Name: three-dimensional particle tracking velocimetry for turbulence applications: case of a jet flow
Uploaded: 2016-02-27T15:00:00.000+00:00
Duration: 13 min 2 s
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 flow under consideration.</li>
		<li>Keep away all burnable materials near the light source, including paper of any kind.</li>
	</ol>
	</li>
</ol>
2. Experimental Set-up
<ol>
	<li>Choose the Appropriate Lens
	<ol>
		<li>Choose a lens with low aberration to avoid calibration problems. Recommended lens types are telephoto or micro lenses.</li>
		<li>Make sure that the lens covers the desired field-of-view (FOV) within the object distance, O, by estimating the needed magnification, M. 
		Note: the magnification is the ratio of the length of the camera chip to the FOV, and the object distance can be computed as O = f(1/M + 1), where f is the focal length of the lens. In this experiment, the length of the camera chip is 20.34 mm and the corresponding FOV, or the primary mirror, is 50 mm with the limited object distance O &#8804;250 mm. (The object distance is restrained due to the finite length of the slider where the camera and the view splitter are mounted.) The magnification is M = 20.34/50 = 0.41, and approximate focal length with the given range of the object distance is f &#8804;72.7 mm. Thus, a micro lenses with a focal length of 60 mm is used with a focal ratio of f/2.8D.</li>
	</ol>
	</li>
	<li>Mount and Adjust the Camera with the View Splitter.
	<ol>
		<li>Level the center of the primary mirror with that of the interrogation volume by sliding the mirror along the vertical mounting post and fixing the mirror with the post holder. Note that this step is performed before installing the secondary mirrors.</li>
		<li>Mount the camera and set the center of image coincident with the center of the primary mirror.</li>
		<li>Adjust the width and height of the camera view to just cover the primary mirror by controlling the region of interest (ROI) setting in the recording software. This process is to reduce image size and image noise. Note: in this experiment, the size of the primary mirror is 5 x 5 cm2 (1,728 x 1,728 pixels).</li>
		<li>Design a (customized) 3D calibration target. It should enclose the whole investigation volume. Ensure that each view of the splitter captures all the target marks to allow a uniform calibration. 
		Note: in this demonstration, the target was 3D Printed using vero back plastic. It has a stair-like shape with dimension 35 x 35 x 30 mm3, with 1 mm diameter white target points separated 2.5 mm, 5 mm and 10 mm in the vertical, streamwise and spanwise directions. Accurate geometry of the target is crucial as it projects into a calibration model and position of the cameras with respect to the flume.</li>
	</ol>
	</li>
	<li>Place the Calibration Target into the Interrogation Volume.
	<ol>
		<li>Position the calibration target on an adjustable height platform facing towards to the camera.</li>
		<li>Match the height of the center of the calibration target with the center of the interrogation volume by adjusting the height of the target platform. 
		Note: in this example, the center mark of the calibration target is leveled with the center of the jet nozzle, 20 cm height. A bubble level gauge can be used to level the target.</li>
	</ol>
	</li>
	<li>Mount and Adjust the Secondary Mirrors of the Four-view Splitter.
	<ol>
		<li>Locate the primary mirror at a distance from the interrogation that ensures its full capture. It is 0.2 m for this demonstration (Figure 1).</li>
		<li>Mount secondary mirror at its approximate position, where the camera view from each side of the primary mirror is roughly aligned with a secondary mirror. Secure the secondary mirror by fixing it to the secondary mirror&#39;s vertical mounting post.</li>
		<li>Repeat this process for the other three mirrors. Check geometrical symmetry of all secondary mirrors with respect to the primary mirror.</li>
		<li>Make final adjustments by adjusting the mirror mount of the secondary mirrors to ensure that each of the four views encloses the entire calibration target. One efficient way to check the mirrors&#39; positions and angles is to use a laser pointer to visualize each view&#39;s image path.</li>
		<li>Check for overlapping of sub-images by moving one mirror. 
		Note: If only one view changes, then the overlapping region is negligible. Otherwise, repeat steps 2.4.2 to 2.4.5 until the overlapping region is minimized.</li>
	</ol>
	</li>
	<li>Place light source(s) directly facing the interrogation volume. Make sure to have the camera covered when adjusting the light to avoid damage on the camera sensor.
	<ol>
		<li>Check that the light source is uniformly distributed over the entire investigation volume.</li>
		<li>Enhance the light intensity, if necessary, by placing a magnifying lens directly under the light sources. Note: in this experiment, a plano-convex magnifying lens of focal distance f0 = 450 mm is used to intensify the illumination.</li>
	</ol>
	</li>
</ol>
3. Set-up Optimization
<ol>
	<li>Turn on and Adjust the Settings on the Camera to increase the Image Quality.
	<ol>
		<li>Adjust the lens magnification until the reflection through the primary mirror is equally focused in all four views of the secondary mirrors.</li>
		<li>Check that the images from the view splitter are symmetric and capture the interrogation volume by observing the symmetry of the calibration target image from the four views.</li>
		<li>Adjust the f-number to capture the closest and the furthest calibration target points from the camera. 
		Note: This allows the camera to capture tracer particles only in the depth of the interrogation volume. In this example, the f-number is 11.</li>
		<li>Set the desired frame rate as 550 Hz (it depends on particular application, see Introduction) and maximize the light sensitivity accordingly in the recording software.</li>
	</ol>
	</li>
	<li>Check illumination in each view of the primary mirror by observing the particle density difference in each view of the splitter through a live camera view. 
	Note: If multiple light sources are used to illuminate the interrogation volume, it is probable to have differences in each view of the splitter. In this experiment, the top two secondary mirrors received less light because the illumination comes from the top. The use of a flat mirror at the bottom of the flume can help to reduce the light variation across the views.</li>
	<li>Turn off the background lights in the room before using 3D-PTV light sources.</li>
	<li>Adjust the G&#38;B level of the camera to better capture the light scatter from the particles. Record several short sequences with various G&#38;B levels and find the optimum one by observing the distribution and density of particle trajectories. 
	Note: in this experiment, the range of G&#38;B level was 0-500, and black (B) level was set to 500, to brighten the dimmer light scatter, whereas, gain (G) was set medium, 300, to moderately amplify image signals and avoid over-brighten the image.</li>
</ol>
4. Calibration
<ol>
	<li>Place the calibration target in the investigation volume before adding tracer particles, and take few calibration images. Use a dimmer light source (e.g. LED flash light) to illuminate the target.</li>
	<li>Divide the calibration image into four independent sub-images and make a text file containing the reference coordinate positions of the target marks. OpenPTV software (http://www.openptv.net) is used here for this purpose. 
	Note: onward processing is identical to users employing a multiple camera set-up.</li>
	<li>Click &#39;Create calibration&#39; tab to start the calibration process after saving images and the text file obtained in step 4.2 in the &#39;Cal&#39; folder of the software.</li>
	<li>Click the &#39;Edit calibration parameters&#39; tab and choose the &#39;Calibration Orientation Parameters&#39; tab to define magnification, rotation angles and distance between the center of each split view and the origin of the calibration target. 
	Note: The first row is the distance from the origin calibration target to the camera sensor in the x,y,z direction. The second row indicates the angles, in radians, around the x,y,z axes. Next, a 3 by 3 data represents the rotation matrix. Then, the two following rows are the pinhole distances of the x and y axes from the image center (mm) and focal distance. The last row contains the position of the flume glass in respect to the origin target in the x,y,z direction.</li>
	<li>Click &#39;Detection&#39; and &#39;Show initial guess&#39; to investigate that the &#39;guess&#39; points are matched with the detected target points.</li>
	<li>Repeat step 4.4 for all four views until the &#39;guess&#39; points are aligned with the set of calibration images.</li>
	<li>Click &#39;Orientation&#39; to reconstruct the orientation of the interrogation volume. 
	Note: The calibration can be improved by adjusting the lens distortion and affine transformation. Now, the investigation volume is calibrated and ready to process data. See author&#39;s thesis36 for additional description the calibration process.</li>
</ol>
5. Flow Setting/Data Collection
<ol>
	<li>Estimate the maximum amount of particles captured in each camera view from the frame rate and maximum flow velocity. In this demonstration, the reference velocity is U &#8776; 0.4 m/sec, the frame rate is 550 Hz and ~4 x 4 x 4 cm3 interrogation volume. This resulted in ~1,000 particles per frame.</li>
	<li>Turn on the camera with the optimized settings obtained in step 3.</li>
	<li>Add seeding particles and wait several mean residence time to allow the flow reaching a steady state. Add more particles if needed but avoid high seeding density, estimated in the step 5.1, which may cause ambiguities. 
	Note: in this example, ~1.6 g of 100 m silver-coated hollow ceramic spheres of 1.1 g/cm3 density are used as seeding for the fluid medium (2 x 0.4 x 0.4 m3).</li>
	<li>Record the desired number of flow images. 
	Note: in this experiment, 9,000 images at 550 Hz were captured using the recording software. Repeat steps 2.4 to 5.3 if the camera and/or view splitter is moved (even a slight motion can heavily affect results).</li>
</ol>
6. Data Processing (Via OpenPTV Software)
<ol>
	<li>Divide the raw image obtained in step 5.4 into four independent sub-images.</li>
	<li>Click &#39;Init/restart&#39; under the &#39;Start&#39; tab to load initial images from four views.</li>
	<li>Right-click the &#39;Run&#39; directory and click &#39;Main Parameters&#39; to control number of cameras, refractive indices, particle recognition, number of sequence images, observation volume and criteria for correspondences.
	<ol>
		<li>Define the number of cameras (views) used for the experiment under the &#39;General&#39; tab. In this experiment, set the number of cameras as 4.</li>
		<li>Define refractive indices along the camera view under the &#39;Refractive Indices&#39; tab.</li>
		<li>Define the min and max numbers of pixel detection as well as gray value threshold to optimize the number of particle detection in all four views under the &#39;Particle recognition&#39; tab. The min and max numbers of pixel detection and gray threshold determine the pixel size and brightness level for particle detection. It eliminates noise and particles out of focus.</li>
		<li>Define the number of images to process under the &#39;Parameters for sequence processing&#39;.</li>
		<li>Define the observation volume under the &#39;Observation Volume&#39; tab.</li>
		<li>Define the correlation of correspondences under the &#39;Criteria for correspondences&#39; including total band parameter (mm) for stereo matching.</li>
	</ol>
	</li>
	<li>Click &#39;High pass filter&#39; under the &#39;Preprocess&#39; tab. This intensifies light scattering from particles in all four views.</li>
	<li>Click &#39;Particle Detection&#39; to determine the centroid of detected particles at sub-pixel level for all four views. Repeat steps 6.2 and 6.3 until the number of detected particles similar to the expected number of particles calculated in step 5.1.</li>
	<li>Click &#39;Correspondences&#39; to establish stereoscopic correspondences in each view. 
	Note: To reconstruct the 3D position of detected particles, correspondences should be determined at least from three views.</li>
	<li>Click &#39;3D positions&#39; to obtain detected particle 3D position based on the calibration.</li>
	<li>Click &#39;Sequence without display&#39; to repeat the process from steps 6.4 to 6.7 for all image sequences. 
	Note: This creates a &#39;rt_is&#39; file for each image set containing a summary of detected particles in the frame with a tab-separated text file format.</li>
	<li>Right-click the &#39;Run&#39; directory and click &#39;Tracking Parameters&#39; to define parameters of radius sphere, (e.g. dvxmin and dvymin in mm/frame), to search candidate particles for tracking.</li>
	<li>Click &#39;Tracking without display&#39; to define particle identification (ID) of reconstructed particles obtained in step 6.7. 
	Note: It correlates a sequence of adjacent frames for tracking using a four-frame predictor predictor-corrector scheme24. This process creates a ptv_is file for each image set which contains tracking information of detected particles in the frame; the first two columns show the particle ID in the previous frame and in the next frame, respectively.</li>
	<li>Click &#39;Show trajectories&#39; to visualize trajectories in each camera view.</li>
</ol>
7. Post Processing (Optative)
Note: the reach and type of post-processing depends on individual needs and it is, therefore, customizable. Here, point base calculations are briefly described as an example.
<ol>
	<li>Obtain Data in Lagrangian Frame (via Matlab).
	<ol>
		<li>Extract the 3D position of each particle and its associated ID from the ptv_is files. It allows linking detected particles among image sequences to reconstruct trajectories.</li>
		<li>Calculate velocity and acceleration of particles from the given frame rate for each trajectory. In this demonstration, the velocity and acceleration of particles are calculated by low-pass filtering the position signal with a moving cubic spline34,37.</li>
		<li>Make a struct array format with fields containing 3D positions, 3D velocities, 3D accelerations, and time stamp as well as trajectory ID of each trajectory. In this data format, the length of struct array represents number of trajectories.</li>
	</ol>
	</li>
	<li>Obtain Data in Eulerian Frame (via Matlab).
	<ol>
		<li>Transform the struct array (step 7.1.3) to a temporal one using the time stamp of each particle. This creates a similar struct array structure obtained in step 7.1.3, but the length of struct array now represents the frame numbers, which is 9,000 in this experiment.</li>
		<li>Interpolate the temporal struct array into a three dimensional grid for each time frame to obtain instantaneous velocity fields in Eulerian coordinates. In this demonstration, the griddata function in Matlab is used to perform the interpolation.</li>
	</ol>
	</li>
</ol>

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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. -. T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Three-dimensional particle tracking velocimetry for turbulence applications. , (2015).</li><li>L&#252;thi, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Some+aspects+of+strain,+vorticity+and+material+element+dynamics+as+measured+with+3D+particle+tracking+velocimetry+in+a+turbulent+flow.">Some aspects of strain, vorticity and material element dynamics as measured with 3D particle tracking velocimetry in a turbulent flow.</a> ETH Z&#252;rich. , (2002).</li><li>Pope, S. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Turbulent flows. , (2000).</li></ol>

The authors declare that they have no competing financial interest.

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><tbody><tr><td>Mikrotron 4CXP 4 lanes CXP-6 CoaXPress</td><td>ImageOps</td><td>CAMMC4082</td><td>High-speed camera</td></tr><tr><td>Active Silicon FireBird CoaX Frame Grabber</td><td>ImageOps</td><td>FBD-4XCXP6</td><td>Frame Grabber</td></tr><tr><td>100 &amp;mu;m silver-coated hollow ceramic spheres</td><td>Potters Industries LLC</td><td>AG-SL150-30-TRD</td><td>Seeding Paritcles</td></tr><tr><td>StreamPix6</td><td>Upstate Technical Equipment CO.,INC</td><td>MISNOR-STP-6-S-CL</td><td>Camera appliation</td></tr><tr><td>Four-view splitter</td><td>Photrack AG</td><td></td><td>Customized part and necessary if performing 3D-PTV with one camera</td></tr><tr><td>250 Watts Spotlight Halogen</td><td>General Electrics</td><td>23719</td><td>Light source</td></tr><tr><td>OpenPTV (Software)</td><td>OpenPTV (http://www.openptv.net)</td><td></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...

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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 ...

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12.1K Views.  University of Illinois at Urbana-Champaign. 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 ≈ 7,000.

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

Determining 3D Flow Fields via Multi-camera Light Field Imaging

In the field of fluid mechanics, the resolution of computational schemes has outpaced experimental methods and widened the gap between predicted and observed phenomena in fluid flows. Thus, a need exists for an accessible method capable of resolving three-dimensional (3D) data sets for a range of problems. We present a novel technique for performing quantitative 3D imaging of many types of flow fields. The 3D technique enables investigation of complicated velocity fields and bubbly flows. Measurements of these types present a variety of challenges to the instrument. For instance, optically dense bubbly multiphase flows cannot be readily imaged by traditional, non-invasive flow measurement techniques due to the bubbles occluding optical access to the interior regions of the volume of interest. By using Light Field Imaging we are able to reparameterize images captured by an array of cameras to reconstruct a 3D volumetric map for every time instance, despite partial occlusions in the volume. The technique makes use of an algorithm known as synthetic aperture (SA) refocusing, whereby a 3D focal stack is generated by combining images from several cameras post-capture 1. Light Field Imaging allows for the capture of angular as well as spatial information about the light rays, and hence enables 3D scene reconstruction. Quantitative information can then be extracted from the 3D reconstructions using a variety of processing algorithms. In particular, we have developed measurement methods based on Light Field Imaging for performing 3D particle image velocimetry (PIV), extracting bubbles in a 3D field and tracking the boundary of a flickering flame. We present the fundamentals of the Light Field Imaging methodology in the context of our setup for performing 3DPIV of the airflow passing over a set of synthetic vocal folds, and show representative results from application of the technique to a bubble-entraining plunging jet.

A technique for performing quantitative three-dimensional (3D) imaging for a range of fluid flows is presented. Using concepts from the area of Light Field Imaging, we reconstruct 3D volumes from arrays of images. Our 3D results span a broad range including velocity fields and multi-phase bubble size distributions.

In the field of fluid mechanics, the resolution of computational schemes has outpaced experimental methods and widened the gap between predicted and ...

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 ...

Image-based Lagrangian Particle Tracking in Bed-load Experiments

Image analysis has been increasingly used for the measurement of river flows due to its capabilities to furnish detailed quantitative depictions at a relatively low cost. This manuscript describes an application of particle tracking velocimetry (PTV) to a bed-load experiment with lightweight sediment. The key characteristics of the investigated sediment transport conditions were the presence of a covered flow and of a fixed rough bed above which particles were released in limited number at the flume inlet. Under the applied flow conditions, the motion of the individual bed-load particles was intermittent, with alternating movement and stillness terms. The flow pattern was preliminarily characterized by acoustic measurements of vertical profiles of the stream-wise velocity. During process visualization, a large field of view was obtained using two action-cameras placed at different locations along the flume. The experimental protocol is described in terms of channel calibration, experiment realization, image pre-processing, automatic particle tracking, and post-processing of particle track data from the two cameras. The presented proof-of-concept results include probability distributions of the particle hop length and duration. The achievements of this work are compared to those of existing literature to demonstrate the validity of the protocol.

The manuscript presents a protocol for the conduction of bed-load sediment transport experiments where the moving particles are tracked by image analysis. The experimental facility, the procedures for run realization and data processing, and finally some proof-of-concept results are presented here.

Image analysis has been increasingly used for the measurement of river flows due to its capabilities to furnish detailed quantitative depictions at a ...

Environment

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 ...

Micro-particle Image Velocimetry for Velocity Profile Measurements of Micro Blood Flows

Micro-particle image velocimetry (&mu;PIV) is used to visualize paired images of micro particles seeded in blood flows. The images are cross-correlated to give an accurate velocity profile. A protocol is presented for &mu;PIV measurements of blood flows in microchannels. At the scale of the microcirculation, blood cannot be considered a homogeneous fluid, as it is a suspension of flexible particles suspended in plasma, a Newtonian fluid. Shear rate, maximum velocity, velocity profile shape, and flow rate can be derived from these measurements. Several key parameters such as focal depth, particle concentration, and system compliance, are presented in order to ensure accurate, useful data along with examples and representative results for various hematocrits and flow conditions.

Micro-particle image velocimetry (&mu;PIV) is used to visualize paired images of micro particles seeded in blood flows which are cross-correlated to give an accurate velocity profile. Shear rate, maximum velocity, velocity profile shape, and flow rate, each of which has clinical applications, can be derived from these measurements.

Micro-particle image velocimetry (&mu;PIV) is used to visualize paired images of micro particles seeded in blood flows. The images are cross-correlated to ...

Bioengineering

Visually Based Characterization of the Incipient Particle Motion in Regular Substrates: From Laminar to Turbulent Conditions

Two different experimental methods for determining the threshold of particle motion as a function of geometrical properties of the bed from laminar to turbulent flow conditions are presented. For that purpose, the incipient motion of a single bead is studied on regular substrates that consist of a monolayer of fixed spheres of uniform size that are regularly arranged in triangular and quadratic symmetries. The threshold is characterized by the critical Shields number. The criterion for the onset of motion is defined as the displacement from the original equilibrium position to the neighboring one. The displacement and the mode of motion are identified with an imaging system. The laminar flow is induced using a rotational rheometer with a parallel disk configuration. The shear Reynolds number remains below 1. The turbulent flow is induced in a low-speed wind tunnel with open jet test section. The air velocity is regulated with a frequency converter on the blower fan. The velocity profile is measured with a hot wire probe connected to a hot film anemometer. The shear Reynolds number ranges between 40 and 150. The logarithmic velocity law and the modified wall law presented by Rotta are used to infer the shear velocity from the experimental data. The latter is of special interest when the mobile bead is partially exposed to the turbulent flow in the so-called hydraulically transitional flow regime. The shear stress is estimated at onset of motion. Some illustrative results showing the strong impact of the angle of repose, and the exposure of the bead to shear flow are represented in both regimes.

Two different methods for characterizing the incipient particle motion of a single bead as a function of the sediment bed geometry from laminar to turbulent flow are presented.

Two different experimental methods for determining the threshold of particle motion as a function of geometrical properties of the bed from laminar to ...

Experimental Investigation of the Flow Structure over a Delta Wing Via Flow Visualization Methods

It is well known that the flow field over a delta wing is dominated by a pair of counter rotating leading edge vortices (LEV). However, their mechanism is not well understood. The flow visualization technique is a promising non-intrusive method to illustrate the complex flow field spatially and temporally. A basic flow visualization setup consists of a high-powered laser and optic lenses to generate the laser sheet, a camera, a tracer particle generator, and a data processor. The wind tunnel setup, the specifications of devices involved, and the corresponding parameter settings are dependent on the flow features to be obtained.
Normal smoke wire flow visualization uses a smoke wire to demonstrate the flow streaklines. However, the performance of this method is limited by poor spatial resolution when it is conducted in a complex flow field. Therefore, an improved smoke flow visualization technique has been developed. This technique illustrates the large-scale global LEV flow field and the small-scale shear layer flow structure at the same time, providing a valuable reference for later detailed particle image velocimetry (PIV) measurement.
In this paper, the application of the improved smoke flow visualization and PIV measurement to study the unsteady flow phenomena over a delta wing is demonstrated. The procedure and cautions for conducting the experiment are listed, including wind tunnel setup, data acquisition, and data processing. The representative results show that these two flow visualization methods are effective techniques for investigating the three-dimensional flow field qualitatively and quantitatively.

Here, we present a protocol to observe unsteady vortical flows over a delta wing using a modified smoke flow visualization technique and investigate the mechanism responsible for the oscillations of the leading-edge vortex breakdown locations.

It is well known that the flow field over a delta wing is dominated by a pair of counter rotating leading edge vortices (LEV). However, their mechanism is ...

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 ...

Particle Image Velocimetry Investigation of Hemodynamics via Aortic Phantom

Aortic valve dysfunction and stroke have recently been reported in transcatheter aortic valve implantation (TAVI) patients. Thrombus in the aortic sinus and neo-sinus due to hemodynamic changes has been suspected. In vitro experiments help investigate the hemodynamic characteristics in the cases where an in vivo assessment proves to be limited. In vitro experiments are also more robust, and the variable parameters are controlled readily. Particle image velocimetry (PIV) is a popular velocimetry method for in vitro studies. It provides a high-resolution velocity field such that even small-scale flow features are observed. The purpose of this study is to show how PIV is used to investigate the flow field in the aortic sinus after TAVI. The in vitro setup of the aortic phantom, TAVI for PIV, and the data acquisition process and post-processing flow analysis are described. The hemodynamic parameters are derived, including the velocity, flow stasis, vortex, vorticity, and particle residence. The results confirm that in vitro experiments and PIV help investigate the hemodynamic features in the aortic sinus.

Particle Image Velocimetry Investigation of Hemodynamics via Aortic Phantom

The present protocol describes particle image velocimetry (PIV) measurements performed to investigate the sinus flow through the in vitro setup of the transcatheter aortic valve (TAV). The hemodynamic parameters based on velocity are also determined.

Aortic valve dysfunction and stroke have recently been reported in transcatheter aortic valve implantation (TAVI) patients. Thrombus in the aortic sinus ...

A Protocol for Real-time 3D Single Particle Tracking

Real-time three-dimensional single particle tracking (RT-3D-SPT) has the potential to shed light on fast, 3D processes in cellular systems. Although various RT-3D-SPT methods have been put forward in recent years, tracking high speed 3D diffusing particles at low photon count rates remains a challenge. Moreover, RT-3D-SPT setups are generally complex and difficult to implement, limiting their widespread application to biological problems. This protocol presents a RT-3D-SPT system named 3D Dynamic Photon Localization Tracking (3D-DyPLoT), which can track particles with high diffusive speed (up to 20 &#181;m2/s) at low photon count rates (down to 10 kHz). 3D-DyPLoT employs a 2D electro-optic deflector (2D-EOD) and a tunable acoustic gradient (TAG) lens to drive a single focused laser spot dynamically in 3D. Combined with an optimized position estimation algorithm, 3D-DyPLoT can lock onto single particles with high tracking speed and high localization precision. Owing to the single excitation and single detection path layout, 3D-DyPLoT is robust and easy to set up. This protocol discusses how to build 3D-DyPLoT step by step. First, the optical layout is described. Next, the system is calibrated and optimized by raster scanning a 190 nm fluorescent bead with the piezoelectric nanopositioner. Finally, to demonstrate real-time 3D tracking ability, 110 nm fluorescent beads are tracked in water.

This protocol details the construction and operation of a real-time 3D single particle tracking microscope capable of tracking nanoscale fluorescent probes at high diffusive speeds and low photon count rates.

Real-time three-dimensional single particle tracking (RT-3D-SPT) has the potential to shed light on fast, 3D processes in cellular systems. Although ...

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

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Chapters in this video

Determining 3D Flow Fields via Multi-camera Light Field Imaging

High-speed Particle Image Velocimetry Near Surfaces

Image-based Lagrangian Particle Tracking in Bed-load Experiments

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

Micro-particle Image Velocimetry for Velocity Profile Measurements of Micro Blood Flows

Visually Based Characterization of the Incipient Particle Motion in Regular Substrates: From Laminar to Turbulent Conditions

Experimental Investigation of the Flow Structure over a Delta Wing Via Flow Visualization Methods

Simultaneous Measurement of Turbulence and Particle Kinematics Using Flow Imaging Techniques

Particle Image Velocimetry Investigation of Hemodynamics via Aortic Phantom

A Protocol for Real-time 3D Single Particle Tracking