Name: three-dimensional particle tracking velocimetry for turbulence applications: case of a jet flow
Uploaded: 2016-02-27T15:00:00
Description: Watch this Scientific Journal Video about Three-dimensional Particle Tracking Velocimetry for Turbulence Applications: Case of a Jet Flow at JoVE.com

This work was supported by the Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign, as part of the start-up package of Leonardo P. Chamorro.

1. Lab Safety
<ol>
	<li>Review the safety guidelines of the selected illumination source (e.g. laser, industrial LED, halogen). 
	Note: in this experiment, a set of five 250 Watts halogen spotlights are used as illumination. Basic safety and recommendation aspects for this light source are described as follows.
	<ol>
		<li>Avoid direct contact with halogen lights, which operate at high temperatures (~3,000 K color temperature).</li>
		<li>Keep the light ON only when acquiring data to avoid heating the flo...

<ol>
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P., Wilcox, N. A., Tatterson, G. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Application+of+image+processing+to+the+analysis+of+three-dimensional+flow+fields.">Application of image processing to the analysis of three-dimensional flow fields.</a> Opt Eng. 23, 283-287 (1984).</li><li>Racca, R., Dewey, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+method+for+automatic+particle+tracking+in+a+three-dimensional+flow+field.">A method for automatic particle tracking in a three-dimensional flow field.</a> Exp Fluids. 6, 25-32 (1988).</li><li>Maas, H. G., Gruen, D., Papantoniou, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Particle+tracking+velocimetry+in+three-dimensional+flows.">Particle tracking velocimetry in three-dimensional flows.</a> Exp Fluids. 15, 133-146 (1993).</li><li>Kasagi, N., Matsunaga, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Three-dimensional+particle+tracking+velocimetry+measurement+of+turbulence+statistics+and+energy+budget+in+a+backward-facing+step+flow.">Three-dimensional particle tracking velocimetry measurement of turbulence statistics and energy budget in a backward-facing step flow.</a> Int J Heat Fluid Fl. 16, 477-485 (1995).</li><li>Virant, M., Dracos, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=3D+PTV+and+its+application+on+Lagrangian+motion.">3D PTV and its application on Lagrangian motion.</a> Meas Sci Technol. 8, 1539 (1997).</li><li>Willneff, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> A spatio-temporal matching algorithm for 3 D particle tracking velocimetry. , (2003).</li><li>Rosi, G. A., Sherry, M., Kinzel, M., Rival, D. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Characterizing+the+lower+log+region+of+the+atmospheric+surface+layer+via+large-scale+particle+tracking+velocimetry.">Characterizing the lower log region of the atmospheric surface layer via large-scale particle tracking velocimetry.</a> Exp Fluid. 55, 1-10 (2014).</li><li>Fu, S., Biwole, P. H., Mathis, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Particle+Tracking+Velocimetry+for+indoor+airflow+field:+A+review.">Particle Tracking Velocimetry for indoor airflow field: A review.</a> Build Environ. 87, 34-44 (2015).</li><li>Kolaas, J., Jensen, A., Mielnik, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Visualization+and+measurements+of+flows+in+micro+silicon+Y-channels.">Visualization and measurements of flows in micro silicon Y-channels.</a> Eur Phys J E. 36, 1-11 (2013).</li><li>G&#252;lan, U., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Experimental+study+of+aortic+flow+in+the+ascending+aortavia+Particle+Tracking+Velocimetry.">Experimental study of aortic flow in the ascending aortavia Particle Tracking Velocimetry.</a> Exp Fluids. 53, 1469-1485 (2012).</li><li>Mei, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Velocity+fidelity+of+flow+tracer+particles.">Velocity fidelity of flow tracer particles.</a> Exp Fluids. 22, 1-13 (1996).</li><li>Tropea, C., Yarin, A. L., Foss, J. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Springer handbook of experimental fluid mechanics. 1, (2007).</li><li>Melling, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Tracer+particles+and+seeding+for+particle+image+velocimetry.">Tracer particles and seeding for particle image velocimetry.</a> Meas Sci Technol. 8, 1406 (1997).</li><li>Hering, F., Leue, C., Wierzimok, D., J&#228;hne, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Particle+tracking+velocimetry+beneath+water+waves.+Part+I:+visualization+and+tracking+algorithms.">Particle tracking velocimetry beneath water waves. Part I: visualization and tracking algorithms.</a> Exp Fluids. 23, 472-482 (1997).</li><li>Biferale, L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Lagrangian+structure+functions+in+turbulence:+A+quantitative+comparison+between+experiment+and+direct+numerical+simulation.">Lagrangian structure functions in turbulence: A quantitative comparison between experiment and direct numerical simulation.</a> Phys Fluids. 20, 065103 (2008).</li><li>L&#252;thi, B., Tsinober, A., Kinzelbach, W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Lagrangian+measurement+of+vorticity+dynamics+in+turbulent+flow.">Lagrangian measurement of vorticity dynamics in turbulent flow.</a> J Fluid mech. 528, 87-118 (2005).</li><li>Hoyer, K., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=3d+scanning+particle+tracking+velocimetry.">3d scanning particle tracking velocimetry.</a> Exp Fluids. 39, 923-934 (2005).</li><li>Kim, J. -. 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3D-PTV has great potential to unravel the complex physics of a variety of turbulent flows such as large-scale turbulent motions in the lower atmosphere25, indoor air distribution26, or pulsatile flows in aortic topology28 among many others. However, an understanding of its advantages and limitations as well as experience is essential to maximize its potential. Trial and error preliminary testing and exhaustive iterations for optimum settings, including frame rate, illumination source, G&#...

Turbulent jet flows are ubiquitous in engineering applications. Detailed characterization of such flows is crucial in a wide spectrum of practical problems spanning from large-scale environmental discharge systems to electronic micro-scale devices. Because of its impact on a number of broad applications, jet flows have been studied in depth1-4. Several experimental techniques, including hotwire anemometry4-8, Laser Doppler Velocimetry (LDV)4,9-12, and Particle Image Velocimetry (PIV)12-16, have been used to characterize jet flows in a wide range of Reynolds numbers and boundary conditions. Recently, a few studies have been made using 3D-PTV to study the turbulent/non-turbulent interface of jet flows17,18. 3D-PTV is a technique especially suitable to describe complex turbulent fields from a different perspective. It allows the reconstruction of particle trajectories within a volume in a Lagrangian frame of reference using multi-view stereoscopy. The technique was first introduced by Chang19 and further developed by Racca and Dewey20. Since then, many improvements have been made on the 3D-PTV algorithm and experimental setup21-24. With these accomplishments and previous works, the system has been successfully used to study various fluid phenomena such as large-scale fluid motion in a domain of 4 m x 2 m x 2 m25, indoor airflow field26, pulsatile flows27 and aortic blood flow28.
The working principle of a 3D-PTV measurement consists of data acquisition system set-up, recording/pre-processing, calibration, 3D correspondences, temporal tracking and post-processing. An accurate calibration allows for a precise detection of particle positions. The correspondence of the particles detected in more than three image views allows for the reconstruction of a 3D particle position based on the epipolar geometry. A linkage from consecutive image frames result in a temporal tracking that defines the particle trajectories s(t). Optimization of the 3D-PTV system is essential to maximize the likelihood of multi-particle traceability.
First step of the optimization is to acquire an appropriate data acquisition system including high-speed cameras, illumination source and features of seeding particles. The camera resolution along with the size of the interrogation volume defines the pixel size and, therefore, the required seeding particle size, which should be larger than a single pixel. The centroids of detected particles are estimated with sub-pixel accuracy by taking the average position of particle pixels weighted by brightness21. The camera&#39;s frame rate is closely associated with Reynolds number and the ability to link detected particles. A higher frame rate allows for resolving faster flows or a larger number of particles since the tracking becomes more difficult when the mean displacement between images exceeds the mean separation of the particles.
Shutter speed, aperture and sensitivity are three factors to consider in the image capture. Shutter speed should be fast enough to minimize blurring around a particle, which reduces uncertainty of particle centroid position. Camera aperture should be adjusted to the depth of field of the interrogation volume to reduce the probability of detecting particles outside the volume. Since the maximum sensitivity of a camera is fixed, as the frame rate increases, the necessary light required to illuminate the particles should increase accordingly. Unlike PIV, complex optic settings and high-power lasers are not strictly required in 3D-PTV, as long as the light source is sufficiently scattered from the tracer particles to the camera. Continuous LED or halogen lights are good cost-effective options that bypass the need of synchronization21.
In 3D-PTV, like other optical flow measurement techniques, tracer particle velocity is assumed to be the local instantaneous fluid velocity29. However, this is only the case for ideal tracers of null diameter and inertia; tracer particles should be large enough to be captured by a camera. The fidelity of a finite particle can be determined by the Stokes number St, i.e. the ratio of the relaxation time scale of particles and the time scale of turbulent structures of interest. In general, St should be substantially smaller than 1. For St&#160; &#8804;0.1 flow tracking errors are below 1%30. In-depth discussion can be found in Mei et al.29-31. Recommended particle size for a 3D-PTV experiment varies depending on the light source and camera sensitivity. With halogen or LED lights as illumination sources, relatively larger particles are used (e.g. 50-200 &#181;m)32, whereas smaller particles (e.g. 1-50 &#181;m)33,34 can be used with a high power laser (e.g. 80-100 Watts CW laser). Particles with high reflectivity for a given wavelength light, like silver coated under halogen light, can amplify their mark into an image. The seeding density is another important parameter for a successful 3D-PTV measurement. Few particles result in low number of trajectories, while an excessive number of particles cause ambiguities in establishing correspondences and tracking. Ambiguities in establishing correspondences include overlapping particles and detecting multiple candidates along the defined epipolar line. In the tracking process, the ambiguity due to a high seeding density is occurred because of the relatively short mean separation of particles.
Second step is optimal settings in recording/pre-processing to enhance the image quality. Photographic settings, such as gain &#38; black level (G&#38;B), play an important role in optimizing the image quality. Black level defines the brightness level at the darkest part of an image, whereas gain amplifies the brightness of an image. Slight variations of the G&#38;B levels can significantly impact the likelihood of traceability. In fact, high G&#38;B may over-brighten an image and eventually damage the camera sensor. To illustrate this, the impact of G&#38;B levels on the flow reconstruction is also examined in this article. In the pre-processing step, the images are filtered with a high-pass filter to emphasize light scatter from particles. The pixel size and gray scale are adjusted to maximize the particle detection within the interrogation volume.
Third step of the optimization is accurate calibration of the stereoscopic imaging, which is based on epipolar geometry, camera parameters (focal length, principle point, and distortion coefficients), and refractive index changes. This process is essential to minimize the 3D reconstruction error of the fiducial target points. Epipolar geometry uses relative distances (between camera and interrogation volume) and tilted angle from the target image. Refractive index changes along the camera view through the interrogation volume can be taken into account based on the procedure of Mass et al.21. In this experiment, a 3D stair-like structure with regularly distributed target points is used as a target.
In a 3D-PTV experiment, although only two images are needed to determine a 3D particle position, typically more cameras are used to reduce ambiguities21. An alternative to expensive setups with multiple high-speed cameras is the view splitter, proposed by Hoyer et al.35 for the use of 3D-PTV and recently applied by Gulean et al.28 for the biomedical applications. The view splitter consists of a pyramid-shaped mirror (hereon primary mirror) and four adjustable mirrors (hereon secondary mirror). In this work, a four-view splitter and a single camera were used to mimic the stereoscopic imaging from four cameras. The system is used to characterize the intermediate flow field of a pipe jet with a diameter, dh = 1 cm and Re &#8776; 7,000 from a Lagrangian and Eulerian frames at around 14.5-18.5 diameters downstream from the jet origin.

<table border="0" cellpadding="0" cellspacing="0" width="749">
	<tbody>
		<tr height="21">
			<td height="21" style="height:21px;width:223px;">ImageOps</td>
			<td style="width:119px;">CAMMC4082</td>
			<td style="width:407px;">High-speed camera</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:223px;">ImageOps</td>
			<td style="width:119px;">FBD-4XCXP6</td>
			<td>Frame Grabber</td>
		</tr>
		<tr height="24">
			<td height="24" style="height:24px;width:223px;">Potters Industries LLC</td>
			<td style="width:119px;">AG-SL150-30-TRD</td>
			<td>Seeding Paritcles</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;width:223px;">Upstate Technical Equipment CO.,INC</td>
			<td style="width:119px;">MISNOR-STP-6-S-CL</td>
			<td>Camera appliation</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:223px;">Photrack AG</td>
			<td style="width:119px;"></td>
			<td>Customized part and necessary if performing 3D-PTV with one camera</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:223px;">General Electrics&#160;</td>
			<td style="width:119px;">23719</td>
			<td>Light source</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:223px;">OpenPTV(http://www.openptv.net)</td>
			<td style="width:119px;"></td>
			<td>Open source particle tracking software (Note: available as a service for anyone who wants to use it without all the installation mess or computer power availability problems).</td>
		</tr>
	</tbody>
</table>

three-dimensional particle tracking velocimetry for turbulence applications: case of a jet flow

3D-PTV is a quantitative flow measurement technique that aims to track the Lagrangian paths of a set of particles in three dimensions using stereoscopic recording of image sequences. The basic components, features, constraints and optimization tips of a 3D-PTV topology consisting of a high-speed camera with a four-view splitter are described and discussed in this article. The technique is applied to the intermediate flow field (5 &#60;x/d &#60;25) of a circular jet at Re &#8776; 7,000. Lagrangian flow features and turbulence quantities in an Eulerian frame are estimated around ten diameters downstream of the jet origin and at various radial distances from the jet core. Lagrangian properties include trajectory, velocity and acceleration of selected particles as well as curvature of the flow path, which are obtained from the Frenet-Serret equation. Estimation of the 3D velocity and turbulence fields around the jet core axis at a cross-plane located at ten diameters downstream of the jet is compared with literature, and the power spectrum of the large-scale streamwise velocity motions is obtained at various radial distances from the jet core.

A photograph and a schematic of the setup are shown in Figures 1 and 2. The calibration target, the fiducial marks reflected on the view-splitter and 3D calibration reconstruction are illustrated in Figure 3. The RMS of the recognized calibration targets is 7.3 &#181;m, 5.7 &#181;m and 141.7 &#181;m in the streamwise x, spanwise y, and depth z directions. The relative higher RMS in the z-coordina...

Watch this Scientific Journal Video about Three-dimensional Particle Tracking Velocimetry for Turbulence Applications: Case of a Jet Flow at JoVE.com

Three-dimensional Particle Tracking Velocimetry for Turbulence Applications: Case of a Jet Flow

A three-dimensional particle tracking velocimetry (3D-PTV) system based on a high-speed camera with a four-view splitter is described here. The technique is applied to a jet flow from a circular pipe in the vicinity of ten diameters downstream at Reynolds number Re &#8776; 7,000.

3D-PTV is a quantitative flow measurement technique that aims to track the Lagrangian paths of a set of particles in three dimensions using stereoscopic ...

The goals of this experiment are to set up and optimize a particle tracking velocimetry system for turbulence applications and use it to study jet flow. This method can help to answer key questions in the turbulence field using Lagrangian description of the flow. The main advantage of this techniques is its ability to track a large set of particles in time and space.Though this method can provide insight into a turbulence and Lagrangian frame of reference it can be applied to other application such as motion capture or motion tracking. The set up for this experiment is for the study of the intermediate flow field of a pipe jet. This water filled tank has within it a flume to create a jet with the diameter of one centimeter.This schematic provides an overview of the setup before the addition of equipment for three dimensional particle tracking velocimetry. To prepare for particle tracking, identify a region of study, the interrogation volume. In this experiment it is a cube with its faces parallel to those of the larger volume and its closest point 15 centimeters from the end of the flume.Once the interrogation volume is chosen, begin with positioning the primary mirror. The mirror is pyramid shaped and mounted on a vertical mounting post along the side of the experiment tank. Slide the mirror along the post to level it with the center of the interrogation volume then fix it in place.Next, mount the camera behind the primary mirror. Set the center of the camera image to be coincident with the center of the primary mirror. Now, move to the computer to check its connection with the camera.Continue by setting up the interface between the camera and recording software. Begin with the camera view of the primary mirror and its surroundings. Use the software's region of interest setting to adjust the width and height of the camera view to just cover the primary mirror.The next step is to place a calibration target in the tank to set up the secondary mirrors. This target is custom designed to encompass the entire interrogation volume. The target has one millimeter diameter target points for calibration.First, place an adjustable height platform into the tank to support the target. Position the calibration target on the platform and orient it to face the camera. Adjust the height of the platform so as to match the center of the calibration target with the center of the calibration volume.Proceed by completing the four view splitter. Adjust the position of the primary mirror so that it captures the full interrogation volume. The secondary mirrors are mounted on vertical mounting posts on either side of the primary mirror.Mount each secondary mirror so that it is roughly aligned with the camera view from the closest side of the primary mirror. Secure the secondary mirror in place. The final arrangement should be geometrically symmetric with respect to the primary mirror.Use a laser pointer and direct it onto a mirror in turn to visualize the image path. Make final adjustments to the secondary mirror using the beam path as a guide so the view encompasses the entire calibration target. Repeat the visualization of the image path and make adjustments for each of the remaining secondary mirrors.After all mirrors have been adjusted, monitor the sub-images of the camera view. At the computer, observe the camera view of the images. At the view splitter, move one of the secondary mirrors to investigate sub-image overlap.If only one view changes as the mirror moves there is negligible overlap. With the four way splitter in place, turn off the camera and move on to work with the lighting. Place light sources directly facing the interrogation volume from above.In this experiment a magnifying lens is put in position directly under the light sources to enhance light intensity. Turn the light sources on and visually check that light is uniformly distributed over the entire interrogation volume. Begin optimizing the set up by returning to work with the camera.Adjust the lens focus until the reflection through the primary mirror is equally focused in all four views of the secondary mirrors. Ensure the images from the view splitter are symmetric and show the interrogation volume. Move on to adjust the f-stop to be able to capture both the closest and furthest point on the calibration target.The camera will only capture tracer particles in the interrogation volume. At this point, turn to the recording software. Set the desired frame rate.550 Hertz is used for this experiment. Then maximize the light sensitivity in accordance with the chosen frame rate. Now, prepare to take calibration images of the calibration target.Attach an LED flashlight to the tank directly above the target for illumination. Capture several images of the target via the four view splitter for later calibration steps. When done remove the calibration target, its support stand and the light in order to begin adjusting the illumination.Prepare a small quantity of seeding particles for optimizing the illumination. Get a small beaker containing water and add the seeding particles to it. Wait for the particles to diffuse.The particles are 100 micrometer silver-coated hollow ceramic spheres. Pour the mixture into the tank to assist with optimizing the illumination and camera settings. Continue with the room lights off and the light sources on.On the computer, monitor the live camera view of the primary mirror and check the illumination in each view by observing the particle density difference between views. The geometry in this experiment causes the top two mirrors to receive less light. At the setup remedy this by adding a flat mirror below the light source at the bottom of the tank.Under the light sources this addition helps reduce the variation across the view. At the camera adjust the gain and black levels each in the range of about zero to 500. The goal is to better capture light scatter from the particles.In this sequence, three stills using different gain and black levels are compared. A gain setting of 300 and a black setting of 500 was used in this experiment. Turn off the light sources and turn on the room lighting.At the computer open software for particle tracking velocimetry. The idea is to load one of the calibration images taken previously. Use the software to divide this image into four independent sub-images that correspond to the different secondary mirror views.Save each of these sub-images to a different file. Each image will be used as if from a separate camera. Proceed with calibration steps using coordinate data and steps for multiple cameras.Prepare for data collection by turning on the velocimetry light sources. Next, get the seeding particles and add them to the tank. Wait for steady-state flow to develop in the system.With the room lights off monitor the camera view to verify the correct particle seeding density as in this slowed video of jet flow. When satisfied, record the desired number of flow images. After the data has been collected begin data processing by dividing the collected images into four sub-images and storing them.With this done, the open PTV screen appears with menu choices across the top and a display for different camera views at the right. Move to the start tab in the menu items and click to reveal the options. Click image restart to load the four camera views of the first raw image in the data.Move the mouse cursor to the run directory and click on it. From there click main parameters. On the new window go to the general tab.Enter the number of cameras, in this case four. Continue entering parameters under the other tabs. When done, move the mouse to click on the preprocess menu item.There click high pass filter. The result is to intensify the light scattering in the four images. Also under the preprocess menu item, find and click on particle detection to determine the centroid of each detected particle.Then again under the preprocess menu item, click correspondences to establish stereoscopic correspondences. Next, move to the 3D positions menu item. Under it select 3D positions to determine the three dimensional position of detected particles based on the calibration.Under the sequence menu item find and click sequence without display to repeat the preprocessing steps for all of the collected image sequences. Now, enter the parameters to be used to identify particles for tracking. Continue by selecting the tracking menu item.From there choose tracking without display to correlate adjacent frames. And choose detected particles to visualize 3D reconstructed particles. When the tracking is complete go to the tracking menu item and select it.Click show trajectories to visualize trajectories in each camera view. Each of the camera views now has the particle trajectories depicted. In addition, a data file has been created for post-processing.This is a sample of three representative particle trajectories in the intermediate field region around the plane at 16 times the pipe jet diameter. When the distance from the jet core is small, longer trajectories are observed in the one second time interval of these data. As the distance from the jet core increases, the trajectory length decreases.At three times the pipe jet diameter the tracer particles exhibit short and complex trajectories. Here are all of the successfully reconstructed particle trajectories that cross the same plane. The distribution of velocities ranges from zero to 60%of the jet's exit velocity.Low velocities are in the blue range of the spectrum and high velocities are in the red range. Analysis reveals that particles near the jet core exhibit smoother trajectories. Particle tracking allows the calculation of the curvature along the particle trajectory.This is a plot of the mean curvature times the pipe jet diameter versus the scaled radial distance for two planes. One at 16 and the other at 17 pipe jet diameters. Both increase monotonically but the plane further from the pipe outlet has a reduced mean curvature outside the jet core.This plot provides information on the mean streamwise velocity distribution of the jets in the plane's 16 pipe jet diameters beyond the pipe outlet. Once mastered this technique can be done in six hours if it is performed properly excluding the processing time using the software. While attempting this procedure it is important to remember to have uniform illumination, correct camera settings and a sufficient amount of seeding particles.After watching this video you should have a good understanding of how to set up the view splitter, assemble the light source, control the camera parameters and optimize the PTV system. After its development this technique paved the way for researchers in the field of turbulence to explore experimental studies in Lagrangian flow dynamics.

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