AI agents devised a way to reduce friction of an airplane wing model after being trained using relatively simple computer simulations. The work demonstrates how AI could help speed up the development of聽more efficient and sustainable听惫别丑颈肠濒别蝉.听
How we and our machines move is affected by fluids, from air dragging on wind turbine blades to blood flowing through our veins. But聽calculating what a fluid will do聽under specific circumstances is very difficult, even with supercomputers.聽
鈥淪imulating fluids usually involves millions or billions of coupled differential equations, and even with Moore鈥檚 law, with the fastest computers in the world, we鈥檙e maybe 100 years away from simulating the flows we actually care about at engineering scales,鈥� says聽聽at the University of Washington.聽
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He and his colleagues have discovered that AI might offer a shortcut, because it can devise ways to control fluid flow in complex situations based on relatively simple computer simulations, substituting an AI training period for difficult-to-run computations.
They created a platform, HydroGym, in which many AI agents could tweak how a fluid flowed over virtual objects 鈥� for instance, by adding actuators that inject fluid or changing the object鈥檚 motion 鈥� to聽decrease the drag聽they experienced against virtual fluids. The virtual objects, and the behaviour of the fluids, could be simulated with today鈥檚 computers but varied in levels of complexity.
The AI agents tackled the fluid control task by using a trial-and-error approach known as聽reinforcement learning. They could also coordinate with each other to achieve the best overall performance, a strategy which researchers had not tried for fluids problems on this scale before, says team member聽聽at RWTH Aachen University in Germany.
The team discovered that the AI agents could apply lessons learned from experimenting on more simple, textbook examples in a computer simulation to work out how virtual objects would behave in more complex scenarios 鈥� even without access to a computer simulation of those complex scenarios.聽
For instance, after working out how to control flow of a turbulent fluid in a flat channel, the agents successfully took on the task of controlling fluid surrounding a curved, three-dimensional airplane wing model, ultimately managing to decrease the energetically wasteful friction between the wing and the fluid by 38 per cent.聽
鈥淭he AI wasn鈥檛 just memorising one flow configuration. It is picking up something genuinely general about how fluids behave, not just fitting to the one setup it was trained on,鈥� says聽at the University of Michigan, who was part of the team.
This transfer of principles from a simple to a more complex case suggests that the AI agents could help us tackle ever-bigger and more intricate fluid flow scenarios, without requiring those scenarios to be fully simulated on a computer first. It may eventually be possible to explore fluid flow scenarios that are currently too challenging to simulate.聽
The researchers also hope HydroGym will provide computational infrastructure for AI to become a well-tested tool across all areas of science and engineering that deal with fluids, similar to how聽AlphaFold聽is used across studies of proteins, says Vinuesa.聽
鈥淚f we took something like global shipping, if you could reduce the drag by one percentage point, that would result in probably billions of dollars of fuel saving and an enormous amount of reduction in greenhouse gas emissions,鈥� says Brunton. 鈥淭he financial and the ecological impact is profound for the tiniest improvements.鈥澛�
Nature
