With the right physics, it’s possible to blast a box of clear circles across the solar system with pinpoint accuracy to come inside a mustache from distant worlds.
But stir a little milk into your tea and the best physicists can do is risk guessing what kinds of patterns you’ll see spinning in the drinks.
Fluids are really chaotic elements as far as science goes, but a new way of calculating their motion could soon make their flow more predictable.
Not only can scientists use this to improve our understanding of hydrodynamics, but it can make everything from weather forecasts to vehicle design vastly more accurate.
Physicists from the Georgia Institute of Technology have shown that it is possible to identify the moments in which turbulence reflects measurable patterns, and effectively find flashes of the mathematically defined order within the hoopla.
“Nearly a century ago, perturbation was described statistically as a stochastic process,” Says Georgia Tech physicist Roman Grigoriev.
“Our results provide the first empirical demonstration, on appropriately short time scales, that perturbation dynamics is deterministic—and relate it to the underlying deterministic equations.”
Trouble is hard to predict Largely because of the way small vortices or vortices are formed in a fluid. When matter flows in a straight line in a smooth stream, its speed and trajectory are easy to predict. If any path in the stream becomes sluggish, perhaps by dragging along a less moving surface, the fluid will coil back on itself.
With each new crimping current, a new surface forms that can produce new vortices.
Just to make it more complicated, each vortex acts at the whims of a number of factors — from pressure to viscosity — and quickly adds up to a storm in a teacup that no computer can hope to track.
Up close, everything looks very random. Take a step back, and the statistics will show that the overall process remains firmly embedded in the same ancient rules that govern every other moving object in the universe.
“Turbulence can be thought of as a vehicle following a series of roads,” Says Grigoriev.
“Perhaps a better analogy is a train, which not only follows a railway according to a fixed schedule, but also has the same shape as the railway that it follows.”
Just as with our standard rail, perturbation can be described as either numerical simulation or by means of physical models. And just as a train schedule is helpful in getting you to run on time, sticking to a mathematical approach to turbulence is the only way to go if you want reliable forecasts.
Unfortunately, all these numbers can be summed up quickly, which makes the calculations costly.
To see if there was a way to simplify the forecast, the team created a tank with transparent walls and a liquid that contained small fluorescent particles. Directing fluid between a pair of independently rotating cylinders and tracking the glowing contents was like watching trains roll through the station in real time.
However, the researchers actually needed to come up with timelines first and see which ones looked like what they were seeing.
Doing so involves computational solutions to set of equations Created nearly 200 years ago. By aligning the experiment with the mathematical results, the team can determine when certain disturbance patterns, called coherent structures, appear.
Although they appear regularly in moving fluids, the timing of coherent structures is unpredictable. In this particular setup, the coherent structures adhered to a quasi-periodic pattern consisting of two frequencies – one rotating around the axis of flow symmetry, and the other based on another set of transitions in the surrounding current.
Although it is not a simple set of equations that can describe turbulence in all its forms, it does show the role that coherent structures can play in making it more predictable.
By expanding on this work, future research can make its “timelines” of perturbations more dynamic, describing them in more detail than statistical averages can provide.
“It could give us the ability to dramatically improve the accuracy of weather forecasts, most notably enabling the prediction of extreme events such as hurricanes and hurricanes,” Says Grigoriev.
“The dynamic framing is also essential for our ability to engineer flows with desired properties, for example, reducing drag around vehicles to improve fuel efficiency, or enhancing mass transit to help remove more carbon dioxide from the atmosphere in the emerging direct air capture industry.”
He may even finally tell you what to expect to see in your next cup of tea.
This research was published in PNAS.