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Scientists are often trained to look for the absolute best solution to a given problem. On a chalk board, this might look something like drawing a graph to find the minimum or maximum point of a function. When designing a turbojet, this may mean adjusting the angles of the rotor blades to a small degree to achieve a tenth of a percent increase in efficiency.
Adrian Bejan, the J.A. Jones Distinguished Professor of Mechanical Engineering at Duke University, was busy showing the first to a class full of students when he had an idea: This isn’t how nature works. Evolution is a series of design changes that happen on their own in a clear direction; He never attaches himself to a single point on the drawing board. The evolving system or animal is free to follow what works. Not so much that its performance suffers greatly, but enough that it opens up access to other near-optimal design options.
Since science often looks for clues to nature to solve challenges, Bejan wondered if it might look the other way, to predict nature before looking at it. If problem solvers and builders are free to miss the absolute highest mark, how reasonable do they consider designs to be?
That’s the question Bejan asked in a new paper published online May 16 in the journal Biosystems. Using two relatively simple examples—passenger passages from a train and a bird flapping its wings—he discovered that the answer is, “Too much.”
“In engineering, design, theater, architecture or even the organization of this university, any form of design benefits from the ability to make good but imperfect decisions and the freedom to move forward and consider other opportunities for improvement,” Bejan said. “If one clings to the idea of the best of all, nothing new will ever be created.”
In the newspaper, Bijan first looks at the example of passengers who arrive by train and walk through a room with many exit points. With the total area of the room remaining constant but the length and width of the room can be changed freely, it solves the optimal shape of the room to get all occupants to where they are going faster. With the solution equations in hand, he shows that giving wiggle room of 1% for defects away from best performance opens design space by 28%.
In his second example, Bejan looks at the flapping motion of birds at roughly constant height and speed. Considering the various forces involved—drag during glide, lift generated by wing size, speed, and body size, among others—he formulates an equation for the wings’ rhythm necessary to maintain a constant speed with minimal effort. Although there is a perfect answer, Bejan again states that allowing only 1% shortfall above the theoretical minimum effort opens up the design space by 20%.
Bejan says he chose these examples because they involved changing only one variable, one degree of freedom—the shape of the chamber or the flapping rhythm of the wing. In more complex examples with many variables, these small tolerances for defects create a broader range of “good enough” solutions.
The lesson is that science now has a predictive idea of how nature works. By focusing less on finding the absolute perfect designs, researchers may use the freedom to move frequently toward entirely new design concepts that would not otherwise be in their sights. It also gives designs, methods, and entire fields of study the ability to adapt to a changing world.
“The dogma of chasing best design is unhelpful,” Bejan said. “Science education should go hand in hand with the freedom to take a shot, hit the vicinity of the mark and move on. The end goal is not just to hit the bullseye, but to keep more arrows in your quiver to keep taking shots over a long period of time.”
This work was supported by a grant from CaptiveAire Systems.
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