FYFD

Tag: direct numerical simulation

  • Beneath the Surface

    Beneath the Surface

    Signs of a ship’s passage can persist long after it’s gone. The churn of its propellers and the oil leaked from its engines leave a mark on the water’s surface that, in some cases, is visible even from orbit. But the frothy wake of a ship is no easy place to measure; there are simply too many bubbles. To reveal the physics behind that froth, these researchers turned to direct numerical simulation, a type of computational fluid dynamics that calculates the full details of a flow, typically using a supercomputer to do so.

    In their poster, the blue field of wavy lines shows turbulence under the water’s surface. For (relative) simplicity, the turbulence is statistically uniform — as opposed to matching a particular ship’s wake. The interface between air and water is shown in red. The water surface is complex and undulating, spotted with bubbles trapped below the water and droplets flying through the air. Simulations like these help scientists focus on the detailed mechanisms that connect the turbulent water to the complex air-water surface. Once those are understood, researchers can develop models that approximate the physics for more specific situations, like the passage of a cargo ship. (Image credit: A. Calado and E. Balaras)

  • Droplet Medusa

    Droplet Medusa

    Vibration is one method for breaking a drop into smaller droplets, a process known as atomization. Here, researchers simulate this break-up process for a drop in microgravity. Waves crisscrossing the surface create localized craters and jets, making the drop resemble the Greek mythological figure of Medusa. With enough vibrational amplitude, the jets stretch to point of breaking, releasing daughter droplets. (Image and research credit: D. Panda et al.)

  • Bubbles in Turbulence

    Bubbles in Turbulence

    In nature and industry, swarms of bubbles* often encounter turbulence in their surrounding fluid. To study this situation, researchers used numerical simulation to observe bubbles across a range of density, viscosity, and surface tension values relative to their surroundings. They found that density differences between the two fluids made negligible changes to the way bubbles broke or coalesced.

    In contrast, viscosity played a much larger role. More viscous bubbles were less likely to deform and break, thanks to their increased rigidity. When looking at small deformations along the bubble interface, both density and viscosity had noticeable effects. With increasing bubble density, they observed more dimples on the interface; increasing the viscosity had the opposite effect, making the bubbles smoother. (Image credit: Z. Borojevic; research credit: F. Mangani et al.)

    *We usually think of bubbles as air or another gas contained within a liquid. But this study’s authors use the term “bubble” more broadly to mean any coherent bits of fluid in a different surrounding fluid. Colloquially, this means their results apply to both bubbles and drops.

  • Asperitas Formation

    Asperitas Formation

    In 2017, the World Meteorological Organization named a new cloud type: the wave-like asperitas cloud. How these rare and distinctive clouds form is still a matter of debate, but this new study suggests that they need conditions similar to those that produce mammatus clouds, plus some added shear.

    Using direct numerical simulations, the authors studied a moisture-filled cloud layer sitting above drier ambient air. Without shear, large droplets in this cloud layer slowly settle downward. As the droplets evaporate, they cool the area just below the cloud, changing the density and creating a Rayleigh-Taylor-like instability. This is one proposed mechanism for mammatus clouds, which have bulbous shapes that sink down from the cloud.

    When they added shear to the simulation, the authors found that instead of mammatus clouds, they observed asperitas ones. But the amount of shear had to be just right. Too little shear produced mammatus clouds; too much and the shear smeared out the sinking lobes before they could form asperitas waves. (Image credit: A. Beatson; research credit: S. Ravichandran and R. Govindarajan)

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    Shear and Convection in Turbulence

    In nature, we often find turbulence mixed with convection, meaning that part of the flow is driven by temperature variation. Think thunderstorms, wildfires, or even the hot, desiccating winds of a desert. To better understand the physics of these phenomena, researchers simulated turbulence between two moving boundaries: one hot and one cold. This provides a combination of shear (from the opposing motion of the two boundaries) and convection (from the temperature-driven density differences).

    Please note that, despite the visual similarity, these simulations are not showing fire. There’s no actual combustion or chemistry here. Instead, the meandering orange streaks you see are simply warmer areas of turbulent flow, just as the blue ones are cooler areas. The shape and number of streaks are important, though, because they help researchers understand similar structures that occur in our planet’s atmosphere — and which might, under the wrong circumstances, help drive wildfires and other convective flows. (Image, research, and video credit: A. Blass et al.)

  • 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.)

  • Growing Droplets

    Growing Droplets

    The moisture in clouds eventually condenses into droplets that grow into raindrops and fall. Some steps in this process are well understood, but others are not. In particular, scientists have struggled with the problem of how droplets grow from about 30 microns to 80 microns, where they’re big enough to start falling and merging.

    Laboratory experiments and numerical simulations (below) have shown that turbulence can help drive small water drops together. When droplets are tiny and light, they simply follow the air flow. But when they’re a little heavier, turbulent eddies (seen in orange below) act like miniature centrifuges, flinging larger water droplets (shown in cyan below) out into clusters, where they’re more likely to collide with one another.

    Although this effect has been seen in experiments and simulation, it’s been difficult to capture in clouds themselves. But a new set of test flights (above) confirms that this mechanism is present in the wild as well! (Image credit: UCAR/NCAR Earth Observing Laboratory, P. Ireland et al., source; research credits: M. Larsen et al., P. Ireland et al.; via APS Physics; submitted by Kam-Yung Soh)

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  • Building Smart Swimmers

    Building Smart Swimmers

    Scientists have long wondered whether the schooling of fish is driven by hydrodynamic benefits, but the complexity of their environment makes unraveling this complex motion difficult. A recent study uses a different tactic, combining direct numerical simulation of the fluid dynamics with techniques from artificial intelligence and machine learning to build and train autonomous, smart swimmers.

    The authors use a technique called deep reinforcement learning to train the swimmers. Essentially, the swimmer being trained is able to observe a few variables, like its relative position to the lead swimmer and what its own last several actions have been – similar to the observations a real fish could make. During training, the lead swimmer keeps a steady pace and position, and the follower, through trial and error, learns how to follow the leader in such a way that it maximizes its reward. That reward is set by the researchers; in this case, one set of fish was rewarded for keeping a set distance from their leader, one intended to keep them in a position that was usually beneficial hydrodynamically. Another set of fish was rewarded for finding the most energy-efficient method for following.

    Once trained, the smart swimmers were set loose behind a leader able to make random decisions. Above you can see the efficiency-seeker chasing this leader. Impressively, even though this smart swimmer had the option to go it alone (and had never followed such a dynamic leader), it does an excellent job of keeping to the leader’s wake. Compare it with real swimmers and there’s a definite similarity in their behavior, which suggests the technique may be capturing some of an actual fish’s intuition. (Image and research credit: S. Verma et al., source; thanks to Mark W. for assistance)

  • Creating Moana’s Ocean

    Creating Moana’s Ocean

    Hopefully by now you’ve had an opportunity to see Disney’s film Moana. Fluid dynamics play a central role in the movie, and Disney’s animators faced the challenge of hundreds of shots requiring special effects to animate water, lava, waves, and wind. Science Friday has a great segment interviewing a couple of Moana’s animators, in which they discuss the process of turning the ocean itself into a character. 

    Because the physics of fluids is so complex, scientists and animators differ in the way they approach simulations. Scientists usually try to capture a full physical representation of a flow, simulating every detail to the smallest scale and time step. Animators, on the other hand, are interested in capturing a realistic feel for a flow. For an animator, the simulation should be exactly as complex as necessary to make the water move in a way a person believes it should. With Moana, animators had the extra challenge of melding the ocean character’s actions with appropriate water physics–think bubbles, drops, and splashes. The results are impressive and exceptionally fun. (Image credits: Disney/Science Friday; via Jesse C.)

  • Bumblebees in Turbulence

    Bumblebees in Turbulence

    Bumblebees are small all-weather foragers, capable of flying despite tough conditions. Given the trouble that micro air vehicles have when flying in gusty winds, bumblebees can help engineers to understand how nature successfully deals with turbulence. Under smooth laminar conditions like those shown in the animation above, bumblebees stay aloft by beating their wings forward and backward in a figure-8-like motion. On both the forward downstroke and the backward upstroke, you’ll notice a blue bulge near the front of the bee’s wing. This is a leading-edge vortex, which provides much of the bee’s lift.

    Researchers were curious how adding turbulence would affect their virtual bee’s flight. The still image above shows the bee in moderate freestream turbulence (shown in cyan). Surprisingly, this outside turbulence has very little effect on the flow generated by the bee, shown in pink. In fact, the researchers found that the bees could fly through turbulence without a significant increase in power. Too much turbulence does make it hard for the bee to control its flight, though. The bee’s shape makes it prone to rolling, and the researchers estimated, based on a bee’s 20 ms reaction time, that bumblebees can probably only correct that roll and maintain controlled flight at turbulence intensities less than 63% of the mean wind speed. (Image credits: T. Engels et al., source; via Physics Focus)