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Recirculating and swirling turbulent flow in airplane wings hurt fuel economy. A more accurate method for modeling turbulence could help aircraft manufacturers design more efficient planes. Image credit: Karthik Duraisamy, U-M Aerospace Engineering.ANN ARBOR—A thorny aerodynamics problem is about to get a Netflix-style big data treatment from a University of Michigan-led team of engineers.
The engineers are developing a better description of turbulence, which could enable radical, more efficient airplane designs and improve prediction in other fields where chaotic flow comes into play – from the human bloodstream to weather forecasting.
NASA's Leading Edge Aeronautics Research program has recently awarded the project $1.6 million, with the promise of a larger grant if significant progress is made.
"The general impression within the turbulence modeling community is that ideas for improved models have completely stagnated, especially over the past two decades," said Karthik Duraisamy, an assistant professor of aerospace engineering at U-M. He heads the project, which includes collaborators at Stanford, Iowa State, Boeing and the Silicon Valley firm Pivotal Inc. "We are going to take a completely new approach."
Duraisamy compares their method to algorithms for recommending films and products.
The turbulence the research team will focus on is caused by the plane moving through the air so fast that its shape can no longer support smooth airflow around it. To maximize fuel economy in terms of lift and thrust, an airplane wing must consistently maintain that smooth airflow just at the point where a little more speed would break the air up into disorganized eddies. When this turbulence dominates, fuel efficiency takes a nosedive.
The work wouldn't make plane rides smoother. The turbulence that prompts crews to flip on the fasten seatbelt sign is generated by big air masses moving in the atmosphere.
Engineers would like to design wings that reliably operate at peak performance on computers before they spend hundreds of millions of dollars building, testing and tweaking prototypes. But presently that's difficult to do.
The major snag is turbulence. To the chagrin of scientists, its chaotic nature has defied an accurate mathematical description, so it's hard to simulate. Turbulent flow can be calculated precisely in situations involving slow breezes over small bodies, such as insect flight, but accurate models of fast-moving flows typical of airplane flight require too much computing power.
"You can go to the largest supercomputer in the world right now – one that can execute a quadrillion instructions a second – and you can run a simulation on that computer for the next 100 years, and you still may be able to solve just a fraction of a flow that's important to a large commercial airplane such as the Airbus A380," Duraisamy said..
Zoomed-in snapshot of turbulent flow in a tiny region on wing surface. The new study seeks to translate such data directly into more accurate turbulence modeling. Image credit: Taraneh Sayadi, Stanford UniversityBy building a model from a database of airflow measurements and computations, he and his team hope to make predictions based on more realistic approximations.
"Netflix has this database with a large number of users who made a large number of choices. Based on this, what is the next choice you are going to make?" he said.
This is known as machine learning because humans aren't developing the prediction software directly – instead, they write an algorithm that can condense information in a database into the predictive model. The accuracy of the model improves automatically with the amount and quality of the data.
"Applying learning techniques in turbulence is not as simple because Netflix- and Amazon-type predictions do not have to obey the laws of physics. So that's the big challenge – to see how we can build physics into this machine-learning approach," Duraisamy said.
One way that the team will include physics is by solving the turbulence problem for the simple scenarios that can be calculated without approximations. These solutions – from research groups all over the world – will populate the database wherever possible. For more complex situations, they plan to fill in the gaps with the most reliable experimental measurements they can find. This database will then be used to help provide approximations while solving turbulence model equations.
If successful, the method could be expanded to other areas, including combustion and applications outside aviation. For instance, designers of many drug delivery systems need to model the pulsing of blood through arteries, which creates turbulence that affects whether drug carriers can bind to the vessel wall. Meteorologists need accurate models for turbulence in the atmosphere as it affects the formation and evolution of storm systems.
Collaborators include Juan Alonso and Brendan Tracey at Stanford University, Paul Durbin at Iowa State University, Philippe Spalart at Boeing and Pivotal Inc.
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