This material is based upon work supported by National Science Foundation grants EAR-0810270, EAR-1215898, and EAR-1344280, as well as an&#160;NSF Graduate Research Fellowship.

1. Simulation Software
<ol>
	<li>Use the software described in this section.
	<ol>
		<li>Download and install the free/open-source multi-agent modeling language and simulation platform, NetLogo (Available: <a href="http://ccl.northwestern.edu/netlogo/">http://ccl.northwestern.edu/netlogo/</a>, version 5.1 or later). 
		Note: This software is available at no cost and runs on all major operating systems (Windows/Mac/Linux).</li>
		<li>Download the two specific simulation script files (mousedrop.nlogo and interface.nlogo) that accompany this laboratory procedure. (Available: <a href="http://modelingcommons.org/browse/one_model/4259">http://modelingcommons.org/browse/one_model/4259</a> and <a href="http://modelingcommons.org/browse/one_model/4258">http://modelingcommons.org/browse/one_model/4258</a>) 
		Note: Once the simulation platform is installed and these files have been downloaded, double-clicking these files automatically opens the simulations up, ready to run.</li>
	</ol>
	</li>
</ol>
2. Flume Demonstration
<ol>
	<li>Set up the laboratory flume so that all parameters (Table 1) fall within the mousedrop simulation parameter range constraints. 
	Note: The constraints can be adjusted in mousedrop, if required for the physical system by editing the sliders.
	<ol>
		<li>Pour a layer of approximately 15-25 cm of sand into the flume. Measure and record the hydraulic conductivity and porosity of the sand following standard methods32,33.</li>
		<li>Fill the flume with approximately 20-30 cm of water.</li>
		<li>Start the flume and increase the flow rate to a level that is fast enough to move sand grains and thus to create bedforms. 
		Note: The flow rate can be further adjusted to refine bedform characteristics with practice. Bedform sizes are a result of flow rate, water depth and sand properties.</li>
		<li>Allow bedforms to develop for 12-24 hr to form natural dune/ripple morphology. To accelerate this process, manually shape regular dunes, and then allow sediment transport for 4-12 hr. Alternatively, manually form regular triangular dunes. 
		Note: Regular triangular dunes will yield regular patterns of hyporheic exchange, but will not show as much complexity as natural dune/ripple bedforms.</li>
		<li>Once the desired bedforms are achieved, reduce the water flow rate until bed sediment transport slows and bedform characteristics stop changing.
		<ol>
			<li>Visually observe motion of sediment grains comprising the bed, and reduce flow until motion ceases. 
			Note: This will preserve the bed morphology for the duration of the experiment.</li>
			<li>To confirm that slow, episodic motion is not occurring, mark or photograph bedform positions and then observe at a later time. 
			Note: It is only important that bedforms do not move significantly over the time frame of the experiment, so that provides a sufficient observation time to confirm that bedforms are stable.</li>
		</ol>
		</li>
		<li>Adjust flume slope and/or water depth to achieve uniform flow under the reduced flow rate.
		<ol>
			<li>Control channel slope through equipment constructed into the flume, typically either a motorized jack or a hand-crank. Adjust water depth by adding or removing water from the flume. 
			Note: In the experimental setup used here, the entire flume is mounted on a pivot on the downstream end, and the slope is set by a motorized jack at the upstream end.</li>
			<li>While the pump is running, select two longitudinal locations marked with lines perpendicular to the bottom of the flume. At these locations, use a ruler to measure the distance along these perpendicular lines, between the surface of the water and the bottom of the flume. 
			Note: Depending on the flume setup, the bottom of the flume may serve as a better sloped reference line than the bottom of the flume. Selecting a larger longitudinal distance will yield greater accuracy.</li>
			<li>Adjust the slope of the flume and/or the water depth and re-measure until the vertical distance measurements are the same to achieve uniform flow. Measure the sloped horizontal distance along the bottom of the flume between these two longitudinal locations.</li>
		</ol>
		</li>
		<li>Stop the pump and wait for the water to stop moving; this will provide a level surface. Re-measure the distance between the top of the flume and the water surface at each longitudinal location. 
		Note: The channel slope is equal to the difference between these measurements, divided by the sloped horizontal distance between them.</li>
		<li>Re-start the pump.</li>
		<li>Select a test section, which should be a location near the middle or downstream end of the flume where dunes have formed a regular pattern. Ensure that this section encompasses at least one full bedform.</li>
		<li>Measure and record the average sediment depth (D) in the test section with any hand measuring devise (transparent rulers are ideal). For simplicity, use the average distance of a crest and trough to the flume bottom.</li>
		<li>Measure and record the average bedform height in the test section, defined as the difference between the sediment depth at a crest and the sediment depth at a trough with a ruler. Measure several bedforms to obtain a good estimate of the average.</li>
		<li>Again using the ruler, measure and record the average water depth (d) in the test section, defined as the average distance from the water surface to the sand bed. Again, use the average water depth at dune crests and troughs for simplicity.</li>
		<li>Record the channel flowrate (Q) from the flowmeter, and calculate the average velocity as Q / (d *w), where w is the width of the flume and d is the water depth. 
		Note: Our flowmeter is inserted into the recirculation loop of the flume.</li>
		<li>Measure and record the average bedform wavelength in the test section. Typically, measure the wavelength as the distance between successive dune crests.</li>
		<li>Open the Mousedrop simulation (in the NetLogo platform) and check that all measurements are within the variable ranges specified in the simulation user interface. If a measured parameter falls outside of the constraint range, adjust the simulation parameter range by right-clicking on the parameter &#8220;slider&#8221;, selecting &#8220;edit&#8221;, and adjusting the min/max values.</li>
	</ol>
	</li>
	<li>Visualize hyporheic exchange.
	<ol>
		<li>Set the camera in a fixed location (preferably on a tripod) pointed orthogonally to the flume wall with a single bedform in the test section centered in the picture. 
		Note: This will avoid problems of slanted perspective.</li>
		<li>Take a test picture to verify conditions. Adjust the lighting if reflections are a problem.</li>
		<li>Using the syringe and needle, make 2-3 small dye injections near the flume wall. Ensure that these injections form ~2 cm round patches of colored porewater at a variety of vertical and horizontal locations. Use care to minimize the disturbance to the sand bed during the injection. 
		Note: Injections of smaller volumes of dye allow the user see more detail and view individual stream paths.</li>
		<li>Record the start time of the dye injections and take an initial picture. 
		Optional: It can be educational to trace the initial dye fronts with markers on transparency paper, so that the dye movement is easily observable in lab, but these outlines will also block small portions of the dye fronts in pictures, so there is a trade-off.</li>
		<li>Capture the dye front positions at appropriate time intervals. For time lapse photography, use 30 sec intervals to give smooth results.</li>
	</ol>
	</li>
</ol>
3. Simulation
<ol>
	<li>Run Simulation 1: Mousedrop and compare with observed dye transport.
	<ol>
		<li>Open the simulation script named Mousedrop.nlogo. 
		<img alt="Figure 3" src="/files/ftp_upload/53285/53285fig2.jpg" width="600" /> 
		Figure 2.&#160;Mousedrop. This shows where tracers are at 7 different instances in time. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/53285/53285fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a></li>
		<li>Adjust the physical system parameters shown in Table 1 to match flume experimental conditions (specifically: Lambda, BedformHeight, BedDepth, HydrCond, Porosity, ChannelVelocity, Depth, and Slope). Be sure to pay careful attention to units when entering input parameters.</li>
		<li>Adjust sliders Time1, Time2, etc. to indicate times when the simulation tracking color will change. Set these color changes to match observation times in order to facilitate comparison of simulation results with observations. 
		Note: If the Time parameters are all set to 0, the simulation will display a single color throughout.</li>
		<li>After all parameters are set, click the setup button. 
		Note: The bedform should appear in the simulation view.</li>
		<li>Click the mouse-drop button to indicate the starting locations of virtual tracers. Note that multiple locations in the bed may be clicked. Hold the mouse down to release more virtual tracer. When simulating dye movement, use the mouse to either trace dye fronts (the boundary around the dye) or fill in the full area of the dyed region. 
		Note: Introducing more virtual tracer will cause the simulation to run more slowly. The best visual results will vary with computer performance.</li>
		<li>Once all of the virtual tracers have been placed, you can either click the Advance to next time button, which will start the simulation and then stop it at the first time or you can click the go/stop button to begin the simulation indefinitely. Do not re-click the setup button, or the tracers will have to be placed again. 
		Note: Once the simulation starts running, the velocity is calculated for the location of each tracer based on simulation parameters in Equations 4 and 5. The tracer moves according to the velocity field for 100 simulated seconds and then the velocity at the new location is calculated and the procedure is repeated until the tracer leaves the system.</li>
		<li>Optionally, click the go/stop button repeatedly to pause/continue the simulation. Compare the simulated and measured dye distributions at different points in time.</li>
	</ol>
	</li>
	<li>Run Simulation 2: Interface.
	<ol>
		<li>Open the script titled Interface. 
		<img alt="Figure 3" src="/files/ftp_upload/53285/53285fig3.jpg" width="600" /> 
		Figure 3. Interface. This shows 370 tracers flowing through the subsurface using the interface simulation. The tracer paths show where each tracer has been since it was started at the surface water-subsurface interface. Eventually all flow paths should return to the surface water. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/53285/53285fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a> 
		Note: This script introduces virtual tracers on the streambed surface in a flux-weighted manner based on calculated subsurface velocities. This provides a visual representation of the relative amounts of water flowing into (and out of) the streambed at different locations.</li>
		<li>Begin by clicking setup followed by go/stop. 
		Note: This will run the simulation with the default settings. The re-drop? switch is initially set to off, so the cumulative residence time distribution will be plotted as time passes.</li>
		<li>After observing the simulation with the default parameters, click go/stop to stop the simulation.</li>
		<li>Change one or more parameters and then click setup followed by go/stop. 
		Note: This will restart the simulation with the parameters that have been selected.</li>
	</ol>
	</li>
</ol>

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A., Cardenas, M. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Heat+transport+in+hyporheic+zones+due+to+bedforms:+An+experimental+study.">Heat transport in hyporheic zones due to bedforms: An experimental study.</a> Water Resour Res. 50, 3568-3582 (2014).</li><li>Precht, E., Huettel, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Rapid+wave-driven+advective+pore+water+exchange+in+a+permeable+coastal+sediment.">Rapid wave-driven advective pore water exchange in a permeable coastal sediment.</a> J Sea Res. 51, 93-107 (2004).</li><li>Salehin, M., Packman, A. I., Paradis, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Hyporheic+exchange+with+heterogeneous+streambeds:+Laboratory+experiments+and+modeling.">Hyporheic exchange with heterogeneous streambeds: Laboratory experiments and modeling.</a> Water Resour Res. 40, (2004).</li><li>Cardenas, M. B., Wilson, J. L., Zlotnik, V. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Impact+of+heterogeneity,+bed+forms,+and+stream+curvature+on+subchannel+hyporheic+exchange.">Impact of heterogeneity, bed forms, and stream curvature on subchannel hyporheic exchange.</a> Water Resour Res. 40, (2004).</li><li>Sawyer, A. H., Cardenas, M. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Hyporheic+flow+and+residence+time+distributions+in+heterogeneous+cross-bedded+sediment.">Hyporheic flow and residence time distributions in heterogeneous cross-bedded sediment.</a> Water Resour Res. 45, (2009).</li><li>Boano, F., Camporeale, C., Revelli, R., Ridolfi, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Sinuosity-driven+hyporheic+exchange+in+meandering+rivers.">Sinuosity-driven hyporheic exchange in meandering rivers.</a> Geophys Res Lett. 33, (2006).</li><li>Cardenas, M. 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+model+for+lateral+hyporheic+flow+based+on+valley+slope+and+channel+sinuosity.">A model for lateral hyporheic flow based on valley slope and channel sinuosity.</a> Water Resour Res. 45, (2009).</li><li>Tonina, D., Buffington, J. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Hyporheic+exchange+in+gravel+bed+rivers+with+pool-riffle+morphology:+Laboratory+experiments+and+three-dimensional+modeling.">Hyporheic exchange in gravel bed rivers with pool-riffle morphology: Laboratory experiments and three-dimensional modeling.</a> Water Resour Res. 43, (2007).</li><li>Harbaugh, A. W., Banta, E. R., Hill, M. C., McDonald, M. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> MODFLOW-2000, the US Geological Survey modular ground-water model: User guide to modularization concepts and the ground-water flow process. , (2000).</li></ol>

The authors have nothing to disclose.

In conjunction, the flume demonstration and particle tracking simulations provide a comprehensive introduction to hyporheic flow for a range of audiences. Participants of all levels are provided visual evidence for the occurrence of hyporheic exchange induced by bedforms, and the strong variability in subsurface flow paths under bedforms. These procedures can be used as a simple demonstration of porewater flow for undergraduates or K-12 students, or it can be used in graduate courses in conjunction with a more in-depth presentation of river hydraulics, sediment transport, and the mechanics of hyporheic exchange. Regardless of the level, the use of this simple visualization model as interactive technology allows students to form a deeper understanding of these complex and important phenomena than would be achieved through abstract theory and discussion.
While using these methods, differences between the physical system and the simulation should not be viewed as &#8220;mistakes&#8221;, but instead as a &#8220;teachable moment&#8221;, i.e., the starting point for a discussion that will ultimately lead to greater learning. Students should be led to consider a number of questions, including: What are all of the sources of error (in the model, the measurements, and the laboratory procedure)? Which of these could potentially contribute to the discrepancy between simulations and observations? What simplifying assumptions were made in the formulation of the model? How important are small discrepancies, and do they make the model &#8220;wrong&#8221;? As the statistician George Box famously said, &#8220;Essentially, all models are wrong, but some are useful.&#8221;34 A good scientific model captures certain essential features of a system, thus leading to a better understanding, while it neglects details that are less relevant to the issue at hand. This flume laboratory experiment and accompanying simulation provide an excellent case study for students in understanding both the strengths and weaknesses of a model and of an experimental method. Thus, not only do students gain a greater fluency with core concepts of hyporheic exchange and solute transport, but they have learned about the complementary relationship (and the sometimes complex interaction) between theory-building and data collection, between computer modeling and laboratory experimentation. Furthermore, this coupling of lab and simulation fosters the development of important metacognitive skills35 about how knowledge is gained through the scientific research process, through questioning what we know and how we know it. A growing body of research attests to the effectiveness of teaching metacognitive (a.k.a. higher-order thinking) skills36-38.
There are numerous causes for deviations between observed and simulated tracer trajectories. Excessive lateral movement of the needle during an injection will create a preferential flowpath in the sand, allowing dye to escape directly into the water column. Our velocity equations do not include lateral or longitudinal dispersion. In a flume, the bedform geometry is more asymmetrical than the idealized sinusoid defined in the simulations. Sediments are never entirely homogeneous; variations in packing and sediment sizes will affect the local hydraulic conductivity and porosity. While it is best to minimize bedform migration by reducing the flume pump speed before making dye injections, some migration may still occur. Bedform migration alters the position of the bedform crest relative to the injected dye, thereby changing subsurface hydrodynamics. Experimental flowpaths will therefore always differ from simulations, but the general pattern of tracer movement should not change. Under the experimental conditions used here, there is a strong agreement between the model simulations and the observed dye flow. Additional complexities, such as sediment heterogeneity, fractal bedform topography, groundwater discharge, three dimensional topography, cross-channel flow, and temporal variations in stream flow occur in many natural systems. The dye tracer methods described here can be used to explore the effects of these processes through suitable modification of the flume experiment setup. This approach can be used for research as well as teaching purposes, as flow visualization is commonly used to test hypotheses about governing processes, and can also be used to calculate material fluxes and mass balances, for example hyporheic exchange fluxes between the stream and sediment bed21. Dye tracer methods similar to those described here have been used to determine the effects of streambed morphology, sediment heterogeneity, groundwater discharge, and recharge on hyporheic exchange, as well as to assess related processes such as porewater flows induced by waves39-42.
While the simple flow model used here demonstrated a reasonably faithful reproduction of hyporheic flow under carefully controlled laboratory conditions, its use in modeling complex natural systems is limited. Our scripts were written in the NetLogo programming language here primarily as a teaching tool because it provides a simple, free, and open-source agent-based simulation platform, and because it supports excellent visualizations and easy user manipulation of input parameters, which facilitate learning. Other approaches have been developed to simulate hyporheic exchange with more complex system geometry14,20 and sediment structure43,44. A variety of free/open-source tools (e.g., MODFLOW) and commercial software packages (e.g., COMSOL) use finite difference and finite element methods that may be helpful in modeling hyporheic flow under more complex geometries and with subsurface heterogeneity15,45-48.

As surface water moves in a stream, river, or tidal zone it creates head gradients that drive water into and out of the sediments1. In fluvial systems the portion of the streambed sediments where this exchange occurs is known as the hyporheic zone2,3. This zone is important because many nutrients and pollutants are stored, deposited, or transformed within the hyporheic zone4-9. The amount of time a tracer spends in the sediment is called a residence time. Both residence times and the locations of the flow paths affect the transformation processes. Improved understanding of the processes affecting flow through the sediment is needed to predict solute transport in rivers and address large environmental problems resulting from propagation of materials such as nutrients (e.g., coastal hypoxia10,11). In spite of the significance of hyporheic exchange, it is often not described in undergraduate courses in hydrology, fluid mechanics, hydraulics, etc. Educators wishing to add hyporheic exchange to their courses could find it useful to have experimental and numerical visualizations that clearly show this process.
Stream channel sinuosity, surrounding groundwater levels, and streambed topography (i.e., bars, bedforms, and biogenic mounds) all affect hyporheic exchange to varying degrees12-17. This study focused on bedforms, such as dunes and ripples, which are usually key geomorphic features affecting hyporheic flow14,15. We created a numerical simulation and laboratory experiment to visualize flow through a regular series of bedforms. This simulation is based on a body of previous research relating hyporheic flow paths to readily observable system characteristics15,18-21. As this research forms the scientific background for the simulation, a brief summary of the key aspects of the theory follows. Bedform topography, T(x), is given by:
Equation 1: 
<img alt="Equation 1" src="/files/ftp_upload/53285/53285eq1.jpg" />
where H is twice the amplitude of the bedform, k is the wavenumber, and x is the longitudinal dimension parallel to the average streambed surface. An example of this bedform topography is shown in Figure 1.
<img alt="Figure 1" src="/files/ftp_upload/53285/53285fig1.jpg" /> 
Figure 1. Parameter definitions and settings controlled by the user. In Interface, tracer particles are released in a flux-weighted manner at the water/sediment interface and tracked through the sediment. If show-paths? is &#8220;on&#8221; the water tracers mark where they have been, showing their paths. When a tracer returns to the surface water, this changes the total number of tracers in the system, when re-drop? is set to &#8220;off&#8221;. The cumulative residence time distribution plot shows this change by plotting the ratio of the number of tracers remaining in the sediment bed to the initial number as a function of time. If re-drop? is &#8220;on&#8221; then tracers that leave the system are replaced in the same flux-weighted manner as original particles, and the cumulative plot is disabled. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/53285/53285fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table fo:keep-together.within-page="1">
	<tbody>
		<tr>
			<td>Parameter Name</td>
			<td>Units</td>
			<td colspan="3">Definition</td>
			<td>Interface</td>
			<td>Mousedrop</td>
		</tr>
		<tr>
			<td>Lambda (&#955;)</td>
			<td>cm</td>
			<td colspan="3">Wavelength of bedform (see Figure 1)</td>
			<td><img alt="checkmark" src="/files/ftp_upload/53285/checkmark.jpg" /></td>
			<td><img alt="checkmark" src="/files/ftp_upload/53285/checkmark.jpg" /></td>
		</tr>
		<tr>
			<td>BedformHeight (H)</td>
			<td>cm</td>
			<td colspan="3">Twice the bedform amplitude (see Figure 1)</td>
			<td><img alt="checkmark" src="/files/ftp_upload/53285/checkmark.jpg" /></td>
			<td><img alt="checkmark" src="/files/ftp_upload/53285/checkmark.jpg" /></td>
		</tr>
		<tr>
			<td>BedDepth (D)</td>
			<td>cm</td>
			<td colspan="3">Depth of the sediments (see Figure 1)</td>
			<td><img alt="checkmark" src="/files/ftp_upload/53285/checkmark.jpg" /></td>
			<td><img alt="checkmark" src="/files/ftp_upload/53285/checkmark.jpg" /></td>
		</tr>
		<tr>
			<td>HydrCond (K)</td>
			<td>cm/s</td>
			<td colspan="3">Hydraulic Conductivity</td>
			<td><img alt="checkmark" src="/files/ftp_upload/53285/checkmark.jpg" /></td>
			<td><img alt="checkmark" src="/files/ftp_upload/53285/checkmark.jpg" /></td>
		</tr>
		<tr>
			<td>Porosity (&#952;)</td>
			<td></td>
			<td colspan="3">Porosity</td>
			<td><img alt="checkmark" src="/files/ftp_upload/53285/checkmark.jpg" /></td>
			<td><img alt="checkmark" src="/files/ftp_upload/53285/checkmark.jpg" /></td>
		</tr>
		<tr>
			<td>ChannelVelocity (U)</td>
			<td>cm/s</td>
			<td colspan="3">Mean velocity in the surface water or channel</td>
			<td><img alt="checkmark" src="/files/ftp_upload/53285/checkmark.jpg" /></td>
			<td><img alt="checkmark" src="/files/ftp_upload/53285/checkmark.jpg" /></td>
		</tr>
		<tr>
			<td>Depth (d)</td>
			<td>cm</td>
			<td colspan="3">Water depth (see Figure 1)</td>
			<td><img alt="checkmark" src="/files/ftp_upload/53285/checkmark.jpg" /></td>
			<td><img alt="checkmark" src="/files/ftp_upload/53285/checkmark.jpg" /></td>
		</tr>
		<tr>
			<td>Slope (S)</td>
			<td></td>
			<td colspan="3">Slope of the bedforms and water surface</td>
			<td><img alt="checkmark" src="/files/ftp_upload/53285/checkmark.jpg" /></td>
			<td><img alt="checkmark" src="/files/ftp_upload/53285/checkmark.jpg" /></td>
		</tr>
		<tr>
			<td>NumParticles</td>
			<td></td>
			<td colspan="3">The number of particles released into the system.</td>
			<td><img alt="checkmark" src="/files/ftp_upload/53285/checkmark.jpg" /></td>
			<td></td>
		</tr>
		<tr>
			<td>TimeX (Time1, Time2..)</td>
			<td>min</td>
			<td colspan="3">Time at which each color change occurs</td>
			<td></td>
			<td><img alt="checkmark" src="/files/ftp_upload/53285/checkmark.jpg" /></td>
		</tr>
		<tr>
			<td>Simulation Buttons</td>
			<td></td>
			<td colspan="3">Definition</td>
			<td>Interface</td>
			<td>Mousedrop</td>
		</tr>
		<tr>
			<td>Setup</td>
			<td></td>
			<td colspan="3">Set&#8217;s up the simulation using parameters shown</td>
			<td><img alt="checkmark" src="/files/ftp_upload/53285/checkmark.jpg" /></td>
			<td><img alt="checkmark" src="/files/ftp_upload/53285/checkmark.jpg" /></td>
		</tr>
		<tr>
			<td>go/stop</td>
			<td></td>
			<td colspan="3">Starts and stops the simulation</td>
			<td><img alt="checkmark" src="/files/ftp_upload/53285/checkmark.jpg" /></td>
			<td><img alt="checkmark" src="/files/ftp_upload/53285/checkmark.jpg" /></td>
		</tr>
		<tr>
			<td>Step</td>
			<td></td>
			<td colspan="3">Clicking step causes one time step to pass.&#160; This allows users to slow down the code and see exactly what happens in 100 sec.</td>
			<td><img alt="checkmark" src="/files/ftp_upload/53285/checkmark.jpg" /></td>
			<td></td>
		</tr>
		<tr>
			<td>clear paths</td>
			<td></td>
			<td colspan="3">Clears all he blue particle paths from the screen</td>
			<td><img alt="checkmark" src="/files/ftp_upload/53285/checkmark.jpg" /></td>
			<td><img alt="checkmark" src="/files/ftp_upload/53285/checkmark.jpg" /></td>
		</tr>
		<tr>
			<td>Advance to next time</td>
			<td></td>
			<td colspan="3">This causes the program to run until the next color change time (TimeX)</td>
			<td></td>
			<td><img alt="checkmark" src="/files/ftp_upload/53285/checkmark.jpg" /></td>
		</tr>
		<tr>
			<td>mouse-drop</td>
			<td></td>
			<td colspan="3">This button must be clicked before particles may be placed in the subsurface by clicking on locations in the subsurface.</td>
			<td></td>
			<td><img alt="checkmark" src="/files/ftp_upload/53285/checkmark.jpg" /></td>
		</tr>
		<tr>
			<td>show-paths?</td>
			<td></td>
			<td colspan="3">If show-paths? is &#8220;on&#8221; the water particles leave a trail of blue showing where they have been (see Figure 1).</td>
			<td><img alt="checkmark" src="/files/ftp_upload/53285/checkmark.jpg" /></td>
			<td><img alt="checkmark" src="/files/ftp_upload/53285/checkmark.jpg" /></td>
		</tr>
		<tr>
			<td>re-drop?</td>
			<td></td>
			<td colspan="3">If re-drop? is &#8220;on&#8221; the particles are replaced in a flux weighted manner for every particle, which exits the system, and the cumulative plot does not work.&#160; When a particle exits the hyporheic zone the number of particles in the system decreases if re-drop? is &#8220;off&#8221; (see Figure 1).</td>
			<td><img alt="checkmark" src="/files/ftp_upload/53285/checkmark.jpg" /></td>
			<td></td>
		</tr>
	</tbody>
</table>
Table 1. Hyporheic Parameters and Simulation Controls. Each parameter, button, and slider that can be adjusted by the user is given in this table along with a definition.
In this simulation, two processes induce fluid velocity in the sand bed. The first is due to the interactions of the stream flow with bedforms. The velocity head at the water/sediment interface induced by bedforms is also approximately sinusoidal, and shifted by a quarter wavelength from the bedform itself22. The amplitude of the velocity head function at the surface-subsurface interface has been approximated from measurements as16:
Equation 2: 
<img alt="Equation 2" src="/files/ftp_upload/53285/53285eq2.jpg" />
where U is the mean surface water velocity, g is the gravitational constant, and d is the depth of the water (shown in Figure 1). The velocity head function is then given by:
Equation 3: 
<img alt="Equation 3" src="/files/ftp_upload/53285/53285eq3.jpg" />
This head function can then be used to calculate the bedform-based component of the subsurface velocity functions by solving the Laplace equation with a constant sand bed depth20. The second component of the porewater velocity is determined by the slope of the system, S, which corresponds to a gravitational head gradient that yields flow in the downstream direction proportional to <img alt="S/sqrt(1 + S^2)" src="/files/ftp_upload/53285/53285eq_S_inline.jpg" />. The final functions for porewater velocity are:
Equation 4: 
<img alt="Equation 4" src="/files/ftp_upload/53285/53285eq4.jpg" />
Equation 5: 
<img alt="Equation 5" src="/files/ftp_upload/53285/53285eq5.jpg" />
where u is the longitudinal velocity component, v is the vertical velocity component, K is the average hydraulic conductivity of the sediment, is the average porosity of the sediments, y is the vertical coordinate, and D is the depth of the sediments.
Particle tracking simulations were created, which use the NetLogo modeling language and simulation platform23. The two implementations (Mousedrop.nlogo and Interface.nlogo) use these equations to model hyporheic flow with the same simulation core. The primary difference is the initial locations of the tracer particles. Mousedrop allows the user to place simulated tracer anywhere within the subsurface. Subsurface velocity equations 4 and 5 are used to move the tracer to simulate dye injection experiments. In Interface, tracer is always placed along the surface/subsurface boundary in a flux-weighted manner. This mimics the delivery of dissolved and suspended material from the surface water into the porewater, which is crucial to understanding hyporheic exchange. The tracer then moves within the subsurface until it again reaches the stream water. Tracing the dye paths in the flume and simulating the paths using NetLogo yields the streamlines of the flowfield, as long as the flow conditions and bedform morphology remain steady during the period of observation. Interface.nlogo creates a cumulative residence time distribution, which shows the ratio of the number of tracer particles remaining in the sediments to the initial number of tracer particles placed at time 0 as a function of time.
As discussed in a recent literature survey24, there remains considerable debate within the educational research community about the relative merits of hands-on laboratory experiments versus simulated labs and computer models. On the one hand, some feel that &#8220;hands-on experience is at the heart of learning&#8221;25, and caution that cost-savings arguments may be fueling the replacement of hands-on lab activities by computer-based simulations, to the detriment of student understanding26. On the other hand, some researchers in science/engineering education argue that simulations are at least as effective as traditional hands-on labs27, or discuss the benefits of computer-simulation in fostering student-centered &#8220;discovery learning&#8221;28. While consensus has not been reached, many researchers have concluded that, ideally, computer simulations should supplement, rather than supplant, hands-on laboratory experiments29,30. There have also been initiatives within science and engineering education to simultaneously couple physical experimentation and real-world sensing with computer simulations of the phenomena; see, e.g., &#8220;bifocal modeling&#8221;31.
Students can gain a deeper conceptual knowledge and a better understanding of the scientific research process by interacting with both a physical system, and a computer-based simulation of that system. This procedure involves having students perform a solute transport experiment that demonstrates gravitational and bedform-induced hyporheic exchange flow, and match their own experimental setup and results with a computer simulation of the same phenomena. This comparison facilitates important student-learning outcomes, and a deeper discussion of the scientific method, and interplay between model/theory-building and empirical validation through data collection. After performing this comparison, students can also take advantage of the benefits of computer-based simulation to quickly explore a multitude of alternative scenarios by changing model parameters.

<table border="0" cellpadding="0" cellspacing="0" width="1244">
	<tbody>
		<tr height="21">
			<td height="21" style="height:21px;width:117px;">Flume</td>
			<td style="width:199px;">Engineering Laboratory Design</td>
			<td style="width:125px;">Custom</td>
			<td style="width:804px;">Laboratory flume with clear sides for 24-48 hours</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Flowmeter</td>
			<td>Rosemount&#160;</td>
			<td>8800 vortex&#160;</td>
			<td style="width:804px;">This is located inside the recirculation loop of the flume</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Sand</td>
			<td>US. Silica</td>
			<td>F30</td>
			<td style="width:804px;">Research-grade sand to form a layer 10-20 cm deep throughout the flume</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;">Dye</td>
			<td>Samples from food companies</td>
			<td></td>
			<td style="width:804px;">Water-soluble food grade dye made into an aqueous solution.&#160; Dark colors like red, blue and green work best. (Avoid food dyes in propylene glycol)</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Syringe</td>
			<td>HSW</td>
			<td>4100.000V0</td>
			<td style="width:804px;">5-10 mL, e.g. HSW Norm-Ject 2-part disposable syringe</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Pipetting Needle</td>
			<td>Cadence Science</td>
			<td>7942</td>
			<td>14-gage, 6-in blunt end,&#160; to inject the dye deep into the sand.</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;">Digital Camera</td>
			<td>Any</td>
			<td></td>
			<td style="width:804px;">Digital camera with steady tripod (Time lapse cameras can be used to collect rapid evenly spaced data.)&#160; We used a Nikon D7000.</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Ruler</td>
			<td>Any</td>
			<td></td>
			<td style="width:804px;">Transparent is best.</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Measuring Tape</td>
			<td>Any</td>
			<td></td>
			<td style="width:804px;"></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Netlogo Software</td>
			<td>CCL</td>
			<td></td>
			<td><a href="http://ccl.northwestern.edu/netlogo/">http://ccl.northwestern.edu/netlogo/</a></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Mousedrop.nlogo</td>
			<td>Netlogo Commons</td>
			<td align="right">4259</td>
			<td><a href="http://modelingcommons.org/browse/one_model/4259">http://modelingcommons.org/browse/one_model/4259</a></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Interface.nlogo</td>
			<td>Netlogo Commons</td>
			<td align="right">4258</td>
			<td><a href="http://modelingcommons.org/browse/one_model/4258">http://modelingcommons.org/browse/one_model/4258</a></td>
		</tr>
	</tbody>
</table>

visualizing hyporheic flow through bedforms using dye experiments and simulation

Advective exchange between the pore space of sediments and the overlying water column, called hyporheic exchange in fluvial environments, drives solute transport in rivers and many important biogeochemical processes. To improve understanding of these processes through visual demonstration, we created a hyporheic flow simulation in the multi-agent computer modeling platform NetLogo. The simulation shows virtual tracer flowing through a streambed covered with two-dimensional bedforms. Sediment, flow, and bedform characteristics are used as input variables for the model. We illustrate how these simulations match experimental observations from laboratory flume experiments based on measured input parameters. Dye is injected into the flume sediments to visualize the porewater flow. For comparison virtual tracer particles are placed at the same locations in the simulation. This coupled simulation and lab experiment has been used successfully in undergraduate and graduate laboratories to directly visualize river-porewater interactions and show how physically-based flow simulations can reproduce environmental phenomena. Students took photographs of the bed through the transparent flume walls and compared them to shapes of the dye at the same times in the simulation. This resulted in very similar trends, which allowed the students to better understand both the flow patterns and the mathematical model. The simulations also allow the user to quickly visualize the impact of each input parameter by running multiple simulations. This process can also be used in research applications to illustrate basic processes, relate interfacial fluxes and porewater transport, and support quantitative process-based modeling.

The use of a simulation in conjunction with experiments allows students to observe the similarities and differences between idealized mathematical models and more complex real systems. Figure 4 shows an example comparing dye injection photographs with Mousedrop simulations. The initial photograph is used to determine the placement of the simulated dye tracer at time zero, and then the simulation is run for 34.2 min and compared with a photograph taken at that time. Overall the model does an excellent job of capturing the motion of the dyed water over this time interval. The first dye blob, located on the lee side of the bedform, exits the sediments in both the simulated and experimental systems. The second elongates and travels down forming a crescent shape as it spreads out, so that some of the tracer exits downstream of the original location and some upstream. The last dye blob propagates upstream and some of the tracer travels deeper into the sediments. This demonstrates that hyporheic exchange occurs under bedforms and that the patterns of hyporheic exchange flow relate to bedform geometry. The strong agreement between the simulation and the experiment validates the model equations to a first-order level. This procedure also clearly demonstrates that hyporheic exchange is a significant process that scales with bedform size, and that almost half of the porewater flows upstream under bedforms. On close inspection, however, small differences can be seen between the observed and simulated dye transport. The simulation is smoother than the actual dye pattern and does not extend as deeply into the sediment. These discrepancies result from a combination of measurement errors and second-order physical effects resulting from irregular bedform geometry, variability in sediment packing, etc., as described in Table 2.
<img alt="Figure 4" src="/files/ftp_upload/53285/53285fig4.jpg" /> 
Figure 4. Comparing flume dye fronts to simulations. Dye was injected into the flume and a picture was taken at time 0. Tracers were placed into the subsurface using Mousedrop at the same locations as the dye. Tracers then moved for 34.2 simulation minutes and the simulation is then compared to a picture taken 34.2 min&#160;after the initial picture. The observed dye patterns and the simulations compare well at the later time. There are some discrepancies due to spatial variations in the flow field that are not captured by the model. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/53285/53285fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table fo:keep-together.within-page="1">
	<tbody>
		<tr>
			<td>Common Sources of Discrepancies</td>
			<td colspan="2">Expected Result</td>
		</tr>
		<tr>
			<td>Actual head profile differs from assumed sinusoidal curve</td>
			<td colspan="2">Asymmetry in the porewater flow under the bedform</td>
		</tr>
		<tr>
			<td>Irregular series of bedforms</td>
			<td colspan="2">Potential deviations in the flowfield at the location of observation</td>
		</tr>
		<tr>
			<td>Insufficient sediment bed depth</td>
			<td colspan="2">Vertical compression of the porewater profile</td>
		</tr>
		<tr>
			<td>Non-uniform (i.e., time-varying) flow over the bed</td>
			<td colspan="2">Additional elevation head components that superimpose an additional component of porewater flow (e.g., increased asymmetry of the porewater circulation cell under the bedform.)</td>
		</tr>
		<tr>
			<td>Heterogeneity in packing the sediments</td>
			<td colspan="2">Spatial variability in porewater flow (patches of sediments with higher and lower velocity)</td>
		</tr>
		<tr>
			<td>Significant disruption of sediments when injecting dye</td>
			<td colspan="2">Dye release vertically through the injection hole</td>
		</tr>
		<tr>
			<td>Use of a non-water-soluble dye or insufficient dissolution or mixing of the dye before injection</td>
			<td colspan="2">Pooling of dye in porewater, non-uniform porewater transport or slow mobilization of dye from injection locations.</td>
		</tr>
		<tr>
			<td>Inaccurate measurements (frequently due to units)</td>
			<td colspan="2">This can result in drastically wrong results</td>
		</tr>
		<tr>
			<td>Assumed lack of dispersion in simulation</td>
			<td colspan="2">Some expansion is dye shapes</td>
		</tr>
	</tbody>
</table>
Table 2. Sources of Discrepancy Between Observation and Simulation. A list of the common sources of error is enumerated in this table.

Watch this Scientific Journal Video about Visualizing Hyporheic Flow Through Bedforms Using Dye Experiments and Simulation at JoVE.com

Visualizing Hyporheic Flow Through Bedforms Using Dye Experiments and Simulation

This manuscript describes how to create regular bedforms in a flume, visualize flow through the bedforms, and use computer simulations to simulate the hyporheic flow. The computer simulations compare well with the experimental observations. This coupled simulation and experiment is well-suited for both research and educational purposes.

Advective exchange between the pore space of sediments and the overlying water column, called hyporheic exchange in fluvial environments, drives solute ...

visualizing-hyporheic-flow-through-bedforms-using-dye-experiments

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12.1K Views.  St. Ambrose University. This manuscript describes how to create regular bedforms in a flume, visualize flow through the bedforms, and use computer simulations to simulate the hyporheic flow. The computer simulations compare well with the experimental observations. This coupled simulation and experiment is well-suited for both research and educational purposes. 

Video: Visualizing Hyporheic Flow Through Bedforms Using Dye Experiments and Simulation

Simulation, Fabrication and Characterization of THz Metamaterial Absorbers

Metamaterials (MM), artificial materials engineered to have properties that may not be found in nature, have been widely explored since the first theoretical1 and experimental demonstration2 of their unique properties. MMs can provide a highly controllable electromagnetic response, and to date have been demonstrated in every technologically relevant spectral range including the optical3, near IR4, mid IR5 , THz6 , mm-wave7 , microwave8 and radio9 bands. Applications include perfect lenses10, sensors11, telecommunications12, invisibility cloaks13 and filters14,15. We have recently developed single band16, dual band17 and broadband18 THz metamaterial absorber devices capable of greater than 80% absorption at the resonance peak. The concept of a MM absorber is especially important at THz frequencies where it is difficult to find strong frequency selective THz absorbers19. In our MM absorber the THz radiation is absorbed in a thickness of ~ &lambda;/20, overcoming the thickness limitation of traditional quarter wavelength absorbers. MM absorbers naturally lend themselves to THz detection applications, such as thermal sensors, and if integrated with suitable THz sources (e.g. QCLs), could lead to compact, highly sensitive, low cost, real time THz imaging systems.

This protocol outlines the simulation, fabrication and characterization of THz metamaterial absorbers. Such absorbers, when coupled with an appropriate sensor, have applications in THz imaging and spectroscopy.

Metamaterials (MM),  artificial materials engineered to have properties that may not be found in  nature, have been widely explored since the first ...

Measuring Material Microstructure Under Flow Using 1-2 Plane Flow-Small Angle Neutron Scattering

A new small-angle neutron scattering (SANS) sample environment optimized for studying the microstructure of complex fluids under simple shear flow is presented. The SANS shear cell consists of a concentric cylinder Couette geometry that is sealed and rotating about a horizontal axis so that the vorticity direction of the flow field is aligned with the neutron beam enabling scattering from the 1-2 plane of shear (velocity-velocity gradient, respectively). This approach is an advance over previous shear cell sample environments as there is a strong coupling between the bulk rheology and microstructural features in the 1-2 plane of shear. Flow-instabilities, such as shear banding, can also be studied by spatially resolved measurements. This is accomplished in this sample environment by using a narrow aperture for the neutron beam and scanning along the velocity gradient direction. Time resolved experiments, such as flow start-ups and large amplitude oscillatory shear flow are also possible by synchronization of the shear motion and time-resolved detection of scattered neutrons. Representative results using the methods outlined here demonstrate the useful nature of spatial resolution for measuring the microstructure of a wormlike micelle solution that exhibits shear banding, a phenomenon that can only be investigated by resolving the structure along the velocity gradient direction. Finally, potential improvements to the current design are discussed along with suggestions for supplementary experiments as motivation for future experiments on a broad range of complex fluids in a variety of shear motions.

A shear cell is developed for small-angle neutron scattering measurements in the velocity-velocity gradient plane of shear and is used to characterize complex fluids. Spatially resolved measurements in the velocity gradient direction are possible for studying shear-banding materials. Applications include investigations of colloidal dispersions, polymer solutions, and self-assembled structures.

A new small-angle neutron scattering (SANS) sample environment optimized for studying the microstructure of complex fluids under simple shear flow is ...

Experiments on Ultrasonic Lubrication Using a Piezoelectrically-assisted Tribometer and Optical Profilometer

Friction and wear are detrimental to engineered systems. Ultrasonic lubrication is achieved when the interface between two sliding surfaces is vibrated at a frequency above the acoustic range (20 kHz). As a solid-state technology, ultrasonic lubrication can be used where conventional lubricants are unfeasible or undesirable. Further, ultrasonic lubrication allows for electrical modulation of the effective friction coefficient between two sliding surfaces. This property enables adaptive systems that modify their frictional state and associated dynamic response as the operating conditions change. Surface wear can also be reduced through ultrasonic lubrication. We developed a protocol to investigate the dependence of friction force reduction and wear reduction on the linear sliding velocity between ultrasonically lubricated surfaces. A pin-on-disc tribometer was built which differs from commercial units in that a piezoelectric stack is used to vibrate the pin at 22 kHz normal to the rotating disc surface. Friction and wear metrics including effective friction force, volume loss, and surface roughness are measured without and with ultrasonic vibrations at a constant pressure of 1 to 4 MPa and three different sliding velocities: 20.3, 40.6, and 87 mm/sec. An optical profilometer is utilized to characterize the wear surfaces. The effective friction force is reduced by 62% at 20.3 mm/sec. Consistently with existing theories for ultrasonic lubrication, the percent reduction in friction force diminishes with increasing speed, down to 29% friction force reduction at 87 mm/sec. Wear reduction remains essentially constant (49%) at the three speeds considered.

We present a protocol for using a piezoelectrically-assisted tribometer and optical profilometer to investigate the dependence of ultrasonic wear and friction reduction on linear velocity, contact pressure, and surface properties.

Friction and wear are detrimental to engineered systems. Ultrasonic lubrication is achieved when the interface between two sliding surfaces is vibrated at ...

Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes

The use of Finite Element (FE) simulation software to adequately predict the outcome of sheet metal forming processes is crucial to enhancing the efficiency and lowering the development time of such processes, whilst reducing costs involved in trial-and-error prototyping. Recent focus on the substitution of steel components with aluminum alloy alternatives in the automotive and aerospace sectors has increased the need to simulate the forming behavior of such alloys for ever more complex component geometries. However these alloys, and in particular their high strength variants, exhibit limited formability at room temperature, and high temperature manufacturing technologies have been developed to form them. Consequently, advanced constitutive models are required to reflect the associated temperature and strain rate effects. Simulating such behavior is computationally very expensive using conventional FE simulation techniques.
This paper presents a novel Knowledge Based Cloud FE (KBC-FE) simulation technique that combines advanced material and friction models with conventional FE simulations in an efficient manner thus enhancing the capability of commercial simulation software packages. The application of these methods is demonstrated through two example case studies, namely: the prediction of a material's forming limit under hot stamping conditions, and the tool life prediction under multi-cycle loading conditions.

The following paper presents a novel FE simulation technique (KBC-FE), which reduces computational cost by performing simulations on a cloud computing environment, through the application of individual modules. Moreover, it establishes a seamless collaborative network between world leading scientists, enabling the integration of cutting edge knowledge modules into FE simulations.

The use of Finite Element (FE) simulation software to adequately predict the outcome of sheet metal forming processes is crucial to enhancing the ...

Study of Siphon Breaker Experiment and Simulation for a Research Reactor

Under the design conditions of a research reactor, the siphon phenomenon induced by pipe rupture can cause continuous outward flow of water. To prevent this outflow, a control device is required. A siphon breaker is a type of safety device that can be utilized to control the loss of coolant water effectively.
To analyze the characteristics of siphon breaking, a real-scale experiment was conducted. From the results of the experiment, it was found that there are several design factors that affect the siphon breaking phenomenon. Therefore, there is a need to develop a theoretical model capable of predicting and analyzing the siphon breaking phenomenon under various design conditions. Using the experimental data, it was possible to formulate a theoretical model that accurately predicts the progress and the result of the siphon breaking phenomenon. The established theoretical model is based on fluid mechanics and incorporates the Chisholm model to analyze two-phase flow. From Bernoulli&#39;s equation, the velocity, quantity, undershooting height, water level, pressure, friction coefficient, and factors related to the two-phase flow could be obtained or calculated. Moreover, to utilize the model established in this study, a siphon breaker analysis and design program was developed. The simulation program operates on the theoretical model basis and returns the result as a graph. The user can confirm the possibility of the siphon breaking by checking the shape of the graph. Furthermore, saving the entire simulation result is possible and it can be used as a resource for analyzing the real siphon breaking system.
In conclusion, the user can confirm the status of the siphon breaking and design the siphon breaker system using the program developed in this study.

The siphon breaking phenomenon was investigated experimentally and a theoretical model was proposed. A simulation program based on the theoretical model was developed and the results of the simulation program were compared with experimental results. It was concluded that the results of the simulation program matched the experimental results well.

Under the design conditions of a research reactor, the siphon phenomenon induced by pipe rupture can cause continuous outward flow of water. To prevent ...

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 ...

A 100 KW Class Applied-field Magnetoplasmadynamic Thruster

Applied-field magnetoplasmadynamic thrusters (AF-MPD thrusters) are hybrid accelerators in which electromagnetic and gas dynamic processes accelerate plasma to high speed; they have considerable potential for future space applications with the significant advantages of high specific impulse and thrust density. In this paper, we present a series of protocols for designing and manufacturing a 100 kW class of AF-MPD thruster with water-cooling structures, a 130 V maximum discharge voltage, a 800 A maximum discharge current, and a 0.25 T maximum strength of magnetic field. A hollow tantalum tungsten cathode acts as the only propellant inlet to inhibit the radial discharge, and it is positioned axially at the rear of the anode in order to relieve anode starvation. A cylindrical divergent copper anode is employed to decrease anode power deposition, where the length has been reduced to decrease the wall-plasma connecting area. Experiments utilized a vacuum system that can achieve a working vacuum of 0.01 Pa for a total propellant mass flow rate lower than 40 mg/s and a target thrust stand. The thruster tests were carried out to measure the effects of the working parameters such as propellant flow rates, the discharge current, and the strength of applied magnetic field on the performance and to allow appropriate analysis. The thruster could be operated continuously for significant periods of time with little erosion on the hollow cathode surface. The maximum power of the thruster is 100 kW, and the performance of this water-cooled configuration is comparable with that of thrusters reported in the literature.

The goal of this protocol is to introduce the design of a 100 kW class applied-field magnetoplasmadynamic thruster and relevant experimental methods.

Applied-field magnetoplasmadynamic thrusters (AF-MPD thrusters) are hybrid accelerators in which electromagnetic and gas dynamic processes accelerate ...

Visualizing Uniaxial-strain Manipulation of Antiferromagnetic Domains in Fe1+YTe Using a Spin-polarized Scanning Tunneling Microscope

The quest to understand correlated electronic systems has pushed the frontiers of experimental measurements toward the development of new experimental techniques and methodologies. Here we use a novel home-built uniaxial-strain device integrated into our variable temperature scanning tunneling microscope that enables us to controllably manipulate in-plane uniaxial strain in samples and probe their electronic response at the atomic scale. Using scanning tunneling microscopy (STM) with spin-polarization techniques, we visualize antiferromagnetic (AFM) domains and their atomic structure in Fe1+yTe samples, the parent compound of iron-based superconductors, and demonstrate how these domains respond to applied uniaxial strain. We observe the bidirectional AFM domains in the unstrained sample, with an average domain size of ~50-150 nm, to transition into a single unidirectional domain under applied uniaxial strain. The findings presented here open a new direction to utilize a valuable tuning parameter in STM, as well as other spectroscopic techniques, both for tuning the electronic properties as for inducing symmetry breaking in quantum material systems.

Visualizing Uniaxial-strain Manipulation of Antiferromagnetic Domains in Fe1+YTe Using a Spin-polarized Scanning Tunneling Microscope

Using uniaxial strain combined with spin-polarized scanning tunneling microscopy, we visualize and manipulate the antiferromagnetic domain structure of Fe1+yTe, the parent compound of iron-based superconductors.

The quest to understand correlated electronic systems has pushed the frontiers of experimental measurements toward the development of new experimental ...

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation

Traditional U-turn designs can improve operational features obviously, while U-turn diversions and merge segments still cause traffic congestion, conflicts, and delays. An exclusive spur dike U-turn lane design (ESUL) is proposed here to solve the disadvantages of traditional U-turn designs. To evaluate the operation performance of ESUL, a traffic simulation protocol is needed. The whole simulation process includes five steps: data collection, data analysis, simulation model build, simulation calibration, and sensitive analysis. Data collection and simulation model build are two critical steps and are described later in greater detail. Three indexes (travel time, delay, and number of stops) are commonly used in the evaluation, and other parameters can be measured from the simulation according to experimental needs. The results show that the ESUL significantly diminishes the disadvantages of traditional U-turn designs. The simulation can be applied to solve microscopic traffic problems, such as in single or several adjacent intersections or short segments. This method is not suitable for larger scale road networks or evaluations without data collection.

This protocol describes the process of solving a microscopic traffic problem with simulation. The whole process contains a detailed description of data collection, data analysis, simulation model build, simulation calibration, and sensitive analysis. Modifications and troubleshooting of the method are also discussed.

Traditional U-turn designs can improve operational features obviously, while U-turn diversions and merge segments still cause traffic congestion, ...

Automated Delivery of Microfabricated Targets for Intense Laser Irradiation Experiments

Described is an experimental procedure that enables high-power laser irradiation of microfabricated targets. Targets are brought to the laser focus by a closed feedback loop that operates between the target manipulator and a ranging sensor. The target fabrication process is explained in detail. Representative results of MeV-level proton beams generated by irradiation of 600 nm thick gold foils at a rate of 0.2 Hz are given. The method is compared with other replenishable target systems and the prospects of increasing the shot rates to above 10 Hz are discussed.

A protocol is presented for automated irradiation of thin gold foils with high intensity laser pulses. The protocol includes a step-by-step description of the micromachining target fabrication process and a detailed guide for how targets are brought to the laser&#39;s focus at a rate of 0.2 Hz.

Described is an experimental procedure that enables high-power laser irradiation of microfabricated targets. Targets are brought to the laser focus by a ...