Model simulates variable flap stiffness for the best lift

Date:
April 6, 2023
Source:
University of Illinois Grainger College of Engineering
Summary:
There is extensive research on how a fixed-position flap affects
lift in the realm of fluid-structure interaction. So, researchers
conducted a bio-inspired study with a novel twist -- variable
stiffness over time much like a bird can tense, or stiffen, the
musculature and tendons connected to covert feathers -- to learn
more about how it affects lift.

The results showed a 136 percent benefit.


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FULL STORY ========================================================================== There is extensive research on how a fixed-position flap affects
lift in the realm of fluid-structure interaction. However, taking the conversation in a new direction, researchers at the University of Illinois Urbana-Champaign conducted a bio-inspired study with a novel twist --
variable stiffness -- to learn more about how it affects lift.


==========================================================================
The researchers wondered if they could model a flap on an airfoil, or
wing, with varying stiffnesses over time much like a bird can tense,
or stiffen, the musculature and tendons connected to covert feathers.

"We know from previous studies that having a flap with some stiffness
could help increase lift in the stall regime," said Andres Goza,
a professor in the Department of Aerospace Engineering at UIUC. "So,
that begged the question: What if you could tune the stiffness? How
much benefit would there be?" The results of the study showed a big
benefit. "Our flap with a variable stiffness was better than having no
flap by 136 percent and 85 percent better than the best possible single stiffness flap from an earlier study we conducted." Goza and his student Nirmal Nair modeled a variable stiffness actuator on a flap hinged to
an airfoil via a torsional spring to create a hybrid controller that
changes the stiffness over time. The flap itself cannot flop or bend
in any way. The stiffness refers to how tightly the torsional spring is
holding onto the flap.

"In the simulation, we trained a controller that determined a specific
value on the spectrum from very stiff to very loose. The controller was
built using reinforcement learning, and trained to select a stiffness
to improve lift on the airfoil," Goza said.

"Using the variable stiffness actuators, we obtain the changes in
stiffness values of the spring. The spring is a simplified model. In
practice, this functionality can be implemented using variable stiffness actuators, though this is a non-trivial step that would require a new
research effort, beyond the scope of what we looked at. The results
of our tuneable stiffness paradigm were compared to the best possible
single stiffness case, obtained by building a performance map for several different simulations corresponding to a single stiffness value each."
Goza said the lift improvements are achieved due to large-amplitude flap oscillations as the stiffness varies over four orders of magnitude.

"For the first nine time units, the controller tried different stiffnesses
and learned what happened," Goza said. "Then we turned it loose for the remainder of the simulation: at a given instance in time, it decides
to change the stiffness and actively adapt over time based on what the
flow is doing to get a boost in lift." Goza said it is complicated to
develop a control strategy like this one.

"As the stiffness changes, the flap moves. Then the flap motion changes
the airflow around it, so there is a complex coupling going on," Goza
said. "Now the flap will respond differently to the change of the flow
field around it and as the flow field changes, the response of the flap
will change again.

Simulating this two-way coupling is a source of complexity.

"A strength of our work is that we model all of that. We fully account for
the two-way coupling between the structural motion and the response. And
that's key to developing an accurate controller. We need to be able to
say, when I change the stiffness, here's the interplay that will happen
and harness that to give it a better lift." Goza said most often when
people think about control, it's about feedback. We receive information
about a system, then use that information to make a decision. There are consequences, and you keep auto-correcting.

"This hybrid controller tunes the stiffness, but we call it hybrid
because we don't directly control the flap motion. We're just saying
the flap has a specific stiffness, and I am going to actuate that and
change the stiffness.

Everything that happens next is based on the physics of that
stiffness. The flap will feel what's happening in the flow and start
deploying of its own accord. And it's going to start inducing these other dynamics." Goza said the most natural application for this research is unoccupied vehicles that have onboard computers.

"For these smaller aircraft, gusts can have a much larger impact,"
Goza said.

"They need to be more maneuverable, for example in natural disasters there
may be a need to reach a location where humans can't easily travel." He
added that computation has utility "because you can allow the controller
to vary the stiffness across 4 orders of magnitude, and whatever
the resulting number is just gets used in the simulation. You're not constrained by physical limitations. That lets us explore parameter spaces
that we wouldn't otherwise know about, and use that as a springboard to motivate clever experimentalists to realize these parameter ranges.

"At this point in the research, the structural designs that undergo the required stiffness changes don't exist. So, in this way computation can
inspire material scientists to develop new materials/structural design paradigms that can do it," Goza said.

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========================================================================== Story Source: Materials provided by University_of_Illinois_Grainger_College_of_Engineering.

Original written by Debra Levey Larson. Note: Content may be edited for
style and length.


========================================================================== Journal Reference:
1. Nirmal J. Nair, Andres Goza. Bio-inspired variable-stiffness
flaps for
hybrid flow control, tuned via reinforcement learning. Journal of
Fluid Mechanics, 2023; 956 DOI: 10.1017/jfm.2023.28 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2023/04/230406152642.htm

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