Name: Echo Particle Image Velocimetry (Video) | JoVE Journal
Uploaded: 2012-12-27T12:00:00.000+00:00
Duration: 16 min 31 s
Description: Learn echo particle image velocimetry for measuring velocity fields in optically opaque fluids using ultrasound imaging and tracer particles. This method enables researchers to study fluid dynamics in challenging environments.

ai_agent_IP

ai_agent

maintenance

instantTrialDurationMinutes

isBiopharma

enableNewMyJoveSectionDesign

educationFeatures

seoMetaApi

ai_copilot_institution_blacklist

ai_chat_institution_blacklist

freeAccessWindow

ssoRoleCapture

scheduledMaintenanceBanner

joveLabsFeatures

disable_autoplay_for_bots

extended_trial_duration

5 minutes

The authors gratefully acknowledge support by the National Science Foundation, CBET0846359, grant monitor Horst Henning Winter.

1. Create a Measurable Flow
<ol>
 <li>EPIV validation measurements will be demonstrated in pipe flow of a glycerin water solution (50% glycerin - 50% water). A schematic of the experimental setup is shown in Figure 1. </li>
 <li>Hollow glass spheres with a nominal diameter of 10 &mu;m are added to the fluid at a concentration of approximately 17 weight parts per million. The hollow glass spheres serve as ultrasound contrast agents, and their size and density are chosen such that they passively follow the fluid flow.10 </li>
 <li>A fixed voltage is supplied to the pump to introduce a known flow rate. The flow rate is chosen such that U &lt;&lt; &Delta;X/&delta;t, where U is the mean velocity in the pipe, &Delta;X is the linear length of the EPIV measurement volume, and &delta;t is the time step between images, i.e., the flow need be "slow" compared to the fps of the ultrasound system.3 </li>
</ol>
2. Calibrate the Ultrasound
<ol>
 <li>Mount the ultrasound probe to the exterior pipe wall. A water based topical gel is applied to the ultrasound probe to minimize loss of transmission of the ultrasound beam between the probe face and the pipe wall.</li>
 <li>Power on the ultrasound machine. A live stream of ultrasound images begins automatically once all systems load. </li>
 <li>Set the image depth using the Depth Control knob on the control panel of the ultrasound machine. </li>
 <li>Set the total image gain using the 2D Gain knob on the control panel of the ultrasound machine. </li>
 <li>Adjust the Time Gain Compensation (TGC) sliders to attenuate scatter from the pipe walls and to compensate for depth related attenuation of the ultrasound signal. </li>
 <li>The image width, focus, probe operating frequency, and frame rate are adjusted using the assignable control knobs. These four knobs, located on the top left of the control panel, vary according to the mode in which the system is running. In 2D mode (as presently used), from left-to-right the knobs correspond to width, focus, frequency, and frame rate, respectively. Note that owing to the fundamental principles of ultrasound imaging9, these four parameters are inherently coupled. Consequently, for a given ultrasound image scan (i.e., an EPIV experiment) there is a trade-off between spatial and temporal resolution. </li>
 <li>See Figure 2 for a representative ultrasound image of pipe flow seeded with 10 &mu;m hollow glass spheres. Note that due to limited lateral resolution, the glass spheres are smeared in the lateral direction and appear as ellipsoids in the image. </li>
</ol>
3. Data Collection
<ol>
 <li>Press the New Exam button on the ultrasound control panel to start a new experiment. </li>
 <li>Create a new "patient" by entering Pipe Flow in Last Name and todays date in
 First Name and the test number in Patient ID. </li>
 <li>Following creation of the "patient", an ultrasound scan begins until the preset maximum of between 1000-1500 images is reached, after which a new scan loop begins. Pressing the Freeze button on the ultrasound control panel twice will restart the scan at anytime prior to reaching the maximum preset number of images. </li>
 <li>Once a good set of ultrasound images has been acquired (i.e., sharp seed particle images and sufficient seed particle density), press the Freeze button on the ultrasound control panel to stop image acquisition. </li>
 <li>Press the Cineloop button on the ultrasound control panel. Select the set of ultrasound images to be analyzed using the First Cycle knob on the ultrasound control panel to select the first image in the set, and the Last Cycle knob to select the last image in the set.</li>
 <li>Press the Image Store button on the ultrasound control panel to save the selected set of ultrasound images.</li>
 <li>Press the Archive button on the ultrasound control panel and use the mouse cursor to select End Exam. This will prompt the user to select images or cineloops to save to the local hard drive. Select the cineloop(s) of interest then exit the exam. </li>
 <li>Press the Archive button on the ultrasound control panel and use the mouse cursor to first select More and then select Disk management. Disk management will transfer the saved cineloop(s) to the PC running the PIV software. </li>
</ol>
4. Converting Filetype
<ol>
 <li>An ultrasound image is stored as a digital imaging communications in medicine (DICOM) file type on the ultrasound machine. In order to be opened and read by the PIV software, the DICOM files must be converted to picture files. Presently, a Matlab script running DICOM2JPG.m is used to convert the DICOM files to joint photographic experts group (JPEG) file type. </li>
 <li>The JPEG ultrasound images are then analyzed using DaVis software from LaVision. </li>
</ol>
5. Computing Displacement Fields, D(x, y), Using DaVis
<ol>
 <li>Double mouse click on the DaVis icon on the PC. Select New Project. Select
 PIV. </li>
 <li>Select Import Images in the toolbar, and choose Import via Numbered Files. In the pull-down menu, locate the folder where the JPEG ultrasound images are stored, and double click on the first image of the set. This will import all ultrasound images in this numbered set. </li>
 <li>Typically an image mask will be defined to isolate the region of interest (ROI) in the ultrasound image to be processed. For pipe flow, the mask is used to define the ROI between the pipe walls (i.e., the fluid). </li>
 <li>Go to the main control panel in DaVis, select the tab located under Current Project containing the imported images, and select the tab labeled Batch Processing. This enables the vector processing window of DaVis for batch processing of the imported ultrasound images. </li>
 <li>From the operations list, using the PIV-Time-Series tree, select vector calculation parameters, and choose the parameters to be used for vector processing. If a mask is used, check the box Data Range = use masked area in the vector calculation parameter menu. Note that optimal selection of vector calculation parameters is dependent on flow geometry, flow properties, image resolution, tracer particle density, and desired quantitative flow analysis.10 
 For the pipe flow measurements, the parameters that have typically yielded the best results are multipass with decreasing interrogation size of 32 x 32 pixel2 to 8 x 8 pixel2, with an overlap of 50%. Relative vector range restriction was set to &plusmn;all(window size/2) and absolute vector range restriction was set to &plusmn;5 pixels. Lastly, a 3 x 3pixel2 median filter was used to suppress noise and smooth the vector fields.</li>
 <li>On the left of the batch processing screen, select the total amount of images to be processed and select start processing. This will compute the displacement field, D(x,y), between successive ultrasound images using cross-correlation algorithms. </li>
</ol>
6. Analyzing Vector Fields
<ol>
 <li>For post-processing and data analysis, the EPIV vector fields are exported from DaVis as .txt files. This is achieved by selecting the vector displacement branch under the JPEG image branch in the project screen. In the toolbar, select the Export tab, select file type ASCII .txt, choose/create an export folder, and select Export. </li>
 <li>The exported vector fields are named Bxxxxx.txt, where 00001&le; xxxxx &le; 99999, with B denoting buffer. Each file contains four data columns: (1) x-location of the vector in the image, (2) y-location of the vector in the image, (3) x-component of displacement (i.e. streamwise displacement), (4) y-component of displacement (i.e., wall-normal displacement). The Bxxxxx.txt files are opened and processed in MATLAB to first compute the velocity field, by 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]. Next, ensemble average velocity vector fields, wall-normal profiles of mean velocity, among other flow quantities of interest are computed. (See section Representative Results.) </li>
</ol>

<ol><li>White, F. M. <a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=author%3aWhite%2C+FM+%22Fluid+Mechanics%22">Fluid Mechanics</a>. , McGraw Hill. New York, New York. (1994).</li>
<li>Hak, M. G. ad-el <a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=author%3aHak%2C+MGad-el+%22Flow+Control%3A+Passive%2C+Active%2C+and+Reactive+Flow+Management%22">Flow Control: Passive, Active, and Reactive Flow Management</a>. , University Press. Oxford. (2000).</li>
<li>Kim, B. H., Hertzberg, J. R., Shandas, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Development+and+validation+of+echo+PIV%5BTitle%5D)+AND+%22Exp.+Fluids%22%5BJournal%5D)">Development and validation of echo PIV.</a> Exp. Fluids. 36, 455-462 (2004).</li>
<li>Zheng, H., Liu, L., Williams, L., Hertzberg, J. R., Lanning, C., Shandas, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Real+time+multicomponent+echo+particle+image+velocimetry+technique+for+opaque+flow+imaging%5BTitle%5D)+AND+%22Appl.+Phys.+Lett%22%5BJournal%5D)">Real time multicomponent echo particle image velocimetry technique for opaque flow imaging.</a> Appl. Phys. Lett. 88, 261915(2006).</li>
<li>Beulen, B., Bijnens, N., Rutten, M., Brands, P., van de Vosse, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Perpendicular+ultrasound+velocity+measurement+by+2D+cross+correlation+of+RF+data.+Part+A%3A+validation+in+a+straight+tube%5BTitle%5D)+AND+%22Exp.+Fluids%22%5BJournal%5D)">Perpendicular ultrasound velocity measurement by 2D cross correlation of RF data. Part A: validation in a straight tube.</a> Exp. Fluids. 49, 1177-1186 (2010).</li>
<li>Poelma, C., Mari, J. M., Foin, N., Tang, M. -X., Krams, R., Caro, C. G., Weinberg, P. D., Westerweel, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((3D+Flow+reconstruction+using+ultrasound+PIV%5BTitle%5D)+AND+%22Exp.+Fluids%22%5BJournal%5D)">3D Flow reconstruction using ultrasound PIV.</a> Exp. Fluids. 50, 777-785 (2011).</li>
<li>GE VINGMED ULTRASOUND A/A. <a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=%22Vivid+7%2FVivid+7+PRO+User%C3%A2%E2%82%AC%E2%84%A2s+Manual%22">Vivid 7/Vivid 7 PRO Userâ€™s Manual</a>. , FC092326 edition, GE VINGMED. Horten, Norway. (1988).</li>
<li>DaVis Software for Intelligent Imaging [Internet]. , LaVision. Michigan. Available from: 
 <a target="_blank" href="http://www.lavision.de/en/download.php?id=3">http://www.lavision.de/en/download.php?id=3</a> (2013).</li>
<li>Szabo, T. <a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=author%3aSzabo%2C+T+%22Diagnostic+Ultrasound+Imaging%3A+Inside+Out%22">Diagnostic Ultrasound Imaging: Inside Out</a>. , Elsevier Academic Press. Burlington, MA. (2004).</li>
<li>Raffel, M., Willert, C., Wereley, S., Kompenhans, J. <a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=author%3aRaffel%2C+M+%22Particle+Image+Velocimetry%3A+A+Practical+Guide%22">Particle Image Velocimetry: A Practical Guide</a>. , Springer-Verlag. Berlin Heidelberg New York. (2007).</li>
</ol>

Authors have nothing to disclose.

The operating protocol for an echo particle image velocimetry (EPIV) system capable of acquiring two-dimensional fields of velocity in optically opaque fluids or through optically opaque geometries was described. Practical application of EPIV is well-suited for the study of industrial and biological flow systems, where the flow of opaque fluids occurs in a great many application. The particular system presented here was purposefully built to study the flow properties of liquefied biomass fluids used in the production of...

<table><tbody><tr><td>Ultrasound Machine</td><td>GE</td><td>Vivid 7 Pro</td><td></td></tr><tr><td>Linear Ultrasound Array</td><td>GE</td><td>10 L</td><td></td></tr><tr><td>DC Water Pump</td><td>KNF</td><td>NF 10 KPDC</td><td></td></tr><tr><td>Vector Processing Software</td><td>Lavision</td><td>DaVis 7.2</td><td></td></tr><tr><td>Post Processing Software</td><td>Mathworks</td><td>MATLAB 7.12</td><td></td></tr><tr><td>Acrylic Tubing</td><td>McMaster-Carr</td><td>8486K531</td><td></td></tr><tr><td>Ultrasound Gel</td><td>Parker</td><td>Aquasonic 100</td><td></td></tr></tbody></table>

echo particle image velocimetry

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 instantaneous echo particle image velocimetry (EPIV) vector field is shown in Figure 3. The vector plot shows velocity vectors every fourth column, and the background color contour map corresponds to velocity magnitude. An ensemble average vector plot averaged over 1000 instantaneous EPIV vector plots is shown in Figure 4. Consistent with pipe flow, the velocity vectors are primarily in the streamwise direction, the largest velocities occur at the pipe centerline, and the velocities ...

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 ...

The goal of this experiment is to acquire two dimensional, instantaneous fields of velocity in Hagen Zoi flow, also known as laminar pipe flow. Using echo particle image LOC symmetry or E-P-I-V-E-P-I-V validation measurements are demonstrated in a recirculating pipe flow of a 50 50 mixture of water and glycerin. The pipe flow system used here employs a constant pressure head maintained by an aquarium pump to drive the flow.A phased array ultrasound probe is mounted to the pipe wall of the flow system and B mode ultrasound images are streamed. The fluid is seated with hollow glass spheres or tracer particles that faithfully follow the motions of the flow. Ultrasound images are then acquired, transferred to a PC and converted to an image format compatible with the commercial particle image veloc symmetry software.Cross correlation algorithms are applied to successive ultrasound B mode images to compute two dimensional fields of velocity. Ultimately, the vector fields are analyzed to compute flow quantities of interest, such as ensemble average sheer stress, and vorticity. Though this technique can provide insight into basic fluid dynamics, it can be and is often used in practical flow systems, including biomedical applications, for example, arterial or interventricular flows.Our ongoing experiments using liquified biomass and fluid is what interested us in this technique. To set up the EP IV system, start by powering on the pumps. This will start the recirculating pipe flow at a constant rate.Next, apply a water-based topical gel to the ultrasound probe. The gel minimizes loss of transmission of the ultrasound beam between the probe face and the pipe. Then using a specially constructed probe mount with through wall pipe fittings, attach the ultrasound probe to the exterior pipe wall power on the ultrasound machine.Once all systems load, a live stream of images will appear on the ultrasound screen. 2D mode is the default setting for the linear probe to acquire EPIV measurements. First, measure out the appropriate dry weight of nominal 10 micrometer hollow glass spheres so that their final concentration when added to the flow system is about 17 weight parts per million.Next, extract a volume of fluid from the basin and add the particles to the fluid to form a concentrated particle solution. The particles when added to the flow system will serve as ultrasound contrast agents or tracer particles. Add the concentrated particle solution to the recirculating pipe flow system by stirring it into the water basins.The glass spheres can then be viewed on the ultrasound monitor. After a few minutes, the glass spheres will be evenly distributed throughout the system, So one of the most difficult parts of this procedure is obtaining clear high resolution ultrasound images. In order to maximize the quality of these images, we adjust the gain focal points and dynamic range based on apriori estimates of the flow velocity.We further optimize these parameters on the fly by analysis of the ultrasound images. Use the depth control knob on the control panel of the ultrasound to set the image depth to three centimeters. Next, using the 2D gain knob, adjust the total image gain to increase the brightness of the image such that the seed particles are clearly visible in the instrument panel.Adjust the time gain compensation sliders to attenuate scatter from the pipe walls and to compensate for depth related attenuation of the ultrasound signal. This will remove excess image at the top and bottom of the pipe walls in 2D mode. The knobs on top of the control panel from left to right correspond to width, focus frequency and frame rate.Use these knobs to adjust the image further to achieve the highest possible physical resolution, frequency and frame rate for analysis. Then adjust the probe operating frequency to 10 megahertz and set the frame rate as 49.5 frames per second. Note that these four parameters are inherently coupled.Consequently, for a given ultrasound image scan, there is a trade-off between spatial and temporal resolution. Due to limited lateral resolution, the glass spheres will be smeared in the lateral direction and appear as ellipsoids in the image. Once the parameters are optimized, it is time to collect data on the ultrasound control panel on the instrument.Press the new exam button to start a new experiment. Under patient. Enter pipe flow as the last name and the date as the first name.Enter the test number in the patient ID field. The ultrasound scan will then begin automatically when the preset maximum of 1000 to 1, 500 images is reached. A new scan loop begins as scanning continues.Make adjustments to the imaging parameters until the seed particle is sharply in focus with about 10 particles per interrogation area. To restart the scanning recording loop, press the freeze button on the ultrasound control panel. Once a sufficient number of ideal images have been acquired, press the freeze button.Next, press the C loop button on the ultrasound control panel. Select all images to include all ultrasound images in the analysis set. Once the images for analysis have been selected, press the image store button to save the selected set of ultrasound images.Once the images have been saved, press the archive button on the ultrasound control panel. When prompted, select the syn loop of interest from the small window to be saved to the local hard drive. Then use the mouse cursor to select end exam.Press the archive button and use the mouse cursor to first select more and then select disc management. This will transfer the saved cyl loop or cyl loops to the PC running the particle image velocity symmetry or PIV software. Once the images are captured and saved, the ultrasound image must be converted from a digital imaging communications in medicine or DICOM file to a joint photographic experts group or JPEG picture file for analysis.Use a MATLAB script running DICOM to jpeg dot m to convert the DICOM files to JPEGs. This script was made in-house and can be obtained for educational purposes from the web address shown here. Once the file has been converted, open Davi software from Law Vision in the software.Double click on the davi icon, select new project, then select PIV. In the toolbar, select import images and choose import via numbered files. Then in the pull down menu, locate the folder where the JPEG ultrasound images are stored and double click on the first image of the set.This will import all ultrasound images in this numbered set to define a region of interest for analysis that includes only the fluid. Apply a mask to create the mask, enter the coordinates, a rectangular area using two x and y coordinate points based on the information from the DICOM file and knowledge of the pixel dimensions. Next in the main control panel in dvu, click on the tab located under current project containing the imported images.Select the table labeled batch processing. This enables the vector processing window of Davi for batch processing from the operations list using the PIV plus PIV time series tree select vector calculation parameters and choose multipass with a decreasing interrogation size of 64 pixels by 64 pixels to 12 pixels by 12 pixels with an overlap of 50%Set the relative vector range restriction to all and then absolute vector range restriction to five pixels. Then apply a median filter to suppress noise and smooth the vector fields.Next for vector processing, check the box data range equals use masked area in the vector calculation parameter menu. Note that optimal selection of vector calculation parameters is dependent on flow geometry, flow properties, image resolution, tracer particle density, and desired quantitative flow analysis. Once all of the desired parameters have been set at the left of the batch processing screen, select the total number of images to be processed.Click on start processing. This will compute the displacement field between successive ultrasound images using cross correlation algorithms. To analyze the processed data, export the UCV vector fields from DAVO as txt files.To do this under the JPEG image branch in the project screen, select the vector displacement branch. In the toolbar, select the export tab. Select file type.Ask e txt. Choose create an export folder and select export. Next, open the file in MATLAB by running the MATLAB script.The exported vector fields are named B-X-X-X-X-X do TXT where X represents a number in increasing from one to 99, 999. Each file contains four data columns, which can be viewed using notepad one x location of the vector in the image two Y location of the vector in the image, three X component of displacement or stream wise displacement, and four y component of displacement, which describes the wall. Normal displacement use to compute the velocity vector field U as a function of X and Y, where X and Y correspond to spatial coordinates in the ultrasound image, this is done by first converting the displacement field D of X and Y measured in pixels to a displacement field measured in meters using the image scaling parameter M given in units of meter over pixel.Next, the displacement field is divided by the sweep corrected temporal separation between images delta T, where delta T is equal to one over the frame rate given in frames per second, plus the displacement field divided by the time it takes for the ultrasound image to sweep across the image width. In summary, U of X and Y equals M times D of X and Y divided by delta T.DICOM inherently stores a file structure that provides the information needed to compute the image scaling parameter M and sweep corrected temporal separation. Delta T in the present study M equals 77 microns per pixel FPS equals 49.5 and B equals 25, 047 pixels per second.Finally, compute ensemble average velocity vector fields while normal profiles of mean velocity and any other flow quantities of interest. To demonstrate the usefulness of EPIV and to assess measurement uncertainty, two dimensional, instantaneous fields of velocity and laminar pipe flow were acquired as described in this video, this instantaneous vector plot shows velocity vectors every fourth column, and the background color contour map corresponds to velocity magnitude. The two dimensional spatial location of the velocity vector is denoted by D over D and X over D, where D is the radial position measured from the upper wall.D is the pipe diameter, and X is the stream wise position measured from the pipe entrance. The apparent parabolic shape of the velocity vectors along columns indicates that the measurements are consistent with the expected velocity profile for pipe flow. The ensemble averaged vector plot computed by averaging over 1000 instantaneous vector plots provides a representation of the mean velocity field.It will also average out random noise errors in the instantaneous vector fields. The velocity vectors are primarily in the stream wise direction. The largest velocities occur at the pipe center line.The velocities decreased to zero at the pipe walls and the flow is roughly symmetric the mean stream wise velocity profile along the pipe radius obtained by averaging the ensemble averaged vector plot along the rows in the horizontal direction is shown here. Also shown is the expected mean velocity profile for laminar pipe flow. Given the experimental conditions.The agreement between the EPIV measurements and the expected higgin pozo profile is best near the pipe center line and worst near the pipe walls. The large differences near the wall are likely due to strong reflection and refraction of the ultrasound waves at the curved surface of the pipe wall that produce high image intensities in these regions, the high intensities at the wall obscure particle intensities leading to measurement error. With the development of this technique, researchers studying fluid dynamics in engineered or biological flow systems can now acquire spatial temporal variations of the velocity field in optically opaque fluids or through optically opaque geometries.After watching this video, you should have a good understanding of how EPIV works, its limitations and how to build and operate an EPIV system using a commercial ultrasound machine.

Research

JoVE Journal

Engineering

Micro-particle image velocimetry (μPIV) 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 for Velocity Profile Measurements of Micro Blood Flows

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.

High-speed Particle Image Velocimetry Near Surfaces

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.

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

An experimentally accessible analog method for studying molecular hydrodynamic processes in dense fluids is presented. The technique uses particle image velocimetry of vibrated, high-restitution grain piles and allows direct, macroscopic observation of dynamical processes known and predicted to exist in strongly interacting, high density gases and liquids.

An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids

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.

Simultaneous Measurement of Turbulence and Particle Kinematics Using Flow Imaging Techniques

Here we present simplified methods for fabricating transparent neurovascular phantoms and characterizing the flow therein. We highlight several important parameters and demonstrate their relationship to field accuracy.

Meso-Scale Particle Image Velocimetry Studies of Neurovascular Flows In Vitro

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.

Particle Image Velocimetry Investigation of Hemodynamics via Aortic Phantom

The gradient echo memory is a protocol for storing optical quantum states of light in atomic ensembles. Quantum memory is a key element of a quantum repeater, which can extend the range of quantum key distribution. We outline the operation of the scheme when implemented in a 3-level atomic ensemble.

Gradient Echo Quantum Memory in Warm Atomic Vapor

Progress has been made in utilizing spin echo resolved grazing incidence scattering (SERGIS) as a neutron scattering technique to probe the length-scales in irregular samples. Crystallites of [6,6]-phenyl-C61-butyric acid methyl ester have been probed using the SERGIS technique and the results confirmed by optical and atomic force microscopy.

Using Neutron Spin Echo Resolved Grazing Incidence Scattering to Investigate Organic Solar Cell Materials

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.

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

Learn echo particle image velocimetry for measuring velocity fields in optically opaque fluids using ultrasound imaging and tracer particles. This method enables researchers to study fluid dynamics in challenging environments.

Echo Particle Image Velocimetry

Explore More Videos