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Don't be jelly: Exploring effective jellyfish locomotion

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arxiv 1904.09340 v2 pith:V2ERJAX3 submitted 2019-04-19 physics.flu-dyn q-bio.QM

classification physics.flu-dynq-bio.QM
keywords jellyfishperformancebellfluidparametercontractionlocomotionsensitivity
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Jellyfish have been called one of the most energy-efficient animals in the world due to the ease in which they move through their fluid environment, by product of their morphological, muscular, and material properties. We investigated jellyfish locomotion by conducting \textit{in silico} comparative studies and explored swimming performance across different fluid scales (e.g., Reynolds Number), bell contraction frequencies, and contraction phase kinematics for a jellyfish with a fineness ratio of 1 (ratio of bell height to bell diameter). To study these relationships, an open source implementation of the immersed boundary method was used (\textit{IB2d}) to solve the fully coupled fluid-structure interaction problem of a flexible jellyfish bell in a viscous fluid. Thorough 2D parameter subspace explorations illustrated optimal parameter combinations in which give rise to enhanced swimming performance. All performance metrics indicated a higher sensitivity to bell actuation frequency than fluid scale or contraction phase kinematics, via Sobol sensitivity analysis, on a high performance parameter subspace. Moreover, Pareto-like fronts were identified in the overall performance space involving the cost of transport and forward swimming speed. Patterns emerged within these performance spaces when highlighting different parameter regions, which complemented the global sensitivity results. Lastly, an open source computational model for jellyfish locomotion is offered to the science community that can be used as a starting place for future numerical experimentation.

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  1. Realizing Robotic Swimming with Unified Fluid-Robot Multiphysics

    cs.RO 2025-06 conditional novelty 6.0 of 10

    A differentiable, strongly coupled fluid-robot simulator, derived from a single least-action principle, produces swimming gaits that transfer to a physical eel robot.

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