FYFD

Category: Research

  • Cloudy Mornings and Clear Evenings

    Cloudy Mornings and Clear Evenings

    In the past few decades, our knowledge of exoplanets has exploded, but we’re still relatively limited in what we can learn about these worlds. That’s due, in large part, to the indirect way we observe them. Most exoplanets are found when we see them transit, passing between Earth and their star. During a transit, the planet blocks a portion of the light we would otherwise detect from the star, letting us know that something’s there. We’re often able to measure the spectra of light passing through the exoplanet’s atmosphere, giving us a glimpse of chemical signatures.

    Today’s study looks at exoplanet WASP-94A b, a gas giant tidally-locked so that only one side ever faces its star. In its transit, researchers could clearly measure different spectra from the morning and evening sides of the planet. The asymmetry seems to indicate that the exoplanet develops thick clouds on the nightside, which then dissipate during the daytime. (Image credit: H. Robbins/JHU; research credit: S. Mukherjee et al.; via Nature)

    Artist's conception of an exoplanet with clouds forming on the nightside and dissipating on the dayside.
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  • Buckling in Rings

    Buckling in Rings

    From oil drums to–yes–soda cans, liquid-filled cylindrical shells are everywhere. And, it turns out, these structures fail differently than empty shells or ones filled with a solid. Liquid-filled cylinders buckle in sequential rings, as seen in the video below. Researchers found that the buckling resulted from the shell softening and re-stiffening under the compressive load–repeating that process over and over for each ring. Their findings could help us detect containers that are in danger of failing. (Video, image, and research credit: S. Jain et al.; via Ars Technica)

    Animation of a liquid-filled cylindrical shell buckling sequentially under compression.
    Animation of a liquid-filled cylindrical shell buckling sequentially under compression.
  • Regelation Lets Glaciers Flow

    Regelation Lets Glaciers Flow

    Under the cold temperatures and immense pressures of a glacier, ice does not always behave in ways we’d expect. For example, cutting through ice using the pressure of a weighted wire does not break an ice block in two; as the wire passes through the ice, the melted water refreezes in its wake, leaving an intact block. Known as regelation, this process is one way that glaciers flow past obstacles in their path.

    Although many experiments demonstrate regelation for ice with temperatures near freezing, the process occurs in colder ice, too. A new study combines data across a wide range of temperatures with a new physical model of regelation to show how the process changes with temperature. It seems that relatively small temperature changes drastically affect how much meltwater forms around the wire and how slowly the ice refreezes. (Image credit: S. Ferrara; video credit: SciTube; research credit: C. Meyer et al.)

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  • On Dolphin Turbulence

    On Dolphin Turbulence

    Dolphins are such fast and agile swimmers that, naturally, scientists have long wanted to understand how they swim so well. A recent study draws on numerical simulation to analyze the flow a dolphin creates when flapping its tail.

    The resulting flow is highly turbulent–researchers were only able to simulate up to a fraction of a dolphin’s actual Reynolds number–with both large-scale vortices and a cascade of smaller ones. The largest vortices, shown here in white, form on the upper and lower surface of the dolphin’s tail, then slide off the tail in a vortex ring. It’s these vortex rings, the researchers found, that provide the bulk of a dolphin’s thrust.

    The smaller-scale vortices, in contrast, get formed by the large vortices, and they make little to no contribution to the dolphin’s propulsion. Interestingly, these results suggest that we might be able to describe the propulsion of dolphins and other highly turbulent swimmers by focusing only on the largest scales in the flow. (Video, image, and research credit: Y. Motoori et al.; via Ars Technica)

    Animation of the simulated flow from a swimming dolphin.
    Animation of the simulated flow from a swimming dolphin.
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  • AI-Based Weather Forecasting Has Blind Spots

    AI-Based Weather Forecasting Has Blind Spots

    Traditional weather forecasting models are physics-based and rely on supercomputers. Practically speaking, this means that they start from the basic governing equations (like the Navier-Stokes equations) and use approximations to model aspects of the problem in order to make the physics solvable, given constraints on time, computational power, spatial resolution, and so on.

    So-called AI models approach the problem differently, training a model on past weather conditions in order to predict future weather. In some respects, this approach is very successful; AI-based models require less computational infrastructure to run and, in recent years, have greatly improved their predictions of everyday weather.

    However, these AI models do poorly when predicting extreme weather events, because their training data contain relatively few examples of these events. They show limited ability to extrapolate their predictions to more extreme events. But these events–like the unprecedented 2021 heatwave in the Pacific Northwest or many of the Category 5 hurricanes we’ve seen in the last decade–are happening increasingly often due to climate change. Those events will keep happening, more frequently, as warming continues. Physics-based models can predict and forecast these events in ways that AI-based models fail to because they are limited by their trained experiences.

    Researchers are working to find ways to better equip AI-based models with more physical sense, but, as these models proliferate, it’s important for their users (and those of us using their forecasts) to know what their current weaknesses are. (Image credit: B. McGowan; research credit: Y. Sun et al.; see also S. Nath and T. Palmer; via Gizmodo)

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  • Predicting Volcanic Eruptions

    Predicting Volcanic Eruptions

    People have long hoped to reliably predict volcanic eruptions. An automated system at Piton de la Fournaise in France has been doing so since 2014 with an impressive 92% accuracy. The tool, called Jerk, makes its predictions based on real-time measurements of subtle ground movements associated with magma fracturing rock on its way to the surface. Its predictions have ranged from minutes to hours before the start of an eruption.

    So far, the team has only tested the system at one volcano, but they are working to install a second version at Mount Etna, where they’ll see whether other volcanoes produce a similar signal ahead of eruption. If so, Jerk could provide valuable warnings in populated areas and give geologists an automated alternative for monitoring remote volcanoes.

    To learn more, check out the team’s open access paper and this interview with the team leaders over at Gizmodo. (Image credit: F. Beauducel; research credit: F. Beauducel et al.; via Gizmodo)

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  • Understanding Pollen Dispersal

    Understanding Pollen Dispersal

    When the wind blows, trees shift and sway, reconfiguring their shape and their leaves in response. For parts of the year, that flow can also pluck pollen grains off the tree, carrying them on the winds. A new computational simulation models this pollen dispersal from a tree, with the aim of eventually integrating into a tool for urban planners.

    Trees are an important component to fighting climate change, especially in cities, because they cool their surroundings in addition to providing fresh oxygen. But urban planners recognize the downsides to trees, too–allergies, anyone?–and, with the right tools, they could maximize the trees’ advantages while minimizing pollen spread for allergy-sufferers. (Image credit: M. Köles; research credit: T. Dbouk et al.; via Physics World)

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  • Waves on Other Planets

    Waves on Other Planets

    On Earth, most waves form when wind blows across the water. The shear and added energy from the wind ripples the surface, eventually building up waves (through the Kelvin-Helmholtz instability). The same process should happen anywhere else where wind and open liquid surfaces meet–even on other planets. To explore this, researchers built a new model, PlanetWaves, that predicts the waves based on a planet’s gravity, atmospheric conditions, and the density, viscosity, and surface tension of its surface liquid.

    After validating the model with conditions on Earth, the team explored wave conditions for Titan, ancient Mars, and several exoplanets. They found that Titan’s lighter gravity and liquid ethane (which is less dense than water) combined to make waves on Titan much taller than those generated at the same wind speed on Earth (top image). You can watch them in action in the video below. Standing in a light breeze on Titan, you’d watch giant 3-meter waves rolling in.

    The team also found that waves on Mars would have gotten shorter as Mars lost its atmosphere and the air pressure dropped. Over time, the same wind speed would have elicited smaller and smaller waves. Wave action has a big effect on a landscape’s erosion, so understanding how waves look on other planets will help us parse their geography. (Video, image, and research credit: U. Schneck et al.; via MIT News; submitted by Joseph S.)

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  • Featured Video Play Icon

    Jets From Impact

    When a test tube of liquid hits a surface, the curvature of the meniscus focuses the rebounding fluid into a jet. In this video, researchers show some of the many variations they’ve explored on these experiments–from changing the depth of the fluid and the shape of the container, to changing the working fluid to honey or to dry grains. It’s a nice introduction to a fascinating phenomenon! (Video and image credit: H. Watanabe et al.; research credit: H. Watanabe et al. and K. Kobayashi et al.)

    Animation showing how granular jets form in a test tube impact.
    Animation showing how granular jets form in a test tube impact.
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  • Inside an Ear

    Inside an Ear

    Our ears, like those of many other animals, convert mechanical signals to electrical ones, through a Rube-Goldberg-esque series of transformations. External sound waves make their way down the soft tube of the ear canal, which funnels them to a thin-walled cone, the eardrum, that’s about half as large as a dime. Here, the vibrating air pushes against the cone’s membrane, and those vibrations travel onward through a linked trio of small bones that amplify the vibration’s amplitude.

    The last of these bones presses against an even smaller, oval-shaped membrane. As the bone moves, it shakes the membrane, sending waves through the liquid on its other side. Those waves travel down the spirals of the tiny, pea-sized cochlea, named for a snail shell’s shape. As the waves move through the liquid, they bend bundles of hair-like strands back and forth, like tall grass waving in a breeze. The bending triggers a chemical that binds to nerves at the base of the bundles, sending an electrical signal through the nerve and into the brain.

    But the hair-like bundles, known as stereocilia, are also able to amplify incoming vibrations. In this case, the bundles in the outer portion of the cochlea expend energy to bend more than the incoming vibrations naturally make them move. This bending amplifies the fluid motion that gets transmitted to stereocilia further down the line; it’s those bundles that will make the final conversion to an electrical signal the brain receives. (Image credit: B. Kachar; research credit: Y. Thipmaungprom et al.; via APS)

    Scanning electron microscope view of the stereocilia "hair bundles" inside a frog's inner ear.
    Scanning electron microscope view of the stereocilia “hair bundles” inside a frog’s inner ear.