Name: visualizing hyporheic flow through bedforms using dye experiments and simulation
Uploaded: 2015-11-18T15:00:00.000+00:00
Duration: 9 min 49 s
Description: Watch this Scientific Journal Video about Visualizing Hyporheic Flow Through Bedforms Using Dye Experiments and Simulation at JoVE.com

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.bdigitaluss.remotexs.co/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.bdigitaluss.remotexs.co/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.bdigitaluss.remotexs.co/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><tbody><tr><td>Flume</td><td>Engineering Laboratory Design</td><td>Custom</td><td>Laboratory flume with clear sides for 24-48 hours. Alternatively a small teaching flume can be constructed for under 300 dollars following the guidelines provided in our supplementary materials.</td></tr><tr><td>Flowmeter</td><td>Rosemount&amp;nbsp;</td><td>8800 vortex&amp;nbsp;</td><td>This is located inside the recirculation loop of the flume</td></tr><tr><td>Sand</td><td>US. Silica</td><td>F30</td><td>Research-grade sand to form a layer 10-20 cm deep throughout the flume</td></tr><tr><td>Dye</td><td>Samples from food companies</td><td></td><td>Water-soluble food grade dye made into an aqueous solution. Dark colors like red, blue and green work best. (Avoid food dyes in propylene glycol.)</td></tr><tr><td>Syringe</td><td>HSW</td><td>4100.000V0</td><td>5-10 ml, &lt;em&gt;e.g.&lt;/em&gt; HSW Norm-Ject 2-part disposable syringe</td></tr><tr><td>Pipetting Needle</td><td>Cadence Science</td><td>7942</td><td>14-gage, 6-in blunt end,&amp;nbsp; to inject the dye deep into the sand.</td></tr><tr><td>Digital Camera</td><td>Any</td><td></td><td>Digital camera with steady tripod. (Time lapse cameras can be used to collect rapid evenly spaced data.) We used a Nikon D7000.</td></tr><tr><td>Ruler</td><td>Any</td><td></td><td>Transparent is best.</td></tr><tr><td>Measuring Tape</td><td>Any</td><td></td><td></td></tr><tr><td>Netlogo Software</td><td>CCL</td><td></td><td>&lt;a href=&quot;http://ccl.northwestern.edu/netlogo/&quot;&gt;http://ccl.northwestern.edu/netlogo/&lt;/a&gt;</td></tr><tr><td>Mousedrop.nlogo</td><td>Netlogo Commons</td><td>4259</td><td>&lt;a href=&quot;http://modelingcommons.org/browse/one_model/4259&quot;&gt;http://modelingcommons.org/browse/one_model/4259&lt;/a&gt;</td></tr><tr><td>Interface.nlogo</td><td>Netlogo Commons</td><td>4258</td><td>&lt;a href=&quot;http://modelingcommons.org/browse/one_model/4258&quot;&gt;http://modelingcommons.org/browse/one_model/4258&lt;/a&gt;</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.bdigitaluss.remotexs.co/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

Nanyang Technological University

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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

Image-based Lagrangian Particle Tracking in Bed-load Experiments

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

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

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

Environment

Visualization of Flow Field Around a Vibrating Pipeline Within an Equilibrium Scour Hole

An experimental method is presented in this paper to facilitate visualization of the detailed flow fields and determination of the near-boundary shear and normal stresses within an equilibrium scour hole induced by a vibrating pipeline. This method involves the implementation of a pipeline vibration system in a straight flume, a time-resolved particle image velocimetry (PIV) system for pipeline displacement tracking and flow fields measurements. The displacement time-series of the vibrating pipeline are obtained by using the cross-correlation algorithms. The steps for processing raw particle laden images obtained by using the time-resolved PIV are described. The detailed instantaneous flow fields around the vibrating pipeline at different vibrating phases are calculated by using a multiple-time-interval cross-correlation algorithm to avoid displacement bias error in the flow regions with a large velocity gradient. By applying the wavelet transform technique, the captured images that have the same vibrating phase are accurately cataloged before the phase-averaged velocity fields are obtained. The key advantages of the flow measurement technique described in this paper are that it has a very high temporal and spatial resolution and can be simultaneously used to obtain the pipeline dynamics, flow fields, and near-boundary flow stresses. By using this technique, more in-depth studies of the 2-dimensional flow field in a complex environment, such as that around a vibrating pipeline, can be conducted to better understand the associated sophisticated scour mechanism.

The goal of the protocol is to enable visualization of the detailed flow fields and determination of the near-boundary shear and normal stresses within an equilibrium scour hole induced by a vibrating pipeline.

An experimental method is presented in this paper to facilitate visualization of the detailed flow fields and determination of the near-boundary shear and ...

Combining Fluidic Devices with Microscopy and Flow Cytometry to Study Microbial Transport in Porous Media Across Spatial Scales

Understanding the transport, dispersion and deposition of microorganisms in porous media is a complex scientific task comprising topics as diverse as hydrodynamics, ecology and environmental engineering. Modeling bacterial transport in porous environments at different spatial scales is critical to better predict the consequences of bacterial transport, yet current models often fail to up-scale from laboratory to field conditions. Here, we introduce experimental tools to study bacterial transport in porous media at two spatial scales. The aim of these tools is to obtain macroscopic observables (such as breakthrough curves or deposition profiles) of bacteria injected into transparent porous matrices. At the small scale (10-1000 &#181;m), microfluidic devices are combined with optical video-microscopy and image processing to obtain breakthrough curves and, at the same time, to track individual bacterial cells at the pore scale. At larger scale, flow cytometry is combined with a self-made robotic dispenser to obtain breakthrough curves. We illustrate the utility of these tools to better understand how bacteria are transported in complex porous media such as the hyporheic zone of streams. As these tools provide simultaneous measurements across scales, they pave the way for mechanism-based models, critically important for upscaling. Application of these tools may not only contribute to the development of novel bioremediation applications but also shed new light on the ecological strategies of microorganisms colonizing porous substrates.

Breakthrough curves (BTCs) are efficient tools to study the transport of bacteria in porous media. Here we introduce tools based on fluidic devices in combination with microscopy and flow cytometric counting to obtain BTCs.

Understanding the transport, dispersion and deposition of microorganisms in porous media is a complex scientific task comprising topics as diverse as ...

The Diffusion of Passive Tracers in Laminar Shear Flow

A simple method to experimentally observe and measure the dispersion of a passive tracer in a laminar fluid flow is described. The method consists of first injecting fluorescent dye directly into a pipe filled with distilled water and allowing it to diffuse across the cross-section of the pipe to obtain a uniformly distributed initial condition. Following this period, the laminar flow is activated with a programmable syringe pump to observe the competition of advection and diffusion of the tracer through the pipe. Asymmetries in the tracer distribution are studied and correlations between the pipe cross-section and the shape of the distribution is shown: thin channels (aspect ratio &#60;&#60; 1) produce tracers arriving with sharp fronts and tapering tails (front-loaded distributions), while thick channels (aspect ratio ~1) present the opposite behavior (back-loaded distributions). The experimental procedure is applied to capillary tubes of various geometries and is particularly relevant to microfluidic applications by dynamical similarity.

A protocol for the study of the diffusion of passive tracers in laminar pressure-driven flow is presented. The procedure is applicable to various capillary pipe geometries.

A simple method to experimentally observe and measure the dispersion of a passive tracer in a laminar fluid flow is described. The method consists of ...

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

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

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

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

Methods for Characterizing the Co-development of Biofilm and Habitat Heterogeneity

Biofilms are surface-attached microbial communities that have complex structures and produce significant spatial heterogeneities. Biofilm development is strongly regulated by the surrounding flow and nutritional environment. Biofilm growth also increases the heterogeneity of the local microenvironment by generating complex flow fields and solute transport patterns. To investigate the development of heterogeneity in biofilms and interactions between biofilms and their local micro-habitat, we grew mono-species biofilms of Pseudomonas aeruginosa and dual-species biofilms of P. aeruginosa and Escherichia coli under nutritional gradients in a microfluidic flow cell. We provide detailed protocols for creating nutrient gradients within the flow cell and for growing and visualizing biofilm development under these conditions. We also present protocols for a series of optical methods to quantify spatial patterns in biofilm structure, flow distributions over biofilms, and mass transport around and within biofilm colonies. These methods support comprehensive investigations of the co-development of biofilm and habitat heterogeneity.

Biofilms have complex interactions with their surrounding environment. To comprehensively investigate biofilm-environment interactions, we present here a series of methods to create heterogeneous chemical environment for biofilm development, to quantify local flow velocity, and to analyze mass transport in and around biofilm colonies.

Biofilms are surface-attached microbial communities that have complex structures and produce significant spatial heterogeneities. Biofilm development is ...

Bioengineering

Window on a Microworld: Simple Microfluidic Systems for Studying Microbial Transport in Porous Media

Microbial growth and transport in porous media have important implications for the quality of groundwater and surface water, the recycling of nutrients in the environment, as well as directly for the transmission of pathogens to drinking water supplies. Natural porous media is composed of an intricate physical topology, varied surface chemistries, dynamic gradients of nutrients and electron acceptors, and a patchy distribution of microbes. These features vary substantially over a length scale of microns, making the results of macro-scale investigations of microbial transport difficult to interpret, and the validation of mechanistic models challenging. Here we demonstrate how simple microfluidic devices can be used to visualize microbial interactions with micro-structured habitats, to identify key processes influencing the observed phenomena, and to systematically validate predictive models. Simple, easy-to-use flow cells were constructed out of the transparent, biocompatible and oxygen-permeable material poly(dimethyl siloxane). Standard methods of photolithography were used to make micro-structured masters, and replica molding was used to cast micro-structured flow cells from the masters. The physical design of the flow cell chamber is adaptable to the experimental requirements: microchannels can vary from simple linear connections to complex topologies with feature sizes as small as 2 &mu;m. Our modular EcoChip flow cell array features dozens of identical chambers and flow control by a gravity-driven flow module. We demonstrate that through use of EcoChip devices, physical structures and pressure heads can be held constant or varied systematically while the influence of surface chemistry, fluid properties, or the characteristics of the microbial population is investigated. Through transport experiments using a non-pathogenic, green fluorescent protein-expressing Vibrio bacterial strain, we illustrate the importance of habitat structure, flow conditions, and inoculums size on fundamental transport phenomena, and with real-time particle-scale observations, demonstrate that microfluidics offer a compelling view of a hidden world.

Microfluidic devices can be used to visualize complex natural processes in real time and at the appropriate physical scales. We have developed a simple microfluidic device that mimics key features of natural porous media for studying growth and transport of bacteria in the subsurface.

Microbial growth and transport in porous media have important implications for the quality of groundwater and surface water, the recycling of nutrients in ...

Biology

Flow Visualization in a Water Tunnel: Observing the Leading-edge Vortex Over a Delta Wing

Source: Jose Roberto Moreto, Gustaaf Jacobs and Xiaofeng Liu, Department of Aerospace Engineering, San Diego State University, San Diego, California
The delta wing, shown in Figure 1D, is a popular design in high-speed airplanes due to its superb performance in transonic and supersonic flight regimes. This type of wing has a small aspect ratio and high sweep angle, which reduces drag at high subsonic, transonic and supersonic flight regimes. The aspect ratio is defined as the wing span divided by the average chord <img align="middle" src="/files/ftp_upload/10459/10459eq1a.jpg" />.
An important advantage of the delta wing is its high stall angle. The stall of a delta wing is delayed compared to the stall of a high aspect ratio wing. This is because the lift of a delta wing is enhanced by the leading-edge vortex over the wing.
An effective way to observe this vortex flow phenomenon and study the vortex breakdown in a delta wing is by visualizing the flow in a water tunnel. By injecting dye in the flow surrounding a model from dye ports on the leading-edge, the vortex development and breakdown can be observed and its position measured. The data can also be used to estimate the stall angle.
<img src="/files/ftp_upload/10459/10459fig1.jpg" />
Figure 1. Typical wing planform shapes: A) Rectangular, with constant chord along span, B) elliptical, C) tapered, with variable chord along the span, and D) delta wing, an aft-swept wing with zero taper ratio.

Flow in a Water Tunnel: Leading-edge Vortex over a Delta Wing

Source: Jose Roberto Moreto, Gustaaf Jacobs and Xiaofeng Liu, Department of Aerospace Engineering, San Diego State University, San Diego, California
The ...

JoVE Science Education

Aeronautical Engineering

Surface Dye Flow Visualization: A Qualitative Method to Observe Streakline Patterns in Supersonic Flow

Flow visualization around or on a body is an important tool in aerodynamics research. It provides a method to qualitatively and quantitatively study flow structure, and it also helps researchers theorize and verify fluid flow behavior. Flow visualization can be divided into two categories: off-the-surface visualization and surface flow visualization. Off-the-surface flow visualization techniques involve determining the flow characteristics around the body of interest. They include but are not restricted to particle image velocimetry (PIV), Schlieren imaging, and smoke flow visualization. These techniques can provide qualitative as well as quantitative data on the flow around a body. However, these techniques are generally expensive and difficult to set-up. Surface flow visualization techniques, on the other hand, involve coating the body of interest with a dye to study flow on the surface. These techniques, which are more invasive in practice, include dye flow visualization and, more recently, use pressure-sensitive paint, which gives a detailed image of the flow on the body&#39;s surface. This allows researchers to visualize different flow features, including laminar bubbles, boundary layer transitions, and flow separation. Dye flow visualization, the technique of interest in the current experiment, provides a qualitative picture of the surface flow and is one of the simplest and most cost-effective surface flow visualization methods, specifically for visualizing gaseous flows on a body.
In this experiment, the surface flow behavior on six bodies are studied in supersonic flow. The streakline patterns are obtained using the dye flow visualization technique, and the flow paths, degree of flow attachment and separation, and location and type of shocks are identified and studied from the flow images.

Surface Dye Flow: Streakline Patterns in Supersonic Flow

Flow visualization around or on a body is an important tool in aerodynamics research. It provides a method to qualitatively and quantitatively study flow ...

Visualization of Flow Past a Bluff Body

Source: Ricardo Mejia-Alvarez, Hussam Hikmat Jabbar and Mahmoud N. Abdullatif, Department of Mechanical Engineering, Michigan State University, East Lansing, MI
Owing to the non-linear nature of its governing laws, fluid motion induces complicated flow patterns. Understanding the nature of these patterns has been the subject of intense scrutiny for centuries. Although personal computers and supercomputers are extensively used to deduce fluid flow patterns, their capabilities are still insufficient to determine the exact flow behavior for complex geometries or highly inertial flows (e.g. when momentum dominates over viscous resistance). With this in mind, a multitude of experimental techniques to make flow patterns evident have been developed that can reach flow regimes and geometries inaccessible to theoretical and computational tools.
This demonstration will investigate fluid flow around a bluff body. A bluff body is an object that, due to its shape, causes separated flow over most of its surface. This is in contrast to a streamlined body, like an airfoil, which is aligned in the stream and causes less flow separation. The purpose of this study is to use hydrogen bubbles as a method of visualizing flow patterns. The hydrogen bubbles are produced via electrolysis using a DC power source by submerging its electrodes in the water. Hydrogen bubbles are formed in the negative electrode, which needs to be a very fine wire to ensure that the bubbles remain small and track fluid motion more effectively. This method is suitable for steady and unsteady laminar flows, and is based on the basic flow lines that describe the nature of the flow around objects. [1-3]
This paper focuses on describing the implementation of the technique, including details about the equipment and its installation. Then, the technique is used to demonstrate the use of two of the basics flow lines to characterize the flow around a circular cylinder. These flow lines are used to estimate some important flow parameters like flow velocity and the Reynolds number, and to determine flow patterns.

Flow Around a Bluff Body, Bubbles and Von Karman Vortex Street

Source: Ricardo Mejia-Alvarez, Hussam Hikmat Jabbar and Mahmoud N. Abdullatif, Department of Mechanical Engineering, Michigan State University, East ...