What do you get when you spin a splash? I expect the result is a lot like these whirling fluid structures captured by photographer Hélène Caillaud. I love the fantastical shapes she creates as sheets and filaments are flung outward. These liquid sculptures look like everything from the perfect martini glass to the skirts of a flamenco dancer. Check out the full gallery of images, and be sure to look around at Caillaud’s other stunning liquid art while you’re there. (Images credit: H. Caillaud)
Fire plays an important role in nature, one with which humanity must live without controlling fully. After several disastrous historic wildfires in the American West, the U.S. Forest Service established its own fire lab, where research foresters can study flames firsthand. This video takes us inside the Fire Lab for a look at the facilities and people responsible for helping us better understand this fundamental force of nature. (Video and image credit: Gizmodo + Atlas Obscura)
Kneading bread dough is something of an art. The process binds flour, water, salt, and yeast into a network that is both elastic and viscous. It also traps pockets of air that will determine the texture of the final loaf. Underknead and the bubbles won’t form; overknead and the result will be a dense loaf that doesn’t rise in the oven.
Capturing all of that physics in a realistic model is tough, but researchers have done so and validated their digital dough against experiments. The group focused on simulating industrial mixers, which knead dough with a moving, spiral-shaped rod rotating around a stationary vertical one. They found the industrial set-up did not mix as well as kneading by hand, but that could be improved by swapping the stationary rod for a second spiral one. (Image credit: G. Perricone; research credit: L. Abu-Farah et al.; via Physics World; submitted by Kam-Yung Soh)
Evaporate a droplet full of silica nanoparticles, and you’ll get beautiful, flower-like films. As the water evaporates, dry nanoparticles build up in a solid deposit. The evaporation creates a pressure gradient that pulls toward the center of the drop, forcing the deposit to bend. As stress builds in the deposit, cracks form petal-like segments. The number of cracks is indicative of how much of the drop was solid material; the higher the volume fraction of particles is, the fewer cracks form and the less the deposit bends. (Image, video, and research credit: P. Lilin et al.)
Despite their ubiquity in science fiction, volumetric displays — three-dimensional displays visible from any angle — have been tough to create in real life. But a team from the University of Sussex has made impressive strides using a system based on acoustic levitation.
Here’s how it works: an array of ultrasonic speakers levitates and moves small plastic beads at up to 9 m/s. Simultaneously, LED lights project colors onto the sphere. Thanks to the human brain’s ability to create persistent images from the motion, we’re able to see simple displays like the figure-8 and smiley face above with the naked eye. To form something more complicated, like the spinning globe seen in the final image, the bead must be filmed using a camera with a slow shutter speed. But with that, the display looks incredible.
There’s obviously a ways to go before your R2 unit can project holographic messages for you, but all the basic ingredients for that technology are here. Check out the coverage on Scientific American and the original research paper for more. (Image credit: Star Wars – Lucasfilm; others – E. Jankauskis; research credit: R. Hirayama et al.; via SciAm)
One of the major challenges in fluid dynamics is the size of the parameter spaces we have to explore. Because many problems in fluid dynamics are non-linear, making small changes in the initial set-up can result in large differences in the results. Consider, for example, a simple cylinder towed through a water tank. As the cylinder moves, vortices will form around it and shed off the back, causing the cylinder to vibrate. The details of what will happen will depend on variables like the cylinder’s size and flexibility, the speed it’s being towed at, and which directions it’s allowed to vibrate in. Mapping out the parameter space, even sparsely, could take a graduate student hundreds of experiments.
To speed up this process, engineers are now building robotic facilities like the Intelligent Towing Tank (ITT) shown above. Like graduate students, the ITT can work into the wee hours of the night, but, unlike graduate students, it never needs to eat, sleep, or stop experimenting. Now, one could use a facility like this to brute-force the answers by testing every possible combination of parameters, but even working 24 hours a day, that would take a long time. Instead, researchers use machine learning to guide the robotic facility into choosing test parameters in a way that optimizes the factors the researchers define as important.
Essentially, the system starts with experiments chosen at random within the parameter space, and then uses those results to select areas of interest until it’s gathered enough data to satisfy the limits specified by human researchers. In theory, a well-designed algorithm can dramatically reduce the number of experiments needed to explore a parameter space. (Image and research credit: D. Fan et al.; submitted by Kam-Yung Soh)
I love when science and art come together, which is why I’ve long been a fan of the Flow Vis course at CU Boulder. Some of my earliest posts on FYFD date from previous editions of the course. Here are a few of my favorite images from the Fall 2019 class, from the top:
For those in the Front Range area, the Flow Vis class will be showcasing their work on Saturday, December 14th at the Fiske Planetarium. Snacks are at 4:30 pm and the show starts at 5 pm. For those not nearby, you can peruse the art from this semester and previous ones at your leisure online. (Image credits: colorful ferrofluid – R. Drevno; falling oobleck – A. Kumar; droplets – A. Barron; macro ferrofluid – A. Zetley)
In his short film, “Magic Fluids,” Roman De Giuli uses cyan, magenta, and yellow paints to generate a rainbow of macro colors. All the fluid motion you see is a practical effect, painstakingly created by layering paints and flow mediums of different densities. Like in Siqueiros’ “accidental painting” technique, the less dense paints will eventually rise through the upper layers and spread. De Giuli uses the effect for its motion, but the same physics is key for many artists who use acrylic pouring to paint. (Video and image credit: R. De Giuli)
Frightening as they can be in the moment, storms have a power and majesty all their own. I’ve never seen a better way to capture that than through timelapse, and photographer Nicolaus Wegner offers a great one in “Stormscapes 4″. I particularly like how his frame captures the motion of storms and how they shear, rotate, and billow as they evolve. With a quick glance upward, it’s easy to miss that motion, even if it is fundamental to these storms. Sit back and enjoy. (Video and image credit: N. Wegner)
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.)
