Showing posts with label fluid mechanics. Show all posts
Showing posts with label fluid mechanics. Show all posts

Sea Monkeys Show Scientists How To Rewrite a Rule of Turbulence

Mike's Notes

Fascinating. 😎

What a cool experiment to do. From Quanta Magazine.

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References

  • Biologically generated turbulent energy flux in shear flow depends on tensor geometry by Xinyu Si, Lei Fang. PNAS Nexus, Volume 3, Issue 2, February 2024.

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Last Updated

30/10/2026

Sea Monkeys Show Scientists How To Rewrite a Rule of Turbulence

By: Stephen Ornes
Quanta Magazine: 02/10/2026

Stephen Ornes: Contributing writer

...

Scientists assumed that energy flows in only one direction in a turbulent system. What they didn’t know, until they looked closely at brine shrimp, was that a simple factor can reverse the flow.

...

In Lei Fang’s lab at the University of Pittsburgh, a brine shrimp swims through a two-dimensional turbulent system.

Xinyu Si/University of Pittsburgh

Introduction

In the 1960s and ’70s, colorful comic book advertisements from the Transcience Corporation in New York City promised to mail anyone who sent cash, check, or money order a “bowlfull of happiness” — in reality, a small paper envelope of freeze-dried eggs. Dropped into salt water, the eggs would produce brine shrimp, also known as sea monkeys.

Brine shrimp are about a centimeter long and swim upside down, beating their legs madly and trailing their elongated abdomens behind them like tails. As scientists recently learned, these tiny swimmers can do more than just move themselves.

Researchers have long thought that in a turbulent system like a current of water, energy flows in only one direction, from larger scales to smaller ones, or vice versa, depending on the system’s dimensions. But in observing the humble brine shrimp, scientists at the University of Pittsburgh realized that — with just a tiny adjustment — the flow of energy could be reversed.

The scientists discovered that they could direct the cascade of energy in a two-dimensional system by disrupting the system’s flow with a small obstacle, as long as it was angled just right. “The geometry matters,” said Lei Fang, the Pittsburgh engineer who led the new study.

The discovery capitalized on a fundamental, if often unrecognized, mathematical description of how forces interact to push energy through a system, said Gregory Falkovich, a physicist at the Weizmann Institute of Science and a pioneer in the study of turbulent two-dimensional systems. “This is a beautiful and skillful experimental work.”

These findings could extend to larger, more chaotic systems, including those in three dimensions. The work has implications for our understanding of fluid dynamics and may have applications in areas such as pollution control and drug design.

A translucent white invertebrate with red eyes and a long tail.

Brine shrimp are tiny invertebrates that inhabit waters with high salt concentration. They grow to about 10 millimeters long, the width of a pencil eraser.

Nature Picture Library/Alamy

A Cascade of Chaos

Turbulence is hard to miss. It stirs the raucous foam at the base of a waterfall and shapes the crest of a breaking wave. It’s behind the bumpiness of a flight, the swirls of milk in a cup of coffee, and the roil of plasma on the surface of the sun. “We observe turbulence generally everywhere in our lives, when we go to the beach or wash our hands,” said Francesca De Serio, a civil engineer and expert on hydrodynamics at the Polytechnic University of Bari in Italy.

"We observe turbulence generally everywhere in our lives, when we go to the beach or wash our hands." - Francesca De Serio, Polytechnic University of Bari

A turbulent system is complicated, characterized by complex forces and inner turmoil. But it can start simply enough. For example, turbulence arises when a flowing fluid encounters an obstacle that changes the velocity of part of the flow. In a river, water slows down by the bank due to friction, and the velocity mismatches can produce vortices or eddies. When a river divides to move around a rock, speeds change and voids form; water falls over itself and may start to rotate. Air, too, is a fluid, its turbulence produced by competing currents and changing temperatures.

In such a system, energy moves between size scales. English mathematician and physicist Lewis Fry Richardson, the founder of modern weather forecasting, discovered this when he studied the fluid systems formed by gases in the atmosphere. He found that the kinetic energy of a large eddy feeds into smaller and smaller eddies, until it reaches the scale where viscosity, which resists motion, takes over. The energy is ultimately dissipated as heat. In 1922, Richardson captured the concept in verse:

Big whorls have little whorls

Which feed on their velocity,

And little whorls have lesser whorls

And so on to viscosity.

Starting in the early 1940s, the Soviet mathematician Andrey Kolmogorov provided a clear and rigorous mathematical foundation for the study of turbulence. But his work, like Richardson’s, mainly focused on three-dimensional systems in which energy flows from large features to small ones.

In the late 1960s, the physicists Robert Kraichnan and George Batchelor extended the investigation to two-dimensional systems. Two-dimensional turbulent systems include phenomena like Jupiter’s Great Red Spot, which swirls so violently that it overwhelms any motion farther down in the atmosphere. This type of two-dimensional turbulent system transfers energy from one scale to another, like a three-dimensional one, but with a twist: The energy of a two-dimensional turbulent system cascades in the opposite direction, moving from small eddies up to large ones. In Jupiter’s Great Red Spot, smaller eddies and vortices near the perimeter feed the giant maelstrom in the center.

In the decades after those pioneering studies reported an inverse flux in two-dimensional systems, physicists did not deeply interrogate whether those flows were fixed. The underlying energy mechanisms of two-dimensional turbulent systems certainly weren’t on the mind of Xinyu Si around 2021, when he started investigating brine shrimp as a student in Fang’s lab.

Swimming in the Energy Flow

Fang’s lab focuses on “active matter,” a physics term describing things that move by themselves and inject energy into their environments. It’s a label broad enough to include networks of living things, like bacteria, and nonliving things, like tiny robots.

In this project, Si and Fang were interested in investigating how biological swimmers mix materials in fluids where turbulence shows up. The researchers had hypothesized that, in the mixing of fluids in large natural systems, turbulence generated by tiny organisms plays an underappreciated role. Billions of minuscule creatures, after all, agitate the waterways of the world.


In a laboratory lit with red light, a man holds a tube over a tank filled with cloudy liquid.

Xinyu Si, now a postdoc at the University of Rochester, prepares a research tank.

J. Adam Fenster/University of Rochester

To closely study this in a simple model, Si poured a thin film of salt water over a glass surface in a tank and positioned magnets underneath. When he applied an electric current to the conductive fluid, it interacted with the magnets and created motion in the water, which flowed in one direction along one side of a channel and in the opposite direction along the other side. This created a gradient of velocities across the center, to which Si added brine shrimp.

When Si and Fang calculated how the shrimp’s flailing bodies disrupted the moving water, they saw “something very, very strange,” Fang said: The shrimp weren’t just contributing to the chaos of the system; they also seemed to be shifting the direction in which energy flowed.

A turbulent system can be described in terms of how much it stretches and compresses the fluids within, due to the interactions of fluids flowing at different velocities. These dynamics show up, for example, when you pour confetti into a river and watch it organize into sweeping lines as it races on competing currents downstream.

"Every experiment suggested the same conclusion: The orientation of a brine shrimp’s body seemed to change the turbulent system’s energy flux."

Si and Fang ran the brine shrimp experiment repeatedly. They found that when a solo shrimp swam at an angle of less than 45 degrees to the direction of maximum stretching, the energy flowed from smaller scales to larger ones, feeding the flow itself, as they’d predicted for a 2D system. But if the shrimp swam at an angle of more than 45 degrees to the direction of maximum stretching, the energy went in reverse, pulling from the larger scale of the flow to energize smaller perturbations.

It appeared to be a clean about-face. Every experiment suggested the same conclusion: The orientation of a brine shrimp’s body seemed to change the turbulent system’s energy flux.

The scientists found a way to interpret what they were seeing through a field of mathematics called tensor geometry, which has long been a workhorse for physicists studying turbulence. A tensor is a mathematical tool that can help you measure something that changes in multiple ways at once. For example, when you press down on a sponge, its sides bulge outward. A tensor can describe how stress — the weight of your hand — affects strain — the deformation of the sponge. Tensors give fluid dynamics researchers a powerful way to model physical phenomena that can transform in terms of direction or strength.

Fang and Si found that their brine-shrimp situation made sense if they looked at it as the interaction of two tensors in two dimensions. One tensor represented how shear — the large-scale movement of the water — affected energy flux. The other represented how the energy flux was shaped by small-scale movements from the swimmers.

The orientation of the swimmer determined the alignment of the two tensors, and the alignment of the tensors determined how the energy flowed. As Fang and Si reported in 2024, the angle between the brine shrimp’s body and the forces of the fluid could alter the direction of the energy flux.

Turning the Tide

Portrait of a man in a sweater and tie.

Fang, an engineer at the University of Pittsburgh, is interested in the influence of biological swimmers in natural systems.

University of Pittsburgh

Si had been releasing brine shrimp into the tank, pointing a camera at them, and patiently waiting until one swam through the test area at just the right angle. In the next iteration of the experiment, Si and Fang replaced the brine shrimp with an array of centimeter-long rods, which proved easier to measure. They found that, by altering the orientation of the rods relative to the flow, they were able to control whether energy transferred to higher or lower scales.

The findings held even when Si and Fang invited the scrutiny of two other scientists: Filippo De Lillo and Guido Boffetta of the University of Turin in Italy, whom Fang met at a conference. In the outside experts’ numerical simulations, the same effect clearly showed up. “That made me much more comfortable,” Fang said, “because it’s a very weird thing to manipulate energy flux.”

With renewed confidence, the researchers reported their findings(opens a new tab) in 2025. “It was interesting because they were saying that with small, specific interventions in the flow we can … show a transfer of energy between scales,” De Serio said. They demonstrated that with minimal manipulation, the energy flux in a 2D system can be steered to act more like (but not exactly like) the flux in a 3D one, which suggests that physicists might be able to apply insights from one dimensional domain to the other.

There are confounding issues to overcome. In their experiments, Si and Fang used a shallow pool of fluid whose friction made turbulence hard to initiate. And there are big differences between a weakly turbulent two-dimensional system like the one in their lab and the strongly turbulent three-dimensional ones more commonly found in nature.

Mathematically, though, there’s no reason the scientists’ observations shouldn’t extend to such scenarios. Si and Fang are investigating the possibility, testing ideas on tabletop tornadoes.

They’re also exploring how to influence the “transport barrier,” the threshold between two fluids as they mix. According to Fang’s theory, disrupting this boundary — which could be useful in controlling pollution or mixing fluids for drug development — would use less than 1% of the energy required to keep the boundary intact.

Other scientists have also begun experimenting with manipulating energy flux. De Serio, at the Polytechnic University of Bari, has been firing strong jets into large tanks of turbulent, rotating fluids with high viscosity. “I was attracted by this idea that these [manipulations] could be applied in a real context,” she said. In March 2026, she reported results from experiments conducted on a large rotating vortex manipulated with small, horizontal jets firing into the middle. She observed patches of inverse energy flux that started out as intermittent bursts but grew steady with increasing rotation. Those findings, she says, may point to practical strategies for controlling energy pathways in real-world situations.

Si, now a postdoc at the University of Rochester, said that the current work introduces a new hypothesis that’s testable in any setting where turbulence plays a role, from a tiny “bowlfull of happiness” to the enormous scale of Earth’s atmosphere. Turbulent systems may be notoriously difficult to understand, but what dictates the flow of energy may be an issue of simple geometry.

These New AI Models Are Trained on Physics, Not Words, and They’re Driving Discovery

Mike's Notes

A fantastic use of AI. My instinct is to incorporate fluid-like systems into a future Pipi. That will require a real data centre.

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Last Updated

19/03/2026

These New AI Models Are Trained on Physics, Not Words, and They’re Driving Discovery

By: Elizabeth Fernandez
Fatiron Institute: 09/12/2025

Elizabeth Fernandez is a science writer specializing in science and society, science and philosophy, astronomy, physics, and geology.

While popular AI models such as ChatGPT are trained on language or photographs, new models created by researchers at the Flatiron Institute and other members of the Polymathic AI collaboration are trained using real scientific datasets. The models are already leveraging the knowledge they learn from one field to address seemingly completely different problems in another.

The Walrus AI model simulates fluid motion.

Walrus/Polymathic AI

While most AI models — including ChatGPT — are trained on text and images, a multidisciplinary team of scientists has something different in mind: AI trained on physics.

Recently, members of the Polymathic AI collaboration presented two new AI models trained using real scientific datasets to tackle problems in astronomy and fluidlike systems.

The models — called Walrus and AION-1 — are unique in that they can apply the knowledge they gain from one class of physical systems to seemingly completely different problems. For instance, Walrus can tackle systems ranging from exploding stars to Wi-Fi signals to the movement of bacteria.

That cross-disciplinary skill set is particularly exciting because it can accelerate scientific discovery and give researchers a leg up when faced with small samples or budgets, says Walrus lead developer Michael McCabe, a research scientist at Polymathic AI.

“Maybe you have new physics in your scenario that your field isn’t used to handling. Maybe you’re using experimental data, and you’re not quite sure what class it fits into. Maybe you’re just not a machine-learning researcher and just can’t burn the time working through all the possible models that might fit your scenario,” McCabe explains. “Our hope is that training on these broader classes makes something that is both easier to use and has a better chance of generalizing for those users, as the ‘new’ physics to them might be something another field has been handling for a while.”

Using cross-disciplinary models can also improve predictions when data is sparse or when studying rare events, says Liam Parker, a Ph.D. student at the University of California, Berkeley, and a lead researcher developing for AION-1.

The Polymathic AI team recently announced Walrus in a preprint on arXiv.org and presented AION-1 on Friday, December 5, at the NeurIPS conference in San Diego.

Walrus and AION-1 are ‘foundational models,’ meaning they’re trained on colossal sets of training data from different research areas or experiments. That’s unlike most AI models in science, which are trained with a particular subfield or problem in mind. Rather than learning the ins and outs of a particular situation or starting from a set of fundamental equations, foundational models instead learn the basis, or foundation, of the physical processes at work. Since these physical processes are universal, the knowledge that the AI learns can be applied to various fields or problems that share the same underlying physical principles. Foundational models have a host of benefits — from speeding up computations to performing well in low-data regimes to finding physics shared across different fields.

AION-1 is a foundational model for astronomy. It is trained on data from astronomical surveys that are already massive in their own right: the Legacy Survey, the Hyper Suprime-Cam (HSC), the Sloan Digital Sky Survey (SDSS), the Dark Energy Spectroscopic Instrument (DESI) and Gaia. All in all, that’s more than 200 million observations of stars, quasars and galaxies totaling around 100 terabytes of data. AION-1 uses images, spectra and a variety of other measurements to learn as much as it can about astronomical objects. Then, when a scientist obtains a low-resolution image of a galaxy, for example, AION-1 can extract more information about it, learned from the physics of millions of other galaxies.

Walrus’ domain is fluids and fluidlike systems. Walrus utilizes the Well — a massive dataset compiled by the Polymathic AI team. The Well’s data encompasses 19 different scenarios and 63 different fields in fluid dynamics. All in all, it contains 15 terabytes of data describing parameters such as density, velocity and pressure in physical systems as wide-ranging as merging neutron stars, acoustic waves and shifting layers in Earth’s atmosphere.

Such foundational models can be powerful. AION-1 and Walrus can utilize physics seen in a different case and apply it to learn about something new. It is similar to our senses. “Multiple senses together — rather than one at a time — gives you a fuller understanding of an experience,” the AION-1 team explained in a blog post about the project. “Over time, your brain learns associations between how things look, taste and smell, so if one sense is unavailable, you can often infer the missing information from the others.”

Then, when a scientist is performing a new experiment or observation, they have a starting point — a map of how physics behaves in other similar situations. “It’s like seeing many, many humans,” says Shirley Ho, Polymathic AI’s principal investigator and an astrophysicist and machine learning expert. Ho is a senior research scientist at the Flatiron Institute and a professor at New York University. When “you meet a new friend, because you’ve met so many people before now, you are able to map in your head … what this human is going to be like compared to all your friends before,” she says.

Foundational models make scientists’ lives easier by streamlining data processing. Scientists will no longer have to create a new framework from scratch for every project or task; instead, they can start with an already trained AI to use as a foundation. “I think our vision for some of this foundation model is that it enables anyone to start from a really powerful embedding of the data that they’re interested in … and still achieve state-of-the-art accuracy without having to build this whole pipeline from scratch,” says AION-1 lead researcher Parker.

Their goal is to make tools that scientists can use in their day-to-day research. “We want to bring all this AI intelligence” to the scientists who need it, Ho says.


Other Highlights From the NeurIPS 2025 Conference

CosmoBench: CosmoBench is a multiview, multiscale, multitask cosmology benchmark for geometric deep learning. Curated from the state-of-the-art cosmological simulations, CosmoBench is the largest benchmark of its kind, with over 34,000 point clouds and 25,000 directed trees. CosmoBench features challenging evaluation tasks from cosmology and diverse baselines, including cosmological methods, simple linear models and graph neural networks. This presentation will show how CosmoBench is pushing the frontiers of cosmology and geometric deep learning.

Lost in Latent Space: Physicists model and predict the behavior of physical systems using their understanding of the laws of physics. However, these calculations require significant computing power. Flatiron Institute scientists and other members of the Polymathic AI collaboration studied whether a less taxing form of computing can still yield accurate results. Known as ‘latent diffusion modeling,’ this computational model utilizes artificial intelligence to generate high-quality images at a lower computational cost while accurately capturing physical behavior.

Neurons as Detectors of Coherent Sets in Sensory Dynamics: Our perception of touch, taste, sight and pain is mediated by neurons that carry signals from peripheral receptors to the brain. This work shows that these neurons can be understood as detecting ‘coherent sets’ within the sensory stream — groups of stimulus trajectories that evolve together over time and therefore share a common past or a common future. By distinguishing these coherent sets, some neurons predominantly encode what has just occurred, while others reliably signal what is likely to happen next. Traditional classifications of sensory neurons can thus be reinterpreted as reflecting a division between past-focused and future-predictive processing. Understanding how the nervous system separates and transforms sensory input in this way may offer new routes for treating mental illness and may also guide the development of biologically inspired artificial intelligence.

Predicting Partially Observable Dynamical Systems: Scientists can predict the motion of a falling object or the evolution of fluids using deterministic models that compute a single future outcome from past observations. But this approach breaks down for physical systems where much of the state is hidden. A prominent example is the sun: We can observe the activity on its surface, but the processes deep inside remain largely invisible. Without access to those internal conditions, there isn’t enough information to forecast a single ‘correct’ future. Researchers at the Flatiron Institute, together with collaborators in the Polymathic AI project, have developed a probabilistic approach that can infer these hidden solar processes. By incorporating information from the distant past into a diffusion-based generative model, their method produces an ensemble of plausible futures, offering a clearer understanding of how past sunspot activity shapes its future evolution.

A stunning visualisation explores the intricate circulatory system of our oceans

Mike's Notes

This stunning video from NASA's Goddard Space Flight Centre is an excellent example of a complex system. It has everything.

  • Fluid mechanics
  • Emergence

I discovered the video in a recent edition of Aeon Weekly.

It also shows how this circulation system can change and has changed.

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Last Updated

18/05/2025

A stunning visualisation explores the intricate circulatory system of our oceans

By: Kathleen Gaeta Greer
Aeon: 15/05/2025

This video from NASA’s Goddard Space Flight Center provides an unprecedented look at the intricate, interconnected flow of ocean currents around the world. The visualisation was created using a NASA model built from global data gathered from a wide range of inputs, including buoys and spacecrafts. The resulting imagery illustrates how factors including planetary physics, heat and salinity propel the ceaseless oceanic movement of this global ‘conveyer belt’. As the video zooms in on some of the most active and interesting currents around the globe, the NASA oceanographer Josh Willis details how this aquatic circulatory system affects human life, from fisheries to regional climates.

  • Video by NASA’s Goddard Space Flight Center
  • Producer: Kathleen Gaeta Greer

Oil-Drop Walkers - an Analogue for Wave Particle Duality

Mike's Notes

A facinating analogue experiment.

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11/05/2025

Oil-Drop Walkers - an Analogue for Wave Particle Duality

By: Mike Peters
On a Sandy Beach: 19/04/2019

Mike is the inventor and architect of Pipi and the founder of Ajabbi.

There has been an ongoing research effort by Yves Couder at CNRS University in France and John Bush an Applied Mathematician at MIT in the USA using fluid mechanics as an analogue to look at the de Broglie-Bohm interpretation of Quantum Mechanics.

It originated by chance when observing the movement of oil droplets on a vibrating water bath.

It is fascinating stuff.