprocessing was conducted using the OpenPTV postprocessing package, called Flowtracks^{68}. It includes operations such as storing, handling and manipulating capabilities of vast amounts of data through a sophisticated application of the HDF5 format.
Data Availability
The processed datasets from the current study can be obtained from the corresponding author.
References
Britter, R. E. & Hanna, S. R. Flow and dispersion in urban areas. Annual Reviews in Fluid Mechanics 35, 469–496, https://doi.org/10.1146/annurev.fluid.35.101101.161147 (2003).
Finnigan, J. Turbulence in plant canopies. Annual Review of Fluid Mechanics 32, 519–571, https://doi.org/10.1146/annurev.fluid.32.1.519 (2000).
Harman, I. N., Böhm, M., Finnigan, J. J. & Hughes, D. Spatial variability of the flow and turbulence within a model canopy. BoundaryLayer Meteorology 160, 375–396, https://doi.org/10.1007/s1054601601500 (2016).
Patton, E. G. & Finnigan, J. J. Canopy turbulence. In Fernando, H. J. S. (ed.) Handbook of Environmental Fluid Dynamics, vol. 1, chap. 24, 311–327 (CRC Press, 2013).
Wilson, J. & Sawford, B. Review of lagrangian stochastic models for trajectories in the turbulent atmosphere. BoundaryLayer Meteorology 78, 191–210, https://doi.org/10.1007/BF00122492 (1996).
Pope, S. B. Turbulent Flows. (Cambridge University Press, 2000).
Counihan, J. Wind tunnel determination of the roughness length as a function of the fetch and the roughness density of threedimensional roughness elements. Atmospheric Environment 5, 637–642, https://doi.org/10.1016/00046981(71)90120X (1971).
Raupach, M. R., Thom, A. S. & Edwards, I. A windtunnel study of turbulent flow close to regularly arrayed rough surfaces. BoundaryLayer Meteorology 18, 373–397, https://doi.org/10.1007/BF00119495 (1980).
Shaw, R. H., Brunet, Y., Finnigan, J. J. & Raupach, M. R. A wind tunnel study of air flow in waving wheat: Twopoint velocity statistics. BoundaryLayer Meteorology 76, 349–376, https://doi.org/10.1007/BF00709238 (1995).
Macdonald, R. W. Modelling the mean velocity profile in the urban canopy layer. BoundaryLayer Meteorology 97, 25–45, https://doi.org/10.1023/A:1002785830512 (2000).
Ghisalberti, M. & Nepf, H. M. Mixing layers and coherent structures in vegetated aquatic flows. Journal of Geophysical Research: Oceans 107, 3011, https://doi.org/10.1029/2001JC000871 (2002).
Cheng, H. & Castro, I. Near wall flow over urbanlike roughness. BoundaryLayer Metrology 104, 229–259, https://doi.org/10.1023/A:1016060103448 (2002).
KastnerKlein, P. & Rotach, M. W. Mean flow and turbulence characteristics in an urban roughness sublayer. BoundaryLayer Meteorology 111, 55–84, https://doi.org/10.1023/B:BOUN.0000010994.32240.b1 (2004).
Poggi, D., Porporato, A., Ridolfi, L., Albertson, J. D. & Katul, G. G. The effect of vegetation density on canopy sublayer turbulence. BoundaryLayer Meteorology 111, 565–587, https://doi.org/10.1023/B:BOUN.0000016576.05621.73 (2004).
Castro, I. P. et al. Measurements and computations of flow in an urban street system. BoundaryLayer Meteorology 162, 207–230, https://doi.org/10.1007/s1054601602007 (2017).
Di Bernardino, A., Monti, P., Leuzzi, G. & Querzoli, G. Waterchannel estimation of eulerian and lagrangian time scales of the turbulence in idealized twodimensional urban canopies. BoundaryLayer Meteorol., https://doi.org/10.1007/s1054601702786 (2017).
Addepalli, B. & Pardyjak, E. R. A study of flow fields in stepdown street canyons. Environmental Fluid Mechanics 15, 439–481, https://doi.org/10.1007/s106520149366z (2015).
Moltchanov, S., BohbotRaviv, Y. & Shavit, U. Dispersive stresses at the canopy upstream edge. BoundaryLayer Meteorol 139, 333–351, https://doi.org/10.1007/s1054601095820 (2011).
DezsoWeidinger, G., Stitou, A., van Beeck, M. L. & Riethmuller, J. Measurement of the turbulent mass flux with PTV in a street canyon. Journal of Wind Engineering 91, 1117–1131, https://doi.org/10.1016/S01676105(03)000540 (2003).
Gerdes, F. & Olivari, D. Analysis of pollutant dispersion in an urban street canyon. Journal of Wind Engineering and Industrial Aerodynamics 82, 105–124, https://doi.org/10.1016/S01676105(98)002165 (1999).
Monnier, B., Goudarzi, S. A., Vinuesa, R. & Wark, C. Turbulent structure of a simplified urban fluid flow studied through stereoscopic particle image velocimetry. BoundaryLayer Meteorology 166, 239–268, https://doi.org/10.1007/s1054601703039 (2018).
Raupach, M. R. Applying lagrangian fluid mechanics to infer scalar source distributions from concentration profiles in plant canopies. Agricultural and Forest Meteorology 47, 85–108, https://doi.org/10.1016/01681923(89)900890 (1989).
Castro, I. P., Cheng, H. & Reynolds, R. Turbulence over urbantype roughness: Deductions from windtunnel measurements. BoundaryLayer Meteorology 118, 109–131, https://doi.org/10.1007/s1054600557477 (2006).
DePaul, F. T. & Sheih, C. M. Measurements of wind velocities in a street canyon. Atmospheric Environment 20, 455–459, https://doi.org/10.1016/00046981(86)900855 (1986).
Dracos, T. ThreeDimensional Velocity and Vorticity Measuring and Image Analysis Technique: Lecture Notes from the short course held in Zurich, Switzerland. (Kluwer Academic Publisher, 1996).
Virant, M. & Dracos, T. 3d ptv and its application on lagrangian motion. Measurement 8, 1552–1593, https://doi.org/10.1088/09570233/8/12/017 (1997).
Sato, Y. & Yamamoto, K. Lagrangian measurement of fluidparticle motion in an isotropic turbulent field. Journal of fluid mechanics 175, 183–199, https://doi.org/10.1017/S0022112087000351 (1987).
Snyder, W. H. & Lumley, J. L. Some measurements of particle velocity autocorrelation function in a turbulent flow. Journal of Fluid Mechanics 48, 41–71, https://doi.org/10.1017/S0022112071001460 (1971).
Walpot, R. J. E., van der Geld, C. W. M. & Kuerten, J. G. M. Determination of the coefficients of langevin models for inhomogeneous turbulent flows by threedimensional particle tracking velocimetry and direct numerical simulation. Physics of Fluids 19, https://doi.org/10.1063/1.2717688 (2007).
Gerashchenko, S., Sharp, N. S., Neuscamman, S. & Warhaft, Z. Lagrangian measurements of inertial particle accelerations in a turbulent boundary layer. Journal of Fluid Mechanics 617, 255–281, https://doi.org/10.1017/S0022112008004187 (2008).
Stelzenmuller, N., Polanco, J. I., Vignal, L., Vinkovic, I. & Mordant, N. Lagrangian acceleration statistics in a turbulent channel flow. Physical Review Fluids 2, 054602, https://doi.org/10.1103/PhysRevFluids.2.054602 (2017).
Schanz, D., Gesemann, S. & Schröder, A. Shakethebox: Lagrangian particle tracking at high particle image densities. Experiments in Fluids 57, 70, https://doi.org/10.1007/s0034801621571 (2016).
Borer, D., Delbruck, T. & Rösgen, T. Threedimensional particle tracking velocimetry using dynamic vision sensors. Experiments in Fluids 58, 165, https://doi.org/10.1007/s0034801724525 (2017).
Chan, K.Y., Stich, D. & Voth, G. A. Realtime image compression for highspeed particle tracking. Review of Scientific Instruments 78, 023704, https://doi.org/10.1063/1.2536719 (2007).
Kreizer, M. & Liberzon, A. Threedimensional particle tracking method using fpgabased realtime image processing and fourview image splitter. Experiments in Fluids 50, 613–620, https://doi.org/10.1007/s0034801009643 (2011).
OpenPTV consortium. Open source particle tracking velocimetry (2014).
Raupach, M. R., Finnigan, J. J. & Brunet, Y. Coherent eddies and turbulence in vegetative canopies: The mixinglayer analogy. BoundaryLayer Meteorology 78, 351–382, https://doi.org/10.1007/BF00120941 (1996).
Ghisalberti, M. & Nepf, H. The structure of the shear layer in flows over rigid and flexible canopies. Environmental Fluid Mechanics 6, 277–301, https://doi.org/10.1007/s1065200600024 (2006).
Taylor, G. I. Diffusion by continuous movements. Proceedings of the London Mathematical Society, https://doi.org/10.1112/plms/s220.1.196 (1921).
Nepf, H. M. Drag, turbulence, and diffusion in flow through emergent vegetation. Water Resources Research 35, https://doi.org/10.1029/1998WR900069 (1999).
Finnigan, J. J., Shaw, R. H. & Patton, E. G. Turbulence structure above a vegetation canopy. Journal of Fluid Mechanics 637, 387–424, https://doi.org/10.1017/S0022112009990589 (2009).
Shaw, R. H. & Seginer, I. Calculation of velocity skewness in real and artificial plant canopies. Boundary Layer Meteorology 39, 315–332, https://doi.org/10.1007/BF00125141 (1987).
Yeung, P. K., Pope, S. B., Lamorgese, A. G. & Donzis, D. A. Acceleration and dissipation statistics of numerically simulated isotropic turbulence. Physics of Fluids 18, 065103, https://doi.org/10.1063/1.2204053 (2006).
Mordant, N., Crawford, A. M. & Bodenschatz, E. Threedimensional structure of the lagrangian acceleration in turbulent flows. Physical Review Letters 93, https://doi.org/10.1103/PhysRevLett.93.214501 (2004).
Monin, A. S. & Yaglom, A. M. Statistical Fluid Mechanics. (Dover Publications inc., 1972).
La Porta, A., Voth, G. A., Crawford, A. M., Alexander, J. & Bodenschatz, E. Fluid particle accelerations in fully developed turbulence. Nature 409, 1017, https://doi.org/10.1038/35059027 (2001).
Voth, G. A., La Porta, A., Crawford, A. M., Alexander, J. & Bodenschatz, E. Measurement of particle accelerations in fully developed turbulence. Journal of Fluid Mechanics 469, 121–160, https://doi.org/10.1017/S0022112002001842 (2002).
Mordant, N., Crawford, A. M. & Bodenschatz, E. Experimental lagrangian acceleration probability density function measurement. Physica D 193, 245–251, https://doi.org/10.1016/j.physd.2004.01.041 (2004).
Poggi, D. & Katul, G. G. Evaluation of the turbulent kinetic energy dissipation rate inside canopies by zero and levelcrossing density methods. BoundaryLayer Meteorology 136, 219–233, https://doi.org/10.1007/s1054601095032 (2010).
Voth, G. A., Satyanarayan, K. & Bodenschatz, E. Lagrangian accelertion measuremetns at large reynolds numbers. Physics of Fluids 10, 2268, https://doi.org/10.1063/1.869748 (1998).
Yeung, P. K. & Pope, S. B. Lagrangian statistics from direct numerical simulations of isotropic turbulence. Journal of Fluid Mechanics 207, 531–586, https://doi.org/10.1017/S0022112089002697 (1989).
Tennekes, H. & Lumley, J. L. A First Course in Turbulence. (The MIT Press, 1972).
Crawford, A. M., Mordant, N. & Bodenschatz, E. Joint Statistics of the Lagrangian Acceleration and Velocity in Fully Developed Turbulence. Physical Review Letters 94, 1–4 (2005).
Xia, H., Francois, N., Punzmann, H. & Shats, M. Lagrangian scale of particle dispersion in turbulence. Nature Communications 4, https://doi.org/10.1038/ncomms3013 (2013).
Finnigan, J., Harman, I., Ross, A. & Belcher, S. Firstorder turbulence closure for modelling complex canopy flows. Quarterly Journal of the Royal Meteorological Society 141, 2907–2916, https://doi.org/10.1002/qj.2577 (2015).
Sawford, B. L. Reynolds number effects in lagrangian stochastic models of turbulent dispersion. Physics of Fluids A: Fluid Dynamics 3, 1577, https://doi.org/10.1063/1.857937 (1991).
BohbotRaviv, Y. et al. Turbulence statistics of canopyflows using novel lagrangian measurements within an environmental wind tunnel. Physmod, LHEEADAUC, Ecole Cent. de Nantes (2017).
Melling, A. Tracer particles and seeding for particle image velocimetry. Measurement Science and Technology 8, 1406, https://doi.org/10.1088/09570233/8/12/005 (1997).
Scarano, F. et al. On the use of heliumfilled soap bubbles for largescale tomographic piv in wind tunnel experiments. Experiments in Fluids 56, 42, https://doi.org/10.1007/s0034801519097 (2015).
Schneiders, J. F. G., Scarano, F., Jux, C. & Sciacchitano, A. Coaxial volumetric velocimetry. Measurement Science and Technology 29, https://doi.org/10.1088/13616501/aab07d (2018).
Malik, N. A., Dracos, T. & Papantoniou, D. A. Particle tracking velocimetry in threedimensional flows part ii: Particle tracking. Experiments in Fluids 15, 279–294, https://doi.org/10.1007/BF00223406 (1993).
Ott, S. & Mann, J. An experimental investigation of the relative diffusion of particle pairs in threedimensional turbulent flow. Journal of Fluid Mechanics 422, 207–223, https://doi.org/10.1017/S0022112000001658 (2000).
Luthi, B., Tsinober, A. & Kinzelbach, W. Lagrangian measurment of vorticity dynamics in turbulent flow. Journal of Fluid Mechanics 528, 87–118, https://doi.org/10.1017/S0022112004003283 (2005).
Nimmo Smith, W. A. M. A submersible threedimensional particle tracking valocimetry system for flow visualzation in the coastal ocean. Limnology and Oceanography: Methods 6, 96–104, https://doi.org/10.4319/lom.2008.6.96 (2008).
Shnapp, R. & Liberzon, A. Generalization of turbulent pair dispersion to large initial separations. Phys. Rev. Lett. 120, 244502, https://doi.org/10.1103/PhysRevLett.120.244502 (2018).
Willneff, J. A spatiotemporal matching algorithm for 3D particle tracking velocimetry. Ph.D. thesis (ETH Zurich, 2003).
Xu, H. Tracking lagrangian trajectories in position–velocity space. Measurement Science and Technology 19 (2008).
Meller, Y. & Liberzon, A. Particle data management software for 3dparticle tracking velocimetry and related applications – the flowtracks package. Journal of Open Research Software, https://doi.org/10.5334/jors.101 (2016).
Acknowledgements
The authors are grateful to Meni Konn, Sabrina Shlain, Gregory Gulitski, Valery Babin, Amos Shick and Mordechai Hotovely, for their assistance in preparing and performing the wind tunnel experiment. We also thank Nicolas Mordant for comments on the preliminary draft. This study is supported by the PAZY grant number 2403170.
Author information
Authors and Affiliations
Contributions
A.L., Y.B.R. and E.F. conceived the experiment. All authors designed the experimental setup. R.S., A.L. and Y.B.R. conducted the experiment, R.S. analyzed the results. All authors contributed to and reviewed the manuscript.
Corresponding author
Ethics declarations
Competing Interests
Ron Shanpp, David Peri, Yardena BohbotRaviv, Eyal Fattal and Alex Liberzon have no conflicts to disclose. Erez Shapira is a partner at 1Vision Ltd.
Additional information
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary information
41598_2019_43555_MOESM1_ESM.pdf
Supplementary Material  Extended 3DPTV for direct measurements of Lagrangian statistics of canopy turbulence in a wind tunnel
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Shnapp, R., Shapira, E., Peri, D. et al. Extended 3DPTV for direct measurements of Lagrangian statistics of canopy turbulence in a wind tunnel. Sci Rep 9, 7405 (2019). https://doi.org/10.1038/s41598019435552
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41598019435552
This article is cited by

Flow and Turbulence Statistics of a TwoHeight Canopy Model in a Wind Tunnel
BoundaryLayer Meteorology (2023)

Quadrant Analysis of the Reynolds Shear Stress in a TwoHeight Canopy
Flow, Turbulence and Combustion (2023)

Experimental Investigation of Droplet Generation by PostBreaking Plunger Waves
Water Waves (2022)

Momentum and Turbulent Transport in Sparse, Organized Vegetative Canopies
BoundaryLayer Meteorology (2022)

Stereoscopic PIV measurements using lowcost action cameras
Experiments in Fluids (2021)
Comments
By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.