FYFD

Tag: computational fluid dynamics

  • The Structure of the Blue Whirl

    The Structure of the Blue Whirl

    Several years ago, researchers discovered a new type of flame, the blue whirl. Now computational simulations have helped them untangle the complex structure of this clean-burning flame. Their work shows that the blue whirl is made up of three types of flames, which meet to form a fourth.

    The conical base of the whirl is a fuel-rich flame in which the fuel and oxygen are initially well-mixed. Above that is a diffusion flame, where the fuel and oxygen are initially separate and the flame’s ability to burn is limited by how readily the two mix. Along the sides of the blue whirl is a third flame type, visible only as a faint wisp. Like the first flame, this one is premixed, but it contains much less fuel than oxygen. Finally, those three flames meet in the bright blue ring of the whirl, where the ratio of fuel and oxygen is just right to burn the fuel completely. (Image and research credit: J. Chung et al.; via Science News; submitted by Kam-Yung Soh)

  • Shedding Light on Martian Dust Storms

    Shedding Light on Martian Dust Storms

    In 2018, Mars was enveloped by a global dust storm that lasted for months. Although such storms had been seen before, the 2018 storm offered an unprecedented opportunity for observation from five orbiting spacecraft and two operating landers. As researchers comb through that data, they’re gaining new insights into the mechanisms that drive these extreme events.

    At NASA Ames, a team of researchers used observations of dust columns as input to a simulation of Mars’ global climate, then watched as the digital storm unfolded. Simulations like these have an important advantage over observations: the simulations allow scientists to track the transport of dust from one region to another.

    That dust tracking is critical for some of the team’s results. They found feedback patterns between dust lifting and deposition in different regions. For example, early in the storm dust was largely supplied from the Arabia/Sabaea regions, but once that dust was deposited in the Tharsis region, it kicked off a massive lifting event from Tharsis that put twice as much dust into the atmosphere as had landed there. Later, dust deposited back in Arabia by the Tharsis lofting generated new dust uplifts. As long as more dust got lifted than deposited, the intense storms continued. (Image credits: NASA, T. Bertrand/A. Kling/NASA Ames; research credit: T. Bertrand et al.; see also JGR Planets and AGU; submitted by Kam-Yung Soh)

  • COVID-19 and Outdoor Exercise

    COVID-19 and Outdoor Exercise

    By now you’ve probably come across some blog posts and news articles about a new pre-print study looking at the aerodynamics of running and the potential exposure to exhaled droplets. And you may also have seen articles questioning the accuracy and validity of such simulations. I’ve had several readers submit questions about this, so I dug into both the research and the criticisms, and here are my thoughts:

    Is this study scientifically valid?

    I’ve seen a number of complaints that since this paper hasn’t been peer-reviewed, we shouldn’t trust anything about it. That seems like an unreasonable overreaction to me considering how many studies receive press attention prior to their actual peer-reviewed publication. This is not a random CFD simulation produced by someone who just downloaded a copy of ANSYS Fluent. This work comes from a well-established group of engineers specializing in sports aerodynamics, and long-time readers will no doubt recognize some of their previous publications. Over the past decade, Blocken and his colleagues have become well-known for detailed experimental and simulation work that indicates larger aerodynamic effects in slipstreams than what we generally recognize.

    In this paper, they lay out previous (biological) studies related to SARS and droplet exhalation; they use those papers and several wind tunnel studies to validate computational models of droplet evaporation and runner aerodynamics; and then they use those inputs to simulate how a cloud of exhaled droplets from one runner affects someone running alongside, behind, or in a staggered position relative to the first runner.

    In other words, their work includes all the components one would expect of a scientific study, and it makes scientifically justifiable assumptions with regard to its methods. (That’s not, mind you, to say that no one can disagree with some of those choices, but that’s true of plenty of peer-reviewed work as well.) All in all, yes, this is a scientifically valid study, even if it has not yet undergone formal peer-review*.

    Can simulations actually tell us anything about virus transmission?

    One complaint I’ve seen from both biologists and engineers is that simulations like these don’t actually capture the full physics and biology involved in virus transmission. While I agree with that general sentiment, I would point out two important facts:

    1) Blocken et al. acknowledge that this is not a virology study and confine their scientific results to looking at what happens physically to droplets when two people are moving relative to one another. Whether those droplets can transmit disease or not is a question left to biological researchers.

    2) Most medical and biological research also does not account for the physics of droplet transmission and transport. For the past century, this research has focused almost exclusively on droplet sizes, with the assumption that large droplets fall quickly and small droplets persist a little longer. To my knowledge, some of the only work done on the actual physics of the turbulent cloud produced by coughing or sneezing comes from Lydia Bourouiba’s lab at MIT. And, to me, one of the fundamental conclusions from her work is that droplets (especially small ones) can persist a lot longer and farther than previously assumed. Can those droplets facilitate transmission of COVID-19? The general consensus I’ve seen expressed by medical experts is no, but, to my knowledge, that is based on opinion and assumption, not on an actual scientific study.

    The bottom line

    In my opinion, there’s a big disconnect right now between the medical/biological community and the engineering community. To truly capture the physics and biology of COVID-19 transmission requires the expertise and cooperation of both. Right now both sides are making potentially dangerous assertions.

    Honestly, based on what I know about aerodynamics, I am personally skeptical as to whether 6 ft of physical separation is truly enough; whether it is or not seems to depend on how transmissible the novel coronavirus is through small droplets, which, again, to my knowledge, is unestablished.

    Should we leave more distance than 6ft between us when exercising outdoors? Absolutely. Aerodynamically, it makes perfect sense that following in someone’s slipstream would put you inside their droplet cloud, which needs time and space to disperse. Personally, I’ve sidestepped the question entirely by doing all my cycling indoors while quarantined.

    tl;dr: There are a lot of open questions right now about COVID-19 transmission and what qualifies as safe distancing, but it’s smarter to err on the side of more distancing. Don’t hang close to others when running or cycling outdoors.

    (Image and research credit: B. Blocken et al.; submitted by Corky W. and Wendy H.)

    *I will add that, with my training, I have and do occasionally peer-review studies such as this one, and I read the full paper with the same sort of critical eye I would turn to a paper I was asked to review.

  • Replacing Injections With Pills

    Replacing Injections With Pills

    In medicine, many medications contain molecules too large to be easily absorbed through the intestinal wall, so these so-called biologics — like the insulin administered to diabetics — are injected into the body. Researchers are studying ways that such injections could eventually be replaced with pills, but there are plenty of challenges involved.

    Some substances, known as transient permeability enhancers, allow the intestines to absorb larger molecules, but they work for only tens of minutes, which means researchers must understand how and when to administer them relative to the medication they help patients absorb. To do so, researchers are building computational fluid dynamics models of the human digestive system so that they can better understand how and when different kinds of pills break down in the body. (Image credit: Macro Room, source; via CU Engineering; submitted by Jenny B.)

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    “It’s All About Flow”

    Fluid dynamicists, like other scientists, have lives and interests well beyond our research. Ivo Nedyalkov, for example, is a professional rapper in addition to a PhD-level fluid dynamicist. In “It’s All About Flow,” Dr. Ivo brings those areas of expertise together with a rap all about fluid dynamics. The version embedded here is a bit shorter than the full version, which digs not only into experimental fluid dynamics but into computational work as well.

    Check it out, and if you’d like to see the full lyrics and explanation behind them, he’s posted those as well. You can also ping me here or on Twitter if you’d like to know more about the phenomena he discusses. (Video and image credit: I. Nedyalkov/ASME; full video here; lyrics and explanation)

  • Inferring Flows with Neural Networks

    Inferring Flows with Neural Networks

    Fluid dynamicists have long used flow visualization methods to get a qualitative sense for flows, but it’s rare to derive much quantitative data from this imagery. But that may soon change thanks to a new computational technique, called Hidden Fluid Mechanics, that uses data from flow visualizations combined with physics-informed neural networks to derive the underlying velocities and pressures in a flow.

    The technique relies on two important ideas. One is that the dye, smoke, or other method of visualizing the flow does not alter the underlying flow; it’s just something carried along by the fluid. In other words, the flow behaves exactly the same whether or not you inserted dye or smoke.

    The second key idea is that the Navier-Stokes equations — which are derived from conservation of mass, momentum, and energy — accurately describe the physics of a flow. That assumption is critical to the technique since it uses those equations to constrain the flow fields the algorithm reconstructs.

    So here, roughly speaking, is what the algorithm actually does: researchers feed it concentration data from a flow visualization — essentially how much smoke or dye is present at every point in space and time — and the neural network reconstructs, based on the Navier-Stokes equations, what velocity and pressure field would produce that concentration data.

    The researchers demonstrate the capabilities of their algorithm by comparing its results to flows where all the information is known. The first image in the gallery above shows concentration data for the flow in an aneurysm. The full flow field is known already from a numerical simulation, but the researchers gave their new algorithm only the concentration data. From that, it reconstructed the streamlines for the aneurysm’s flow, shown in the second image as “Learned”. The “Exact” streamlines on the left are taken from the original numerical simulation data. As you can see, the results are remarkably similar. (Image credit: drawings – L. da Vinci, others – M. Raissi et al.; research credit: M. Raissi et al.; submitted by Stuart H.)

  • Adapting to the Flow

    Adapting to the Flow

    Simulating fluid dynamics computationally is no simple task. One of the major challenges is that flows typically consist of many different lengthscales, from the very large to the extremely tiny. In theory, correctly capturing the physics of the flow requires computing all of those scales, and that means having a very close, dense grid of points at which the physics must be calculated during every time step of a simulation. Even for a relatively simple flow, this quickly balloons into a prohibitively expensive problem. It simply takes a computer far too long to calculate solutions for so many points.

    One technique that’s been developed to save time is Adaptive Mesh Refinement. You can see an example of it above. The background is a grid of points that are far from one another in places where the flow isn’t changing and are tightly spaced in areas where the rising flames are most changeable. Adaptive Mesh Refinement algorithms automatically change these grid points on the fly, adding more where they’re needed and subtracting them where they aren’t. The end result is a much faster computational result that doesn’t sacrifice accuracy. Check out the videos below for some examples of this technique in action. (Video and image credit: N. Wimer et al.)

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    Creating Biofuel

    One production technique for biofuel converts agricultural waste through pyrolysis. These systems heat biomass particles in a mixture of sand and nitrogen gas until the biomass particles release tar and syngas, a key ingredient of biofuel. All this heating and mixing takes place in a fluidized bed, where the injected nitrogen gas helps the particle mixture move like a fluid.

    Building prototypes of these systems can be costly, so industry has largely relied on computational studies to predict performance. But capturing the complicated physics behind turbulent gas and particle interactions is tough, and some models discard key information in favor of faster and cheaper simulations. In this study, the authors found that clustering between particles has a major effect on syngas production, something that industrial studies must account for. 

    This is one of the challenges of computational fluid dynamics; although the codes have become more and more accessible over time, getting reliable results still requires a solid understanding of the strengths and limitations of each model used. (Image, video, and research credit: S. Beetham and J. Capecelatro, source; submitted by Jesse C.)

  • Bay of Fundy Tides

    Bay of Fundy Tides

    Canada’s Bay of Fundy has some of the wildest tidal flows in the world. Every six hours, the flow direction through the strait shifts and tidal currents rise to several meters per second. This creates distinct jets a couple kilometers long that pour from one side of the strait to the other. 

    What you see here is a numerical simulation of the flow using a technique called Large Eddy Simulation (or LES, for short). It’s one method used by fluid dynamicists to model turbulent flows without taking on the complexity of the full Navier-Stokes equations. At large lengthscales, like those of the jets and eddies we see above, LES uses the exact physics. But when it comes to the smaller scales – like the flow nearest the shores or the bottom of the strait – the simulation will approximate the physics in order to make calculations quicker and easier. Models like these make large-scale problems – including modeling our daily weather patterns – possible. (Image credit: A. Creech, source)

  • Prehistoric Filter Feeders

    Prehistoric Filter Feeders

    Earth’s earlier ages are filled with enduring mysteries about the plants and creatures that lived and died long before humanity. Many of these organisms, like the aquatic Ernietta shown above, are known only from scattered fossil remains. Yet fluid dynamics is helping us understand how Ernietta lived and fed some 545 million years ago.

    Ernietta were sack-like organisms consisting of stitched-together tubular elements. They had no way to move around and no obvious method for transporting nutrients into their bodies. Scientists hypothesized that they likely used one of two feeding methods: either Ernietta relied on its surface area to extract nutrients directly from the water or its shape enabled it to trap larger particles to feed on from the flow. To decide between these modes, scientists turned to computational fluid dynamics.

    By modelling both single Ernietta and small groups, they found that the shape of the organism generates a rotating current inside the bag that pulls flow down along one side and back up the other. Moreover, being near one another enhanced this effect, helping downstream Ernietta catch more particles than they otherwise would. All in all, the results suggest not only Ernietta’s likely feeding method but also that they lived in colonies and practiced one of the earliest known examples of communal feeding! (Image credit: D. Mazierski, source; research credit: B. Gibson et al.; via ArsTechnica; submitted by Kam-Yung Soh)