CFD Studies of the Effects of Waveform on Swimming Performance of Carangiform Fish

Numerical Investigation of Dimensionless Parameters in Carangiform Fish Swimming Hydrodynamics

Research into how fish and other aquatic organisms propel themselves offers valuable natural references for enhancing technology related to underwater devices like vehicles, propellers, and biomimetic robotics. Additionally, such research provides insights into fish evolution and ecological dynamics. This work carried out a numerical investigation of the most relevant dimensionless parameters in a fish swimming environment (Reynolds Re, Strouhal St, and Slip numbers) to provide valuable knowledge in terms of biomechanics behavior. Thus, a three-dimensional numerical study of the fish-like lambari, a BCF swimmer with carangiform kinematics, was conducted using the URANS approach with the k-ω-SST transition turbulence closure model in the OpenFOAM software. In this study, we initially reported the equilibrium Strouhal number, which is represented by St∗, and its dependence on the Reynolds number, denoted as Re. This was performed following a power-law relationship of St∝Re(-α). We also conducted a comprehensive analysis of the hydrodynamic forces and the effect of body undulation in fish on the production of swimming drag and thrust. Additionally, we computed propulsive and quasi-propulsive efficiencies, as well as examined the influence of the Reynolds number and Slip number on fish performance. Finally, we performed a vortex dynamics analysis, in which different wake configurations were revealed under variations of the dimensionless parameters St, Re, and Slip. Furthermore, we explored the relationship between the generation of a leading-edge vortex via the caudal fin and the peak thrust production within the motion cycle.

Diving into a Simple Anguilliform Swimmer’s Sensitivity

Computational models of aquatic locomotion range from modest individual simple swimmers in 2D to sophisticated 3D multi-swimmer models that attempt to parse collective behavioral dynamics. Each of these models contain a multitude of model input parameters to which its outputs are inherently dependent, that is, various performance metrics. In this work, the swimming performance’s sensitivity to parameters is investigated for an idealized, simple anguilliform swimming model in 2D. The swimmer considered here propagates forward by dynamically varying its body curvature, similar to motion of a Caenorhabditis elegans. The parameter sensitivities were explored with respect to the fluid scale (Reynolds number), stroke (undulation) frequency, as well as a kinematic parameter controlling the velocity and acceleration of each upstroke and downstroke. The input Reynolds number and stroke frequencies sampled were from [450, 2200] and [1, 3] Hz, respectively. In total, 5000 fluid–structure interaction simulations were performed, each with a unique parameter combination selected via a Sobol sequence, in order to conduct global sensitivity analysis. Results indicate that the swimmer’s performance is most sensitive to variations in its stroke frequency. Trends in swimming performance were discovered by projecting the performance data onto particular 2D subspaces. Pareto-like optimal fronts were identified. This work is a natural extension of the parameter explorations of the same model from Battista in 2020.

Swimming Through Parameter Subspaces of a Simple Anguilliform Swimmer

Computational scientists have investigated swimming performance across a multitude of different systems for decades. Most models depend on numerous model input parameters and performance is sensitive to those parameters. In this article, parameter subspaces are qualitatively identified in which there exists enhanced swimming performance for an idealized, simple swimming model that resembles a Caenorhabditis elegans, an organism that exhibits an anguilliform mode of locomotion. The computational model uses the immersed boundary method to solve the fluid-interaction system. The 1D swimmer propagates itself forward by dynamically changing its preferred body curvature. Observations indicate that the swimmer’s performance appears more sensitive to fluid scale and stroke frequency, rather than variations in the velocity and acceleration of either its upstroke or downstroke as a whole. Pareto-like optimal fronts were also identified within the data for the cost of transport and swimming speed. While this methodology allows one to locate robust parameter subspaces for desired performance in a straight-forward manner, it comes at the cost of simulating orders of magnitude more simulations than traditional fluid–structure interaction studies.