A Numerical Tool for the Analysis of Bioinspired Aquatic Locomotion

Autonomous Underwater Vehicles (AUVs) are increasing in popularity because they can explore the ocean’s depths and perform operations without risking human life, and their applications range from search and rescue to scientific research and military operations. However, despite recent progress, AUVs still have worse swimming capabilities than fishes. The locomotion strategies of fishes have achieved outstanding swimming performances because they have evolved through natural selection for millions of years, so investigating how fishes propel themselves is of great interest to exploit the same mechanism for AUV propulsion. An effective bioinspired design cannot be carried out by blindly copying all the characteristics of fish locomotion, but it should be based on a deep understanding of the underlying physical principles that make fish swim so efficiently. Fish swimming is a complex phenomenon involving the unsteady fluid dynamics of a deformable moving body immersed in water, and advanced numerical tools are needed to figure out the mechanism of fish propulsion. In this chapter, the most popular numerical methods used to analyze fish swimming are described, and the main novel aspects of the proposed technique based on an overset grid are introduced.

This chapter presents the physical principles that allow fishes to propel themselves and analyzes why they move with high energy efficiency while having outstanding swimming performances. Firstly, the different strategies of swimming locomotion of fishes and cetaceans are described emphasizing the differences in the characteristics of the surrounding flow. Then, two analytical models of fish swimming are introduced and compared: the Slender Body Theory and the Waving Plate Model. These models lie on several simplifying assumptions, which do not reflect the actual fish geometry and behavior; still, they are very useful to understand the general basic principles of fish propulsion from a mathematical point of view. The same simplified approach is then used to analyze the vortices in the wake and relate them to the produced thrust. Finally, it is described how the energy efficiency is measured for a self-propelling body, and it is related to the Strouhal number.

This chapter describes the kinematics of cownose ray swimming, relating it to fin geometry and skeletal structure. The equation of the deformed fin surface is presented, and the influence of different kinematic parameters on fin movement is analyzed. Previous numerical studies about batoid swimming are briefly described, and the numerical implementation of the CFD model of cownose ray swimming is presented.

This chapter describes the kinematics of cownose ray swimming, relating it to fin geometry and skeletal structure. The equation of the deformed fin surface is presented, and the influence of different kinematic parameters on fin movement is analyzed. The numerical implementation of the CFD model of cownose ray swimming is presented, and finally, the results are analyzed, highlighting how the swimming performances and the wake structure change according to different kinematic parameters. The main parameters that affect swimming performances are frequency and wavelength of fin motion and frequency resulted in being proportional to the swimming velocity, and it did not affect the dimensionless parameters like energy efficiency and the Strouhal number, whereas a variation in wavelength implies changing the angle of attack of the fin, resulting in a different flow and strongly affecting all swimming performances. The vortices in the wake form a Reverse Karman Street, and vortex rings are connected like in a chain, similarly to other swimming animals, and for some wavelengths, a leading-edge vortex can be observed too. The energy efficiency is one of the highest among fishes, reaching 89% for the best combination of parameters, and the Strouhal number of most analyzed swimming motions is comprised between 0.2 and 0.4.