Swimming through parameter subspaces of a simple anguilliform swimmer

TL;DR

This study identifies parameter subspaces that enhance swimming performance in a simple anguilliform model, revealing sensitivities to fluid scale and stroke frequency, and mapping optimal performance fronts.

Contribution

It introduces a method to locate robust parameter subspaces for optimal swimming performance using numerous simulations of a simplified fluid-structure interaction model.

Findings

Performance is more sensitive to fluid scale and stroke frequency.

Pareto fronts for transport cost and speed were identified.

Parameter subspaces for enhanced performance can be efficiently located.

Abstract

Computational scientists have investigated swimming performance across a multitude of different systems for decades. Most models depend on numerous model parameters and performance is sensitive to those parameters. In this paper, parameter subspaces are qualitatively identified in which there exists enhanced swimming performance for an idealized, simple swimming model that resembles a C. 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.