  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].
