With the right physics, it’s possible to blast a box of circuits with pinpoint precision across the entire solar system into the whiskers of distant worlds.
But add a little milk to your tea, and the best physicists can do is take a chance on the swirl patterns you’ll see in your drink.
As far as science is concerned, fluids are indeed chaotic elements, but a new way to calculate their motion could soon make their flow more predictable.
Scientists can use it not only to improve our understanding of fluid dynamics, but to make everything from weather forecasting to vehicle design more accurate.
Georgia Tech physicists have shown that it is possible to identify moments when turbulence reflects measurable patterns, effectively finding mathematically prescribed flickering in the chaos.
“For nearly a century, turbulence has been statistically described as a random process,” said Roman Grigoriev, a physicist at the Georgia Institute of Technology.
“Our results provide the first experimental illustration that, on appropriately short timescales, the dynamics of turbulence are deterministic — and link this to the underlying deterministic governing equations.”
Turbulence is difficult to predict, mainly because of the way small eddies or eddies form in the fluid. When matter flows in a straight line with a smooth electric current, its velocity and trajectory are easy to predict. If any path in the flow becomes sluggish, perhaps due to dragging along less fluid surfaces, the fluid will roll back on its own.
With each new plume, a new surface forms that can generate new eddies.
To complicate matters, the behavior of each swirl is influenced by multiple factors — from pressure to viscosity — that quickly create a storm in a teacup that no computer can track.
Up close, it all seems so random. Taking a step back, the statistics clearly show that the whole process is still firmly embedded in the old rules that govern all other moving objects in the universe.
“Turbulent flow can be thought of as a car driving along a series of roads,” Grigoriev said.
“Perhaps a better analogy would be a train, which not only follows a railway on a prescribed schedule, but has the same shape as the railway it follows.”
Turbulence can be described as a numerical simulation or a physical model, just like our railway analogy. Just as train timetables help keep you working on time, sticking with math to deal with turbulence is the only way if you want reliable predictions.
Unfortunately, all of these numbers add up quickly, making it computationally expensive.
To see if there was a way to simplify the predictions, the team set up a tank with transparent walls and a liquid containing tiny fluorescent particles. Channeling fluid between a pair of independently rotating cylinders and tracking what’s glowing is like watching a train pass through a station in real time.
However, the researchers actually need to come up with the timelines first to see which ones are similar to what they see.
Doing so involves computing solutions to a set of equations devised nearly 200 years ago. By aligning the experiments with the mathematical results, the team can determine when specific turbulent patterns called coherent structures appear.
Although they frequently occur in moving fluids, the timing of coherent structures is unpredictable. In this particular setup, the coherent structure follows a quasi-periodic pattern consisting of two frequencies — one tilted around the flow’s symmetry axis, and one based on another set of changes in the surrounding current.
While it’s not a simple set of equations that can describe various forms of turbulence, it does demonstrate the role that coherent structures can play in making them more predictable.
By extending this work, future studies could make their turbulent “timelines” more dynamic, characterizing them in more detail than statistical averages.
“It could allow us to significantly improve the accuracy of weather forecasts and, most notably, extreme events like hurricanes and tornadoes,” Grigoriev said.
“Dynamic frames are also critical to our ability to design flows with desired characteristics, such as reducing drag around vehicles to improve fuel efficiency, or enhancing mass transit to help remove more air from the atmosphere in the emerging direct air capture industry. more carbon dioxide.”
It might even end up telling you what to expect in your next cup of tea.
This study was published in NASA.