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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Unit 1

Motion in Two or Three Dimensions

SCIENTISTS IN ACTION

Echo Particle Image Velocimetry

The transport of mass, momentum, and energy in fluid flows is ultimately determined by spatiotemporal distributions of the fluid velocity field.1 Consequently, a prerequisite for understanding, predicting, and controlling fluid flows is the capability to measure the velocity field with adequate spatial and temporal resolution.2 For velocity measurements in optically opaque fluids or through optically opaque geometries, echo particle image velocimetry (EPIV) is an attractive diagnostic technique to generate "instantaneous" two-dimensional fields of velocity.3,4,5,6 In this paper, the operating protocol for an EPIV system built by integrating a commercial medical ultrasound machine7 with a PC running commercial particle image velocimetry (PIV) software8 is described, and validation measurements in Hagen-Poiseuille (i.e., laminar pipe) flow are reported.
For the EPIV measurements, a phased array probe connected to the medical ultrasound machine is used to generate a two-dimensional ultrasound image by pulsing the piezoelectric probe elements at different times. Each probe element transmits an ultrasound pulse into the fluid, and tracer particles in the fluid (either naturally occurring or seeded) reflect ultrasound echoes back to the probe where they are recorded. The amplitude of the reflected ultrasound waves and their time delay relative to transmission are used to create what is known as B-mode (brightness mode) two-dimensional ultrasound images. Specifically, the time delay is used to determine the position of the scatterer in the fluid and the amplitude is used to assign intensity to the scatterer. The time required to obtain a single B-mode image, t, is determined by the time it take to pulse all the elements of the phased array probe. For acquiring multiple B-mode images, the frame rate of the system in frames per second (fps) = 1/&delta;t. (See 9 for a review of ultrasound imaging.)
For a typical EPIV experiment, the frame rate is between 20-60 fps, depending on flow conditions, and 100-1000 B-mode images of the spatial distribution of the tracer particles in the flow are acquired. Once acquired, the B-mode ultrasound images are transmitted via an ethernet connection to the PC running the PIV commercial software. Using the PIV software, tracer particle displacement fields, D(x,y)[pixels], (where x and y denote horizontal and vertical spatial position in the ultrasound image, respectively) are acquired by applying cross correlation algorithms to successive ultrasound B-mode images.10 The velocity fields, u(x,y)[m/s], are determined from the displacements fields, knowing the time step between image pairs, &Delta;T[s], and the image magnification, M[meter/pixel], i.e., u(x,y) = MD(x,y)/&Delta;T. The time step between images &Delta;T = 1/fps + D(x,y)/B, where B[pixels/s] is the time it takes for the ultrasound probe to sweep across the image width. In the present study, M = 77[&mu;m/pixel], fps = 49.5[1/s], and B = 25,047[pixels/s]. Once acquired, the velocity fields can be analyzed to compute flow quantities of interest.

An echo particle image velocimetry (EPIV) system capable of acquiring two-dimensional fields of velocity in optically opaque fluids or through optically opaque geometries is described, and validation measurements in pipe flow are reported.

The transport of mass, momentum, and energy in fluid flows is ultimately determined by spatiotemporal distributions of the fluid velocity field.1 ...

Process of Making Three-dimensional Microstructures using Vaporization of a Sacrificial Component

Vascular structures in natural systems are able to provide high mass transport through high surface areas and optimized structure. Few synthetic material fabrication techniques are able to mimic the complexity of these structures while maintaining scalability. The Vaporization of a Sacrificial Component (VaSC) process is able to do so. This process uses sacrificial fibers as a template to form hollow, cylindrical microchannels embedded within a matrix. Tin (II) oxalate (SnOx) is embedded within poly(lactic) acid (PLA) fibers which facilitates the use of this process. The SnOx catalyzes the depolymerization of the PLA fibers at lower temperatures. The lactic acid monomers are gaseous at these temperatures and can be removed from the embedded matrix at temperatures that do not damage the matrix. Here we show a method for aligning these fibers using micromachined plates and a tensioning device to create complex patterns of three-dimensionally arrayed microchannels. The process allows the exploration of virtually any arrangement of fiber topologies and structures.

The Vaporization of a Sacrificial Component (VaSC) process is used to fabricate microvascular structures. This procedure uses sacrificial poly(lactic) acid fibers to form hollow microchannels with precise 3D geometric positioning provided by laser micromachined guide plates.

Vascular structures in natural systems are able to provide high mass transport through high surface areas and optimized structure. Few synthetic material ...

High-speed Particle Image Velocimetry Near Surfaces

Multi-dimensional and transient flows play a key role in many areas of science, engineering, and health sciences but are often not well understood. The complex nature of these flows may be studied using particle image velocimetry (PIV), a laser-based imaging technique for optically accessible flows. Though many forms of PIV exist that extend the technique beyond the original planar two-component velocity measurement capabilities, the basic PIV system consists of a light source (laser), a camera, tracer particles, and analysis algorithms. The imaging and recording parameters, the light source, and the algorithms are adjusted to optimize the recording for the flow of interest and obtain valid velocity data. 
Common PIV investigations measure two-component velocities in a plane at a few frames per second. However, recent developments in instrumentation have facilitated high-frame rate (&gt; 1 kHz) measurements capable of resolving transient flows with high temporal resolution. Therefore, high-frame rate measurements have enabled investigations on the evolution of the structure and dynamics of highly transient flows. These investigations play a critical role in understanding the fundamental physics of complex flows.
A detailed description for performing high-resolution, high-speed planar PIV to study a transient flow near the surface of a flat plate is presented here. Details for adjusting the parameter constraints such as image and recording properties, the laser sheet properties, and processing algorithms to adapt PIV for any flow of interest are included.

A procedure for studying transient flows near boundaries using high-resolution, high-speed particle image velocimetry (PIV) is described here. PIV is a non-intrusive measurement technique applicable to any optically accessible flow by optimizing several parameter constraints such as the image and recording properties, the laser sheet properties, and analysis algorithms.

Multi-dimensional and transient flows play a key role in many areas of science, engineering, and health sciences but are often not well understood. The ...

Visualization of High Speed Liquid Jet Impaction on a Moving Surface

Two apparatuses for examining liquid jet impingement on a high-speed moving surface are described: an air cannon device (for examining surface speeds between 0 and 25 m/sec) and a spinning disk device (for examining surface speeds between 15 and 100 m/sec). The air cannon linear traverse is a pneumatic energy-powered system that is designed to accelerate a metal rail surface mounted on top of a wooden projectile. A pressurized cylinder fitted with a solenoid valve rapidly releases pressurized air into the barrel, forcing the projectile down the cannon barrel. The projectile travels beneath a spray nozzle, which impinges a liquid jet onto its metal upper surface, and the projectile then hits a stopping mechanism. A camera records the jet impingement, and a pressure transducer records the spray nozzle backpressure. The spinning disk set-up consists of a steel disk that reaches speeds of 500 to 3,000 rpm via a variable frequency drive (VFD) motor. A spray system similar to that of the air cannon generates a liquid jet that impinges onto the spinning disc, and cameras placed at several optical access points record the jet impingement. Video recordings of jet impingement processes are recorded and examined to determine whether the outcome of impingement is splash, splatter, or deposition. The apparatuses are the first that involve the high speed impingement of low-Reynolds-number liquid jets on high speed moving surfaces. In addition to its rail industry applications, the described technique may be used for technical and industrial purposes such as steelmaking and may be relevant to high-speed 3D printing.

Two experimental devices for examining liquid jet impingement on a high-speed moving surface are described: an air cannon device and a spinning disk device. The apparatuses are used to determine optimal approaches to the application of liquid friction modifier (LFM) onto rail tracks for top-of-rail friction control.

Two apparatuses for examining liquid jet impingement on a high-speed moving surface are described: an air cannon device (for examining surface speeds ...

Characterization of Nanocrystal Size Distribution using Raman Spectroscopy with a Multi-particle Phonon Confinement Model

Analysis of the size distribution of nanocrystals is a critical requirement for the processing and optimization of their size-dependent properties. The common techniques used for the size analysis are transmission electron microscopy (TEM), X-ray diffraction (XRD) and photoluminescence spectroscopy (PL). These techniques, however, are not suitable for analyzing the nanocrystal size distribution in a fast, non-destructive and a reliable manner at the same time. Our aim in this work is to demonstrate that size distribution of semiconductor nanocrystals that are subject to size-dependent phonon confinement effects, can be quantitatively estimated in a non-destructive, fast and reliable manner using Raman spectroscopy. Moreover, mixed size distributions can be separately probed, and their respective volumetric ratios can be estimated using this technique. In order to analyze the size distribution, we have formulized an analytical expression of one-particle PCM and projected it onto a generic distribution function that will represent the size distribution of analyzed nanocrystal. As a model experiment, we have analyzed the size distribution of free-standing silicon nanocrystals (Si-NCs) with multi-modal size distributions. The estimated size distributions are in excellent agreement with TEM and PL results, revealing the reliability of our model.

We demonstrate how to determine the size distribution of semiconductor nanocrystals in a quantitative manner using Raman spectroscopy employing an analytically defined multi-particle phonon confinement model. Results obtained are in excellent agreement with the other size analysis techniques like transmission electron microscopy and photoluminescence spectroscopy.

Analysis of the size distribution of nanocrystals is a critical requirement for the processing and optimization of their size-dependent properties. The ...

Development of an Experimental Setup for the Measurement of the Coefficient of Restitution under Vacuum Conditions

The Discrete Element Method is used for the simulation of particulate systems to describe and analyze them, to predict and afterwards optimize their behavior for single stages of a process or even an entire process. For the simulation with occurring particle-particle and particle-wall contacts, the value of the coefficient of restitution is required. It can be determined experimentally. The coefficient of restitution depends on several parameters like the impact velocity. Especially for fine particles the impact velocity depends on the air pressure and under atmospheric pressure high impact velocities cannot be reached. For this, a new experimental setup for free-fall tests under vacuum conditions is developed. The coefficient of restitution is determined with the impact and rebound velocity which are detected by a high-speed camera. To not hinder the view, the vacuum chamber is made of glass. Also a new release mechanism to drop one single particle under vacuum conditions is constructed. Due to that, all properties of the particle can be characterized beforehand.

The coefficient of restitution is a parameter that describes the loss of kinetic energy during collision. Here, a free-fall setup under vacuum conditions is developed to be able to determine the coefficient of restitution parameter for particles in micrometer range with high impact velocities.

The Discrete Element Method is used for the simulation of particulate systems to describe and analyze them, to predict and afterwards optimize their ...

Methods for Measuring the Orientation and Rotation Rate of 3D-printed Particles in Turbulence

Experimental methods are presented for measuring the rotational and translational motion of anisotropic particles in turbulent fluid flows. 3D printing technology is used to fabricate particles with slender arms connected at a common center. Shapes explored are crosses (two perpendicular rods), jacks (three perpendicular rods), triads (three rods in triangular planar symmetry), and tetrads (four arms in tetrahedral symmetry). Methods for producing on the order of 10,000 fluorescently dyed particles are described. Time-resolved measurements of their orientation and solid-body rotation rate are obtained from four synchronized videos of their motion in a turbulent flow between oscillating grids with R&#955; = 91. In this relatively low-Reynolds number flow, the advected particles are small enough that they approximate ellipsoidal tracer particles. We present results of time-resolved 3D trajectories of position and orientation of the particles as well as measurements of their rotation rates.

We use 3D printing to fabricate anisotropic particles in the shapes of jacks, crosses, tetrads, and triads, whose alignments and rotations in turbulent fluid flow can be measured from multiple simultaneous video images.

Experimental methods are presented for measuring the rotational and translational motion of anisotropic particles in turbulent fluid flows. 3D printing ...

Using Synchrotron Radiation Microtomography to Investigate Multi-scale Three-dimensional Microelectronic Packages

Synchrotron radiation micro-tomography (SR&#181;T) is a non-destructive three-dimensional (3D) imaging technique that offers high flux for fast data acquisition times with high spatial resolution. In the electronics industry there is serious interest in performing failure analysis on 3D microelectronic packages, many which contain multiple levels of high-density interconnections. Often in tomography there is a trade-off between image resolution and the volume of a sample that can be imaged. This inverse relationship limits the usefulness of conventional computed tomography (CT) systems since a microelectronic package is often large in cross sectional area 100-3,600 mm2, but has important features on the micron scale. The micro-tomography beamline at the Advanced Light Source (ALS), in Berkeley, CA USA, has a setup which is adaptable and can be tailored to a sample&#39;s properties, i.e., density, thickness, etc., with a maximum allowable cross-section of 36 x 36 mm. This setup also has the option of being either monochromatic in the energy range ~7-43 keV or operating with maximum flux in white light mode using a polychromatic beam. Presented here are details of the experimental steps taken to image an entire 16 x 16 mm system within a package, in order to obtain 3D images of the system with a spatial resolution of 8.7 &#181;m all within a scan time of less than 3 min. Also shown are results from packages scanned in different orientations and a sectioned package for higher resolution imaging. In contrast a conventional CT system would take hours to record data with potentially poorer resolution. Indeed, the ratio of field-of-view to throughput time is much higher when using the synchrotron radiation tomography setup. The description below of the experimental setup can be implemented and adapted for use with many other multi-materials.

For this study synchrotron radiation micro-tomography, a non-destructive three-dimensional imaging technique, is employed to investigate an entire microelectronic package with a cross-sectional area of 16 x 16 mm. Due to the synchrotron&#39;s high flux and brightness the sample was imaged in just 3 min&#160;with an 8.7 &#181;m spatial resolution.

Synchrotron radiation micro-tomography (SR&#181;T) is a non-destructive three-dimensional (3D) imaging technique that offers high flux for fast data ...

Three-Dimensional Ultrasonic Needle Tip Tracking with a Fiber-Optic Ultrasound Receiver

Ultrasound is frequently used for guiding minimally invasive procedures, but visualizing medical devices is often challenging with this imaging modality. When visualization is lost, the medical device can cause trauma to critical tissue structures. Here, a method to track the needle tip during ultrasound image-guided procedures is presented. This method involves the use of a fiber-optic ultrasound receiver that is affixed within the cannula of a medical needle to communicate ultrasonically with the external ultrasound probe. This custom probe comprises a central transducer element array and side element arrays. In addition to conventional two-dimensional (2D) B-mode ultrasound imaging provided by the central array, three-dimensional (3D) needle tip tracking is provided by the side arrays. For B-mode ultrasound imaging, a standard transmit-receive sequence with electronic beamforming is performed. For ultrasonic tracking, Golay-coded ultrasound transmissions from the 4 side arrays are received by the hydrophone sensor, and subsequently the received signals are decoded to identify the needle tip&#39;s spatial location with respect to the ultrasound imaging probe. As a preliminary validation of this method, insertions of the needle/hydrophone pair were performed in clinically realistic contexts. This novel ultrasound imaging/tracking method is compatible with current clinical workflow, and it provides reliable device tracking during in-plane and out-of-plane needle insertions.

Accurate and efficient visualization of invasive medical devices is extremely important in many ultrasound-guided minimally invasive procedures. Here, a method for localizing the spatial position of a needle tip relative to the ultrasound imaging probe is presented.

Ultrasound is frequently used for guiding minimally invasive procedures, but visualizing medical devices is often challenging with this imaging modality. ...

Simultaneous Measurement of Turbulence and Particle Kinematics Using Flow Imaging Techniques

Numerous problems in scientific and engineering fields involve understanding the kinematics of particles in turbulent flows, such as contaminants, marine micro-organisms, and/or sediments in the ocean, or fluidized bed reactors and combustion processes in engineered systems. In order to study the effect of turbulence on the kinematics of particles in such flows, simultaneous measurements of both the flow and particle kinematics are required. Non-intrusive, optical flow measurement techniques for measuring turbulence, or for tracking particles, exist but measuring both simultaneously can be challenging due to interference between the techniques. The method presented herein provides a low cost and relatively simple method to make simultaneous measurements of the flow and particle kinematics. A cross section of the flow is measured using a particle image velocimetry (PIV) technique, which provides two components of velocity in the measurement plane. This technique utilizes a pulsed-laser for illumination of the seeded flow field that is imaged by a digital camera. The particle kinematics are simultaneously imaged using a light emitting diode (LED) line light that illuminates a planar cross section of the flow that overlaps with the PIV field-of-view (FOV). The line light is of low enough power that it does not affect the PIV measurements, but powerful enough to illuminate the larger particles of interest imaged using the high-speed camera. High-speed images that contain the laser pulses from the PIV technique are easily filtered by examining the summed intensity level of each high-speed image. By making the frame rate of the high-speed camera incommensurate with that of the PIV camera frame rate, the number of contaminated frames in the high-speed time series can be minimized. The technique is suitable for mean flows that are predominantly two-dimensional, contain particles that are at least 5 times the mean diameter of the PIV seeding tracers, and are low in concentration.

The technique described herein offers a low cost and relatively simple method to simultaneously measure particle kinematics and turbulence in flows with low particle concentrations. The turbulence is measured using particle image velocimetry (PIV), and particle kinematics are calculated from images obtained with a high-speed camera in an overlapping field-of-view.

Numerous problems in scientific and engineering fields involve understanding the kinematics of particles in turbulent flows, such as contaminants, marine ...