Diving into a simple anguilliform swimmer's sensitivity

TL;DR

This study investigates how various parameters affect the swimming performance of a simple 2D anguilliform model, revealing stroke frequency as the most influential factor and identifying optimal efficiency trends through extensive sensitivity analysis.

Contribution

It provides a comprehensive global sensitivity analysis of a simple anguilliform swimmer model using Sobol sequences, highlighting key parameters influencing performance.

Findings

Performance is most sensitive to stroke frequency.

Optimal efficiency trends are identified in the parameter space.

Performance metrics are projected onto 2D subspaces for analysis.

Abstract

Computational models of aquatic locomotion range from individual modest simple swimmers in 2D to sophisticated 3D individual swimmers to complex multi-swimmer models that attempt to parse collective behavioral dynamics. Each of these models contain a multitude of model input parameters to which the model outputs are inherently dependent, i.e., various swimming 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 \textit{C. elegan}. The parameter sensitivities were explored with respect to the fluid scale (Reynolds number, Re), stroke (undulation) frequency, as well as a kinematic parameter controlling the velocity and acceleration of each upstroke and downstroke. In total, 5000 fluid-structure interaction simulations were performed, each with a unique parameter combination selected via a Sobol sequence. Thus, global sensitivity analysis was performed using Sobol sensitivity analysis. Results indicate that the swimmer's performance is most sensitive to variation in its stroke frequency. Trends in swimming performance were discovered by projecting the performance metrics from the overall 3D parameter space onto particular 2D subspaces and Pareto-like optimal fronts were identified for swimming efficiency. This work is a natural extension of the parameter explorations of the same model from [1].