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&#38;B level and image-filtering method, are directly correlated with the ability of reconstructing the Lagrangian paths of a set of (e.g., tracer) particles. It is important to note that the critical protocol steps, as demonstrated here, are the adjustments of the G&#38;B levels and the illumination of the FOV (combination of halogen spots lights, magnifying lens and reflecting mirror from the bottom of the flume).
These adjustments help to optimize the light scatters within the investigation to the four views. After identifying the experimental settings for high-fidelity measurements, thorough modification and troubleshooting should be made to compute the maximum number of accurate trajectories based on the frame rate, camera resolution and the size of investigation volume. Although the number of captured particles can be increased with higher frame rates, it is worth noticing that the number of tracked particles in 3D-PTV is much lower compared to PIV. The biggest potential of 3D-PTV is in its unique ability of describing the Lagrangian paths of multiple particles. In this demonstration, the view splitter set-up was implemented to avoid using multiple expansive cameras, however, it is important to note that this set-up requires higher camera resolution and limits the size of sample volume.
In this study, the intermediate-field features of a circular jet are analyzed with the 3D-PTV technique. The approach allowed obtaining important features of the flow from Eulerian and Lagrangian frames. In particular, the average curvature of the particles as a function of the radial distance is characterized for first time, at two cross-sectional planes using the Lagrangian features of the particle trajectories. The RMS of the recognized calibration targets ranges between 7.3 &#181;m, to 141.7 &#181;m in the streamwise and spanwise directions. Although this high relative error in the spanwise direction due to small angles of the views in z-direction may not be completely overcome, it can be further reduced by adding more target points in z-direction such as using a 2D calibration target at various locations (multiplane calibration).
Overall, 3D-PTV is a useful technique that can be applied in a number of other problems including time-dependent flows or the dynamics of active scalars. For instance, it can be highly useful to study the interplay between turbulence and species in aquatic environments.

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-coordinate is due to the reduced targets points with respect to those in the other directions and relatively small angles of four views with the z-axis compared to x and y coordinates. The detected particles in each of the four views at any given instant were on the order of 103. Among the detected particles, the number of successful 3D reconstructions is reduced to roughly half due to the fact that only particles in the intersection region are captured. Video 1 shows a high-speed video sample of the jet flow from the four-view splitter.
A sample of four representative particle trajectories in the intermediate-field region around and crossing the x/dh=16 plane at radial distances r/dh=v0, 1.5, 3 from the jet core is illustrated in Figure 4. As expected, longer trajectories in the given time interval (&#916;t &#8776; 1 sec) are observed around the jet core. At the edge of the jet (r/dh &#8805;2), tracer particles exhibit short and more complex trajectories. Figure 5 shows all the successfully reconstructed particle trajectories crossing the x/dh = 16 plane. The particle velocities in the selected domain exhibit a wide distribution ranging from nearly 0 to 0.6Uj, where Uj &#8776; 0.6 m/sec is the exit velocity of the jet, and acceleration/deceleration, &#8706;U&#8260;&#8706;s, where is the Cartesian coordinate. It also reveals that an substantial portion of the particles within the vicinity of the jet core exhibit smoother trajectories. The particle trajectories, s(t), allows for the estimation of the position, velocity and acceleration. Figure 6a shows the case of a particle crossing the x/dh = 16 plane around the jet core. Figure 6b, 6c, and 6d show the 3 components of the particle trajectory, velocity and acceleration as a function of normalized time. It is worth highlighting that the local particle acceleration can be several times the standard gravity. The particle trajectories allow for obtaining specific features of the particle trajectories via the so-called Frenet-Serret frame. It describes the changes of the orthonormal vectors (tangential, normal, binormal) along s(t). Of particular relevance is the curvature, &#954;, which is the inverse of the radius of curvature, &#961;, and defined as:
<img alt="Equation 1" src="/files/ftp_upload/53745/53745eq1.jpg" />
where <img alt="Equation 3" src="/files/ftp_upload/53745/t.jpg" /> = dr/ds is the tangent unit vector of the trajectory and r is the position vector (Euclidean space) of the particle as a function of time, which can be written as a function of , i.e., r(s) = r(t(s)). The curvature, &#954;, is computed for all the particles crossing the x&#8260;dh = 16 and x&#8260;dh = 17 planes. The mean curvature, <img alt="Equation 3" src="/files/ftp_upload/53745/k.jpg" />, as a function of the distance from the jet core r is calculated as:
<img alt="Equation 2" src="/files/ftp_upload/53745/53745eq2.jpg" />
where &#916;r = 0.2dh is used in here. Figure 7 illustrates <img alt="Equation 3" src="/files/ftp_upload/53745/k.jpg" /> = f(r) normalized by dh. It shows a relatively low and nearly constant&#160;<img alt="Equation 3" src="/files/ftp_upload/53745/k.jpg" /> within the area defined by the circular cross section of the pipe, r/dh &#8804;0.5. At a larger distance from the jet core in the x/dh = 16 plane, <img alt="Equation 3" src="/files/ftp_upload/53745/k.jpg" /> increases monotonically. A similar trend is obtained at the x/dh = 17 plane, but with a reduced&#160;<img alt="Equation 3" src="/files/ftp_upload/53745/k.jpg" /> outside the jet core (r/dh &#8805;0.5). It is worth highlighting that this flow feature can be inferred only with the 3D-PTV technique. The data quality based on various levels of G&#38;B settings is assessed in terms of the ratio of linked particles to the rest of 3D-reconstructed particles shown in Table 1. The highest link ratio is observed at the G&#38;B setting of 300&#38;500.
Eulerian flow characteristics can be achieved by grid-interpolation, which mimics 3D particle image velocimetry (3D-PIV). It is important to note that due to the comparatively low particles tracked at each time, a significantly higher number of frames are needed to truly mimic PIV quality for an Eulerian description. This is more critical in the estimation of high-order statistics (e.g., turbulence intensity and Reynolds stresses). The streamwise velocity at the jet core for various G&#38;B levels is illustrated in Figure 8. The measurements are compared with the theoretical behavior:
<img alt="Equation 3" src="/files/ftp_upload/53745/53745eq3.jpg" />
where U0(x) is the streamwise velocity at the jet core, B &#8776; 6 is a constant, and x0 is the virtual origin38. The figure shows the relevance of setting the G&#38;B levels. Figure 9 illustrates the mean velocity distribution of the jets in the x/dh = 16 plane.
Finally, the spectral distribution &#981;(f) of the large-scale motions of the streamwise velocity at locations r/dh = 0, 0.6, and 1 in the x/dh = 10 plane is illustrated in Figure 10. A Butterworth low-pass filter was applied to the velocity time series with cut-off frequency, fc = 200 Hz.
<img alt="Figure 1" src="/files/ftp_upload/53745/53745fig1.jpg" /> 
Figure 1: Schematic of the experimental set-up. <a href="https://www.jove.com/files/ftp_upload/53745/53745fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" src="/files/ftp_upload/53745/53745fig2.jpg" /> 
Figure 2: Experimental set-up. This illustrates various views of the camera and the four-image view splitter, flume and interrogation volume: (top left) top view, (bottom left) back view of the camera and view splitter system, (top middle, bottom middle) side views of the overall experimental set-up, (right) zoom-in view of seeding particles in the jet flows. <a href="https://www.jove.com/files/ftp_upload/53745/53745fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" src="/files/ftp_upload/53745/53745fig3.jpg" /> 
Figure 3: Calibration: (a) Calibration target, (b) Image-set of the calibration target from the view splitter, (c) 3D recognition of the fiducial marks from the calibration target. <a href="https://www.jove.com/files/ftp_upload/53745/53745fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" src="/files/ftp_upload/53745/53745fig4.jpg" /> 
Figure 4: Selected particle trajectories at r&#8260;dh = 0, 1.5, 3. <a href="https://www.jove.com/files/ftp_upload/53745/53745fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" src="/files/ftp_upload/53745/53745fig5.jpg" /> 
Figure 5: Particle trajectories crossing the x/dh = 16 plane, where velocity is shown as a color level. The interrogation volume shown in the figure is contained between (x)&#8260;dh <img alt="Figure 1" src="/files/ftp_upload/53745/e.jpg" />(14.5,18.5), y&#8260;dh <img alt="Figure 1" src="/files/ftp_upload/53745/e.jpg" />(-2,2), and z&#8260;dh <img alt="Figure 1" src="/files/ftp_upload/53745/e.jpg" />(-2,2), where (x, y, z) = (0, 0, 0) is located at the center of the jet origin. The velocity along the individual trajectories, normalized by the bulk velocity U0, is illustrated as a color level. <a href="https://www.jove.com/files/ftp_upload/53745/53745fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" src="/files/ftp_upload/53745/53745fig6.jpg" /> 
Figure 6: (a) Particle trajectory, (b) displacement, (c) velocity, and (d) acceleration of an arbitrary particle. <a href="https://www.jove.com/files/ftp_upload/53745/53745fig6large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 7" src="/files/ftp_upload/53745/53745fig7.jpg" /> 
Figure 7: Curvature of the particles: Graph showing mean curvature of the particles as a function of the radial distance from the jet core at the planes x&#8260;dh = 16 and x&#8260;dh = 17. <a href="https://www.jove.com/files/ftp_upload/53745/53745fig7large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 8" src="/files/ftp_upload/53745/53745fig8.jpg" /> 
Figure 8: Streamwise velocity at the jet core within (x)&#8260;dh <img alt="Figure 1" src="/files/ftp_upload/53745/e.jpg" />(15,18) for various G&#38;B levels. Three G&#38;B levels are included (300&#38;500 (optimum), 300&#38;250, 100&#38;250). <a href="https://www.jove.com/files/ftp_upload/53745/53745fig8large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 9" src="/files/ftp_upload/53745/53745fig9.jpg" /> 
Figure 9: Non-dimensional distribution of the streamwise velocity component at x/dh = 16. <a href="https://www.jove.com/files/ftp_upload/53745/53745fig9large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 10" src="/files/ftp_upload/53745/53745fig10.jpg" /> 
Figure 10: Power spectrum &#981;(f) of the streamwise velocity component at a point located at r/dh = 0 (jet core), 0.6, and 1 in the x/dh = 16 plane. <a href="https://www.jove.com/files/ftp_upload/53745/53745fig10large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Video 1" src="/files/ftp_upload/53745/53745video1.jpg" /> 
Video 1: Video sample of the jet flow from the four-view splitter, 10 times slower than actual speed obtained at 550 fps (<a href="https://www.jove.com/files/ftp_upload/53745/Video1.mp4">right click to download</a>).
<img alt="Table 1" src="/files/ftp_upload/53745/53745table1.jpg" />
Table 1: The ratio of linked particles to the rest of 3D-reconstructed particles at various G&#38;B levels. Three G&#38;B levels are included (100&#38;250, 300&#38;250, 300&#38;500).

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 ...

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12.1K 조회수.  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.

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

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 ...

연구

공학

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.

희생 구성 요소의 증발을 사용하여 세 가지 차원 미세 만들기의 과정

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.

표면 근처 고속 입자 이미지 유속계

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.

다중 입자 Phonon은 감금 모델 라만 분광학을 이용하여 나노 결정 크기 분포의 특성

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.

진공 조건에서 반발 계수의 측정을위한 실험 장치 개발

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.

멀티 스케일 3 차원 마이크로 전자 패키지를 조사하는 방사광 Microtomography 사용

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.

3 차원 초음파 바늘 팁 광섬유 초음파 수신기와 함께 추적

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 ...