Fish maintain high swimming efficiencies over a wide range of speeds. A key to this achievement is their flexibility, yet even flexible robotic fish trail real fish in terms of performance. Here, we explore how fish leverage tunable flexibility by using their muscles to modulate the stiffness of their tails to achieve efficient swimming. We derived a model that explains how and why tuning stiffness affects performance. We show that to maximize efficiency, muscle tension should scale with swimming speed squared, offering a simple tuning strategy for fish-like robots. Tuning stiffness can double swimming efficiency at tuna-like frequencies and speeds (0 to 6 hertz; 0 to 2 body lengths per second). Energy savings increase with frequency, suggesting that high-frequency fish-like robots have
the most to gain from tuning stiffness.
What’s New: Our work is the first that combines biomechanics, fluid dynamics, and robotics to comprehensively study tail stiffness, which helps to uncover the long-existing mystery about how tail stiffness affects swimming performance. What is even more fantastic is that we are not just focused on theory analysis, but also on proposing a practical guide for tunable stiffness. Our proposed tunable stiffness strategy has proved effective in realistic swimming missions, where a robot fish achieved high speed and high efficiency swimming simultaneously.
Key Insights: Our study uncovered the secrets of highly efficient swimming at varying speeds which could inform the design of next generation underwater drones.
The paper Tunable stiffness enables fast and efficient swimming in fish-like robots is on AAAS.
Meet the author Q. Zhong, J. Zhu, F. E. Fish, S. J. Kerr, A. M. Downs, H. Bart-Smith and D. B. Quinn from Department of Mechanical and Aerospace Engineering, University of Virginia, Department of Biology, West Chester University, Department of Electrical and Computer Engineering, University of Virginia.
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