Tail shapes lead to different propulsive mechanisms in the body/caudal fin undulation of fish

Abstract

In this paper, we investigate the hydrodynamics of swimmers with three caudal fins: a round one corresponding to snakehead fish ( Channidae), an indented one corresponding to saithe ( Pollachius virens), and a lunate one corresponding to tuna ( Thunnus thynnus). A direct numerical simulation (DNS) approach with a self-propelled fish model was adopted. The simulation results show that the caudal fin transitions from a pushing/suction combined propulsive mechanism to a suction-dominated propulsive mechanism with increasing aspect ratio ( AR). Interestingly, different from a previous finding that suction-based propulsion leads to high efficiency in animal swimming, this study shows that the utilization of suction-based propulsion by a high- AR caudal fin reduces swimming efficiency. Therefore, the suction-based propulsive mechanism does not necessarily lead to high efficiency, while other factors might play a role. Further analysis shows that the large lateral momentum transferred to the flow due to the high depth of the high- AR caudal fin leads to the lowest efficiency despite the most significant suction.

Hydrodynamic effects of the caudal fin shape of fish in carangiform undulatory swimming

“…[30][31][32] Further validation of self-propelling is performed on the dynamics of a fish when starting movement. 33,34 A triangular mesh is used to present the swimmer surface, where the body has 9840 elements and 4920 node points, while the tail has 1320 elements and 713 node points. The simulation domain is 7L × 5L × 4L, which is discretized by a nonuniform Cartesian mesh.…”

“…The similar model configuration of Chang et al and Zhang et al gives the same efficiency trend. Chang et al also claimed that the lateral flow for the low-aspect ratio caudal fin is strong, leading to low swimming efficiency, while the simulation of Song et al 34 using the caudal fin with same area found that the lateral flow for the high-aspect ratio caudal fin is strong. Therefore, the effect of caudal fin shape varies with multiple factors, which leads to different optimal shapes.…”

Real-time implementation of deep reinforcement learning controller for speed tracking of robotic fish through data-assisted modeling

“…This complex mechanism and distributed nature of hydrodynamics hinder the direct control strategies. Though various impressive results have been obtained in propulsive mechanisms, [5][6][7][8] mechanical structure, [9][10][11][12] and kinematic control, [13][14][15] the findings are insufficient to drive a robotic fish autonomously in an unstructured environment. The main challenge lies in motion control of robotic fish due to the complex dynamic properties of the medium.…”

Numerical simulations of biological flows and their engineering applications

“…12 consider a self-propelled swimmer under control based on deep reinforcement learning method; Song et al. 13 investigate the effects of tail shapes on the propulsive mechanisms in the body/caudal fin undulation of fish.…”

“…Regarding numerical tools used, there are seven papers using immersed boundary methods, 5,7,11,13–15,18 nine papers using body-fitted mesh methods 6,8–10,12,16,17,19,21 and two papers using other methods. 20,22 Among the applications involving moving boundaries, there are seven papers using immersed boundary methods, 5,7,11,13–15,18 one paper using an open source finite element software Elmer, 6 and three papers using commercial software Ansys/Fluent, 8–10,12 indicating that the immersed boundary method is an emerging alternative in solving flows involving moving boundaries and fluid-structure interaction (see also a recent review paper 23 ).…”