I think I've found the right material to use in high-speed video of sediment transport.
A link for aggregate readers that may cut things off: https://vimeo.com/60882601
The color-coded media supplied by Little River Research and Design looks absolutely gorgeous. I got some the other day and I just had to grab a few scoops to shoot some video friday afternoon:
Color-Coded Sediment from Matt Kuchta on Vimeo.
Some new material arrived in the lab today. Just a quick clip of a scoop of the color-coded sediment settling onto the bottom of a glass of water.
Showing posts with label fluid dynamics. Show all posts
Showing posts with label fluid dynamics. Show all posts
Saturday, March 02, 2013
Thursday, February 16, 2012
Thurs-Demo:The One that Glitters
I've been thinking of a pop culture reference for today's post. I was mulling the thought of using a Tolkien-Aragorn reference, or perhaps a Twilight-Vampire comment. Instead, I'll just let you know that pearlescent paint/ink has some great potential for demonstrating fluid dynamics.
Mix a water-based pearlescent paint (this one is probably made with powdered aluminum) with water and stir. Watch lovely Von-Karmen vortices whirl about. See the transition from laminar to turbulent flow. All this can be yours for a few bucks worth of paint.
Or, if you're so inclined, mix a very thin suspension in water and then use the glittery parts to help track where the water goes.
Mix a water-based pearlescent paint (this one is probably made with powdered aluminum) with water and stir. Watch lovely Von-Karmen vortices whirl about. See the transition from laminar to turbulent flow. All this can be yours for a few bucks worth of paint.
Or, if you're so inclined, mix a very thin suspension in water and then use the glittery parts to help track where the water goes.
Wednesday, January 18, 2012
Froude!
Something else that demonstrates an interesting conceptual overlap - the design of battleships and fluid forces exerted on an object. Geology in Motion has a fun little tale about how the "Froude Number" (another wonderful word) came to be.
Friday, October 07, 2011
Friday Flume Fun
Earlier I posted about some interesting data that I got from setting up a simple flume/channel. At first I was rather excited - maybe there was some aspect of fluid dynamics that I was seeing (flow separation, turbulence, etc.). But this is SCIENCE!, so I have to be able to do a few more things before I can do anything more than speculate.
I hooked up another accelerometer to the pump's outlet (just above the water line) and was confronted with something I expected:
The blue line represents the accelerometer attached near the pump, and the red line is the accelerometer attached to the rain gutter flume. At first glance, it appears that the variation is all due to the pump. Changes in the blue line correspond quite directly with changes in the blue line (though smaller). As one goes up, so does the other. At this point, I was pretty convinced that the only thing I was observing in the first test was some behavior of the pump. But, correlation is not causation, so I looked at the data a little more closely:
Well, nothing really new, but the shapes of each "sine wave" is not quite symmetric. So what about just one wave?
Hmm... so, if each accelerometer changes in an identical way, I should expect that values of one curve will vary the same way as the other curve. If I plot values of one accelerometer against the values of the other accelerometer (at the same time interval) I should see a nice correlation - as one value increases, the other value will also increase (and decrease as the other decreases). So I plotted the values off the accelerometer attached to the pump versus the values off the accelerometer attached to the flume.
Okay, now we've got something a little more interesting than just wobbles produced by a water pump. There is clearly something more complex than a simple "change in one variable producing a change in another." You can see what look like two "clusters" of points. Changes in one variable correspond with two possible changes in another. The blue points are values from one accelerometer versus another as the values "fall" to more negative values, while the red points are values from when the values were increasing. Grabbing about 300 seconds worth of data makes this pattern even easier to see:
So, instead of just being some simple "noise" produced by the water pump, I've stumbled upon something a little more complex. In fact, this isn't a bad example of what's called a "non-linear" system. Specifically, I think this system is displaying "hysteresis." Where there are two possible values of a dependent variable, given one value of another. A change in the system (the water pump) causes another change (on the flume channel), but increasing changes behave differently than decreasing changes. There is complexity in the system, but it's not a complex setup. Two accelerometers dumping data to a computer, a pump, some water and a four-foot length of rain gutter is all you need.
Keep in mind, I have only begun what we could call "hypothesizing" here. I am not at a point where I could definitively point to a specific process or mechanism (beyond some kind of "change" in the water pump). Nor is this concept at the point where it goes beyond simply "explaining" a process to constructing a hypothesis that both explains observed behavior and predicts future behavior. In order to be true science, these criteria MUST be fulfilled. But I have preliminary data and observations. I have a few ideas of what might be going on.
Those of you following along at home might be aware that my treatment of the data (and hypothetical mechanisms) has been very shallow. I have not begun to really describe WHAT the accelerometers are measuring. Is it simply a subtle tilting to or away from vertical (thus increasing or decreasing the influence of gravitational acceleration)? Or is some of the acceleration coming from tiny impacts of flowing water or vibrations from the pump? And, of course, what is causing the apparent hysteresis? Is it some difference between increasing/decreasing flow and shear resistance from the water? I'm not sure, but as you can see, this stage of the study is generating lots of questions. That's good. The more questions that fall out now, the easier it will be to pull the system apart (literally and figuratively) to see what's going on. And then, perhaps, I can start to make explanations about what I'm seeing and make predictions about what I will see when I start to make additional changes. Changes like adding sediment.
Whew! That's a lot of thinking. And we haven't even begun to watch sediment transported as bedload yet. But, if you start diving into current research into papers like the one Brian Romans described, you can see what kinds of questions the researchers had to answer before they could even begin to try and account for sediment transport in streams.
So, what would you do next? Scale things up? Reduce complexity in the setup? Eliminate the pump? Why not all of them? And, of course, lets dump some sand in there, too. Keep in mind, this is "science" and if we are to truly understand this system, we must be able to both EXPLAIN what we observe, and PREDICT how the system will behave under different circumstances. Ideally, we'll be able to apply this idea to other systems (like real rivers). Or, at least, gain a better appreciation for complex behaviors.
Who's up for a little chaos theory?
I hooked up another accelerometer to the pump's outlet (just above the water line) and was confronted with something I expected:
The blue line represents the accelerometer attached near the pump, and the red line is the accelerometer attached to the rain gutter flume. At first glance, it appears that the variation is all due to the pump. Changes in the blue line correspond quite directly with changes in the blue line (though smaller). As one goes up, so does the other. At this point, I was pretty convinced that the only thing I was observing in the first test was some behavior of the pump. But, correlation is not causation, so I looked at the data a little more closely:
Well, nothing really new, but the shapes of each "sine wave" is not quite symmetric. So what about just one wave?
Hmm... so, if each accelerometer changes in an identical way, I should expect that values of one curve will vary the same way as the other curve. If I plot values of one accelerometer against the values of the other accelerometer (at the same time interval) I should see a nice correlation - as one value increases, the other value will also increase (and decrease as the other decreases). So I plotted the values off the accelerometer attached to the pump versus the values off the accelerometer attached to the flume.
Okay, now we've got something a little more interesting than just wobbles produced by a water pump. There is clearly something more complex than a simple "change in one variable producing a change in another." You can see what look like two "clusters" of points. Changes in one variable correspond with two possible changes in another. The blue points are values from one accelerometer versus another as the values "fall" to more negative values, while the red points are values from when the values were increasing. Grabbing about 300 seconds worth of data makes this pattern even easier to see:
So, instead of just being some simple "noise" produced by the water pump, I've stumbled upon something a little more complex. In fact, this isn't a bad example of what's called a "non-linear" system. Specifically, I think this system is displaying "hysteresis." Where there are two possible values of a dependent variable, given one value of another. A change in the system (the water pump) causes another change (on the flume channel), but increasing changes behave differently than decreasing changes. There is complexity in the system, but it's not a complex setup. Two accelerometers dumping data to a computer, a pump, some water and a four-foot length of rain gutter is all you need.
Keep in mind, I have only begun what we could call "hypothesizing" here. I am not at a point where I could definitively point to a specific process or mechanism (beyond some kind of "change" in the water pump). Nor is this concept at the point where it goes beyond simply "explaining" a process to constructing a hypothesis that both explains observed behavior and predicts future behavior. In order to be true science, these criteria MUST be fulfilled. But I have preliminary data and observations. I have a few ideas of what might be going on.
Those of you following along at home might be aware that my treatment of the data (and hypothetical mechanisms) has been very shallow. I have not begun to really describe WHAT the accelerometers are measuring. Is it simply a subtle tilting to or away from vertical (thus increasing or decreasing the influence of gravitational acceleration)? Or is some of the acceleration coming from tiny impacts of flowing water or vibrations from the pump? And, of course, what is causing the apparent hysteresis? Is it some difference between increasing/decreasing flow and shear resistance from the water? I'm not sure, but as you can see, this stage of the study is generating lots of questions. That's good. The more questions that fall out now, the easier it will be to pull the system apart (literally and figuratively) to see what's going on. And then, perhaps, I can start to make explanations about what I'm seeing and make predictions about what I will see when I start to make additional changes. Changes like adding sediment.
Whew! That's a lot of thinking. And we haven't even begun to watch sediment transported as bedload yet. But, if you start diving into current research into papers like the one Brian Romans described, you can see what kinds of questions the researchers had to answer before they could even begin to try and account for sediment transport in streams.
So, what would you do next? Scale things up? Reduce complexity in the setup? Eliminate the pump? Why not all of them? And, of course, lets dump some sand in there, too. Keep in mind, this is "science" and if we are to truly understand this system, we must be able to both EXPLAIN what we observe, and PREDICT how the system will behave under different circumstances. Ideally, we'll be able to apply this idea to other systems (like real rivers). Or, at least, gain a better appreciation for complex behaviors.
Who's up for a little chaos theory?
Thursday, February 10, 2011
Thurs-Demo: The one with Corn Syrup
This week's demo is all about particles falling through viscous fluid. In this case, we'll be dropping some steel ball bearings into corn syrup.
The fluid behavior in the video is mostly laminar - the size of the sphere and viscosity of the fluid don't produce any separation of the flow or turbulence. I wrote a bit about Stoke's Law in a previous post. For particles falling through water, the maximum particle size that will exhibit laminar behavior is around 0.1mm. For the steel spheres, we didn't manage to see any flow separation - but did see interference with the container's edge with really big spheres.
A graph of the resulting velocity shows a nice linear relationship between the square of the sphere's radius and its terminal velocity (except for the largest spheres, where interference with the container's edge slowed them down.
What a Drag! Falling Through Syrup from Matt Kuchta on Vimeo.
The fluid behavior in the video is mostly laminar - the size of the sphere and viscosity of the fluid don't produce any separation of the flow or turbulence. I wrote a bit about Stoke's Law in a previous post. For particles falling through water, the maximum particle size that will exhibit laminar behavior is around 0.1mm. For the steel spheres, we didn't manage to see any flow separation - but did see interference with the container's edge with really big spheres.
A graph of the resulting velocity shows a nice linear relationship between the square of the sphere's radius and its terminal velocity (except for the largest spheres, where interference with the container's edge slowed them down.
Monday, April 12, 2010
Upper Flow Regimes
One of the aspects of sedimentary geology that I enjoy is the connection between sediment transport and deposition. Particularly the dynamics of the moving fluid (usually air or water) and its effects on the material being "pushed" along the base of the moving fluid.
Things like ripples in sand are a result of this dynamic relationship. I've got some more detailed posts on different kinds of bedforms, but today I want to share a video of one particularly intriguing type: antidunes.
Any bedform is going to be a byproduct of the depth and velocity of the moving fluid plus the type of material it is flowing over. For most sand, slowly flowing water will produce small ripples - you have probably seen these in the sand along the shore of a lake or river.
However, once you start trying to move fluids faster and faster over this surface, the fluid exhibits some interesting behaviors. The ratio between the velocity of the fluid to gravity and the flow's depth is referred to as the "Froude" number Fr. I've heard it pronounced "FROOD" and "FROWD." I like the sound of the former myself.
For low velocities, or deep streams, this number is small (typically less than one). But, when a shallow stream is flowing very fast, it can be higher than one. When Fr < 1, it is referred to as "subcritical flow." When it is equal to 1, it is called "critical flow," and if Fr > 1, it is called "supercritical flow." Most ripples and dunes form as a result of subcritical flow. With supercritical flow, however, we start to get other bedforms, including antidunes.
Antidunes are relatively easy to create in an artificial flume. They are more difficult to see in nature:
The above video was taken by a friend of mine while in Hawaii this winter. Water was draining down the beach towards the ocean. The shallow stream started picking up speed and began to form antidunes - which appear in the flowing water as rapids that migrate upstream. Recognizing these processes in the sedimentary record requires some understanding of how they work. I'll have to spend some more time talking about sedimentary structures.
I want to thank Kelly McCullough for capturing the above video - my camera had started to act a little funny and I didn't get many good pictures. In this case, video was the right tool for the job. Kelly's a sci-fi author and all-around neat guy. You should check it out sometime.
Things like ripples in sand are a result of this dynamic relationship. I've got some more detailed posts on different kinds of bedforms, but today I want to share a video of one particularly intriguing type: antidunes.
Any bedform is going to be a byproduct of the depth and velocity of the moving fluid plus the type of material it is flowing over. For most sand, slowly flowing water will produce small ripples - you have probably seen these in the sand along the shore of a lake or river.
However, once you start trying to move fluids faster and faster over this surface, the fluid exhibits some interesting behaviors. The ratio between the velocity of the fluid to gravity and the flow's depth is referred to as the "Froude" number Fr. I've heard it pronounced "FROOD" and "FROWD." I like the sound of the former myself.
Fr = (mean velocity) / (g * depth)1/2
For low velocities, or deep streams, this number is small (typically less than one). But, when a shallow stream is flowing very fast, it can be higher than one. When Fr < 1, it is referred to as "subcritical flow." When it is equal to 1, it is called "critical flow," and if Fr > 1, it is called "supercritical flow." Most ripples and dunes form as a result of subcritical flow. With supercritical flow, however, we start to get other bedforms, including antidunes.
Antidunes are relatively easy to create in an artificial flume. They are more difficult to see in nature:
The above video was taken by a friend of mine while in Hawaii this winter. Water was draining down the beach towards the ocean. The shallow stream started picking up speed and began to form antidunes - which appear in the flowing water as rapids that migrate upstream. Recognizing these processes in the sedimentary record requires some understanding of how they work. I'll have to spend some more time talking about sedimentary structures.
I want to thank Kelly McCullough for capturing the above video - my camera had started to act a little funny and I didn't get many good pictures. In this case, video was the right tool for the job. Kelly's a sci-fi author and all-around neat guy. You should check it out sometime.
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