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://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Fluid Mechanics. , (1994).</li><li>Hak, M. G. a. d. -. e. l. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Flow Control: Passive, Active, and Reactive Flow Management. , (2000).</li><li>Kim, B. H., Hertzberg, J. R., Shandas, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Development+and+validation+of+echo+PIV.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Real+time+multicomponent+echo+particle+image+velocimetry+technique+for+opaque+flow+imaging.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Perpendicular+ultrasound+velocity+measurement+by+2D+cross+correlation+of+RF+data.+Part+A:+validation+in+a+straight+tube.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=3D+Flow+reconstruction+using+ultrasound+PIV.">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://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Vivid 7/Vivid 7 PRO Userâ€™s Manual. , (1988).</li><li>Szabo, 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> Diagnostic Ultrasound Imaging: Inside Out. , (2004).</li><li>Raffel, M., Willert, C., Wereley, S., Kompenhans, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Particle Image Velocimetry: A Practical Guide. , (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 border="1">
	<tbody>
 	<tr>
 	<td>Name of the reagent</td>
 <td>Company</td>
 <td>Catalogue number</td>
 <td>Comments (optional)</td>
 </tr>
 <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

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 ...

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Echo Particle Image Velocimetry

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 ...

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14.5K Views.  University of New Hampshire. 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.

Video: Echo Particle Image Velocimetry

Gradient Echo Quantum Memory in Warm Atomic Vapor

Gradient echo memory (GEM) is a protocol for storing optical quantum states of light in atomic ensembles. The primary motivation for such a technology is that quantum key distribution (QKD), which uses Heisenberg uncertainty to guarantee security of cryptographic keys, is limited in transmission distance. The development of a quantum repeater is a possible path to extend QKD range, but a repeater will need a quantum memory. In our experiments we use a gas of rubidium 87 vapor that is contained in a warm gas cell. This makes the scheme particularly simple. It is also a highly versatile scheme that enables in-memory refinement of the stored state, such as frequency shifting and bandwidth manipulation. The basis of the GEM protocol is to absorb the light into an ensemble of atoms that has been prepared in a magnetic field gradient. The reversal of this gradient leads to rephasing of the atomic polarization and thus recall of the stored optical state. We will outline how we prepare the atoms and this gradient and also describe some of the pitfalls that need to be avoided, in particular four-wave mixing, which can give rise to optical gain.

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 memory (GEM) is a protocol for storing optical quantum states of light in  atomic ensembles. The primary motivation for such a technology ...

High-speed Particle Image Velocimetry Near Surfaces

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

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

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

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

The spin echo resolved grazing incidence scattering (SERGIS) technique has been used to probe the length-scales associated with irregularly shaped crystallites. Neutrons are passed through two well defined regions of magnetic field; one before and one after the sample. The two magnetic field regions have opposite polarity and are tuned such that neutrons travelling through both regions, without being perturbed, will undergo the same number of precessions in opposing directions. In this case the neutron precession in the second arm is said to &#34;echo&#34; the first, and the original polarization of the beam is preserved. If the neutron interacts with a sample and scatters elastically the path through the second arm is not the same as the first and the original polarization is not recovered. Depolarization of the neutron beam is a highly sensitive probe at very small angles (&#60;50&#160;&#956;rad) but still allows a high intensity, divergent beam to be used. The decrease in polarization of the beam reflected from the sample as compared to that from the reference sample can be directly related to structure within the sample.
In comparison to scattering observed in neutron reflection measurements the SERGIS signals are often weak and are unlikely to be observed if the in-plane structures within the sample under investigation are dilute, disordered, small in size and polydisperse or the neutron scattering contrast is low. Therefore, good results will most likely be obtained using the SERGIS technique if the sample being measured consist of thin films on a flat substrate and contain scattering features that contains a high density of moderately sized features (30 nm to 5 &#181;m) which scatter neutrons strongly or the features are arranged on a lattice. An advantage of the SERGIS technique is that it can probe structures in the plane of the sample.

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.

The spin echo resolved grazing incidence scattering (SERGIS) technique has been used to probe the length-scales associated with irregularly shaped ...

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

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

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

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

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

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

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

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

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 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 ...

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 ...

3D Printing - Evaluating Particle Emissions of a 3D Printing Pen

Three-dimensional (3D) printing as a type of additive manufacturing shows continuing increase in application and consumer popularity. The fused filament fabrication (FFF) is an inexpensive method used most frequently by consumers. Studies with 3D printers have shown that during the printing process particulate and volatile substances are released. Handheld 3D printing pens also use the FFF method but the consumer&#8217;s proximity to the 3D pens gives reason to higher exposure compared to a 3D printer. At the same time, 3D printing pens are often marketed for children who could be more sensitive to the printing emission. The aim of this study was to implement a low cost method to analyze the emissions of 3D printing pens. Polylactide (PLA) and acrylonitrile butadiene styrene (ABS) filaments of different colors were tested. In addition, filaments containing metal and carbon nanotubes (CNTs) were analyzed. An 18.5 L chamber and sampling close to the emission source was used to characterize emissions and concentrations near the breathing zone of the user.
Particle emissions and particle size distributions were measured and the potential release of metal particles and CNTs investigated. Particle number concentrations were found in a range of 105 - 106 particles/cm3, which is comparable to previous reports from 3D printers. Transmission electron microscopy (TEM) analysis showed nanoparticles of the different thermoplastic materials as well as of metal particles and CNTs. High contents of metal were observed by inductively coupled plasma mass spectrometry (ICP-MS).
These results call for a cautious use of 3D pens due to potential risk to the consumers.

This protocol presents a method to analyze the emission of 3D printing pens. Particle concentration and particle size distribution of the released particle is measured. Released particles are further analyzed with transmission electron microscopy (TEM). Metal content in filaments is quantified by inductively coupled plasma mass spectrometry (ICP-MS).

Three-dimensional (3D) printing as a type of additive manufacturing shows continuing increase in application and consumer popularity. The fused filament ...

Study of Protein Dynamics via Neutron Spin Echo Spectroscopy

Most human body proteins' activity and functionality are related to configurational changes of entire subdomains within the protein crystal structure. The crystal structures build the basis for any calculation that describes the structure or dynamics of a protein, most of the time with strong geometrical restrictions. However, these restrictions from the crystal structure are not present in the solution. The structure of the proteins in the solution may differ from the crystal due to rearrangements of loops or subdomains on the pico to nanosecond time scale (i.e., the internal protein dynamics time regime). The present work describes how slow motions on timescales of several tens of nanoseconds can be accessed using neutron scattering. In particular, the dynamical characterization of two major human proteins, an intrinsically disordered protein that lacks a well-defined secondary structure and a classical antibody protein, is addressed by neutron spin echo spectroscopy (NSE) combined with a wide range of laboratory characterization methods. Further insights into protein domain dynamics were achieved using mathematical modeling to describe the experimental neutron data and determine the crossover between combined diffusive and internal protein motions. The extraction of the internal dynamic contribution to the intermediate scattering function obtained from NSE, including the timescale of the various movements, allows further vision into the mechanical properties of single proteins and the softness of proteins in their nearly natural environment in the crowded protein solution.

Study of Protein Dynamics via Neutron Spin Echo Spectroscopy

The present protocol describes methods for investigating the structure and dynamics of two model proteins that have an important role in human health. The technique combines bench-top biophysical characterization with neutron spin echo spectroscopy to access the dynamics at time and length scales relevant for protein interdomain motions.

Most human body proteins' activity and functionality are related to configurational changes of entire subdomains within the protein crystal structure. The ...

Deep Neural Networks for Image-Based Dietary Assessment

Due to the issues and costs associated with manual dietary assessment approaches, automated solutions are required to ease and speed up the work and increase its quality. Today, automated solutions are able to record a person's dietary intake in a much simpler way, such as by taking an image with a smartphone camera. In this article, we will focus on such image-based approaches to dietary assessment. For the food image recognition problem, deep neural networks have achieved the state of the art in recent years, and we present our work in this field. In particular, we first describe the method for food and beverage image recognition using a deep neural network architecture, called NutriNet. This method, like most research done in the early days of deep learning-based food image recognition, is limited to one output per image, and therefore unsuitable for images with multiple food or beverage items. That is why approaches that perform food image segmentation are considerably more robust, as they are able to identify any number of food or beverage items in the image. We therefore also present two methods for food image segmentation - one is based on fully convolutional networks (FCNs), and the other on deep residual networks (ResNet).

The goal of the work presented in this article is to develop technology for automated recognition of food and beverage items from images taken by mobile devices. The technology comprises of two different approaches - the first one performs food image recognition while the second one performs food image segmentation.

Due to the issues and costs associated with manual dietary assessment approaches, automated solutions are required to ease and speed up the work and ...