Hexbyte Glen Cove The physics of fire ant rafts could help engineers design swarming robots

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Fire ants form a protrusion from an ant raft. Credit: Vernerey Researcher Group at CU Boulder

Noah rode out his flood in an ark. Winnie-the-Pooh had an upside-down umbrella. Fire ants (Solenopsis invicta), meanwhile, form floating rafts made up of thousands or even hundreds of thousands of individual insects.

A new study by engineers at the University of Colorado Boulder lays out the simple physics-based rules that govern how these ant rafts morph over time: shrinking, expanding or growing long protrusions like an elephant’s trunk. The team’s findings could one day help researchers design robots that work together in swarms or next-generation materials in which molecules migrate to fix damaged spots.

The results appeared recently in the journal PLOS Computational Biology.

“The origins of such behaviors lie in fairly simple rules,” said Franck Vernerey, primary investigator on the new study and professor in the Paul M. Rady Department of Mechanical Engineering. “Single ants are not as smart as one may think, but, collectively, they become very intelligent and resilient communities.”

Fire ants form these giant floating blobs of wriggling insects after storms in the southeastern United States to survive raging waters.

In their latest study, Vernerey and lead author Robert Wagner drew on mathematical simulations, or models, to try to figure out the mechanics underlying these lifeboats. They discovered, for example, that the faster the ants in a raft move, the more those rafts will expand outward, often forming long protrusions.

“This behavior could, essentially, occur spontaneously,” said Wagner, a graduate student in mechanical engineering. “There doesn’t necessarily need to be any central decision-making by the ants.”

Treadmill time

Wagner and Vernerey discovered the secrets of ant rafts almost by accident.

In a separate study published in 2021, the duo dropped thousands of into a bucket of water with a plastic rod in the middle—like a lone reed in the middle of stormy waters. Then they waited.

“We left them in there for up to eight hours to observe the long-term evolution of these rafts,” Wagner said. “What we ended up seeing is that the rafts started forming these growths.”

Rather than stay the same shape over time, the structures would compress, drawing in to form dense circles of ants. At other points, the insects would fan out like pancake batter on a skillet, even building bridge-like extensions.

Fire ants form an ant raft. Credit: Vernerey Researcher Group at CU Boulder

The group reported that the ants seemed to modulate these shape changes through a process of “treadmilling.” As Wagner explained, every ant raft is made up of two layers. On the bottom, you can find “structural” ants who cling tight to each other and make up the base. Above them are a second layer of ants who walk around freely on top of their fellow colony-members.

Over a period of hours, ants from the bottom may crawl up to the top, while free-roaming ants will drop down to become part of the structural layer.

“The whole thing is like a doughnut-shaped treadmill,” Wagner said.

Bridge to safety

In the new study, he and Vernerey wanted to explore what makes that treadmill go round.

To do that, the team created a series of models that, essentially, turned an ant raft into a complicated game of checkers. The researchers programmed roughly 2,000 round particles, or “agents,” to stand in for the ants. These agents couldn’t make decisions for themselves, but they did follow a simple set of rules: The fake ants, for example, didn’t like bumping into their neighbors, and they tried to avoid falling into the water.

When they let the game play out, Wagner and Vernerey found that their simulated ant rafts behaved a lot like the real things.

In particular, the team was able to tune how active the agents in their simulations were: Were the individual ants slow and lazy, or did they walk around a lot? The more the ants walked, the more likely they were to form long extensions that stuck out from the raft—a bit like people funneling toward an exit in a crowded stadium.

“The ants at the tips of these protrusions almost get pushed off of the edge into the water, which leads to a runaway effect,” he said.

Wagner suspects that fire ants use these extensions to feel around their environments, searching for logs or other bits of dry land.

The researchers still have a lot to learn about ant rafts: What makes ants in the real world, for example, opt to switch from sedate to lazy? But, for now, Vernerey says that engineers could learn a thing or two from fire ants.

“Our work on fire ants will, hopefully, help us understand how simple rules can be programmed, such as through algorithms dictating how robots interact with others, to achieve a well-targeted and intelligent swarm response,” he said.

More information:
Robert J. Wagner et al, Computational exploration of treadmilling and protrusion growth observed in fire ant rafts, PLOS Computational Biology (2022). DOI: 10.1371/journal.pcbi.1009869

Robert J. Wagner et al, Treadmilling and dynamic protrusions in fire ant rafts, Journal of The Royal Society Interface (2021). DOI: 10.1098/rsif.2021.0213

The physics of fire ant rafts could help engineers design swarming robots (2022, March 2)
retrieved 3 March 2022
from https://phys.org/news/2022-03-physics-ant-rafts-swarming-robots.html

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Hexbyte Glen Cove Flow physics could help forecasters predict extreme events thumbnail

Hexbyte Glen Cove Flow physics could help forecasters predict extreme events

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Brian Elbing (left) holds a microphone with storm chaser Val Castor (right) in front of his storm chasing truck, in which the researchers mounted an infrasound sensor for monitoring tornadoes. Credit: Brian Elbing

About 1,000 tornadoes strike the United States each year, causing billions of dollars in damage and killing about 60 people on average. Tracking data show that they’re becoming increasingly common in the southeast, and less frequent in “Tornado Alley,” which stretches across the Great Plains. Scientists lack a clear understanding of how tornadoes form, but a more urgent challenge is to develop more accurate prediction and warning systems. It requires a fine balance: Without warnings, people can’t shelter, but if they experience too many false alarms, they’ll become inured.

One way to improve tornado prediction tools might be to listen better, according to mechanical engineer Brian Elbing at Oklahoma State University in Stillwater, in the heart of Tornado Alley. He doesn’t mean any sounds audible to human ears, though. As long ago as the 1960s, researchers reported evidence that emit signature sounds at frequencies that fall outside the range of human hearing. People can hear down to about 20 Hertz—which sounds like a low rumble—but a tornado’s song likely falls somewhere between 1 and 10 Hertz.

Brandon White, a graduate student in Elbing’s lab, discussed their recent analyses of the infrasound signature of tornadoes at the 73rd Annual Meeting of the American Physical Society’s Division of Fluid Dynamics.

Elbing said these infrasound signatures had seemed like a promising avenue of research, at least until radar emerged as a frontrunner technology for warning systems. Acoustic-based approaches took a back seat for decades. “Now we’ve made a lot of advances with radar systems and monitoring, but there are still limitations. Radar requires line of sight measurements.” But line of sight can be tricky in hilly places like the Southeast, where the majority of tornado deaths occur.

Maybe it’s time to revisit those acoustic approaches, said Elbing. In 2017, his research group recorded infrasound bursts from a supercell that produced a small tornado near Perkins, Oklahoma. When they analyzed the data, they found that the vibrations began before the tornado formed.

Researchers still know little about the fluid dynamics of tornadoes. “To date there have been eight trusted measurements of pressure inside a tornado, and no classical theory predicts them,” said Elbing. He doesn’t know how the sound is produced, either, but knowing the cause isn’t required for an alarm system. The idea of an acoustics-based system is straightforward.

“If I dropped a glass behind you and it shattered, you don’t need to turn around to know what happened,” said Elbing. “That sound gives you a good sense of your immediate environment.” Infrasound vibrations can travel over long distances quickly, and through different media. “We could detect tornadoes from 100 miles away.”

Members of Elbing’s research group also described a sensor array for detecting tornadoes via acoustics and presented findings from studies on how infrasound vibrations travel through the atmosphere. The work on infrasound tornado signatures was supported by a grant from NOAA.

Other sessions during the Division of Fluid Dynamics meeting similarly addressed ways to study and predict extreme events. During a session on nonlinear dynamics, MIT engineer Qiqi Wang revisited the , a well-known phenomena in fluid dynamics that asks whether a butterfly flapping its wings in Brazil could trigger a tornado in Texas.

What’s unclear is whether the butterfly wings can lead to changes in the longtime statistics of the climate. By investigating the question computationally in small chaotic systems, he found that small perturbations can, indeed, effect long-term changes, a finding that suggests even small efforts can lead to lasting changes in the climate of a system.

During the same session, mechanical engineer Antoine Blanchard, a postdoctoral researcher at MIT, introduced a smart sampling algorithm designed to help quantify and predict extreme events—like extreme storms or cyclones, for example. Extreme events occur with low probability, he said, and therefore require large amounts of data, which can be expensive to generate, computationally or experimentally. Blanchard, whose background is in fluid dynamics, wanted to find a way to identify outliers more economically. “We’re trying to identify those dangerous states using as few simulations as possible.”

The algorithm he designed is a kind of black box: Any dynamical state can be fed as an input, and the algorithm will return a measure of the dangerousness of that state.

“We’re trying to find the doors to danger. If you open that particular door, will the system remain quiescent, or will it go crazy?” asked Blanchard. “What are the states and conditions—like weather conditions, for example—that if you were to evolve them over time could cause a cyclone or storm?”

Blanchard said he’s still refining the algorithm but hopes to start applying it to real data and large-scale experiments soon. He also said it may have implications beyond the weather, in any system that produces extreme events. “It’s a very general algorithm.”