Optimal Flow Sensing for Schooling Swimmers.

Pascal Weber,Guido Novati,Georgios Arampatzis,Costas Papadimitriou,Petros Koumoutsakos,Siddhartha Verma

doi:10.3390/biomimetics5010010

Pascal Weber, Guido Novati + Show 4 more

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https://doi.org/10.3390/biomimetics5010010

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Fish schooling implies an awareness of the swimmers for their companions. In flow mediated environments, in addition to visual cues, pressure and shear sensors on the fish body are critical for providing quantitative information that assists the quantification of proximity to other fish. Here we examine the distribution of sensors on the surface of an artificial swimmer so that it can optimally identify a leading group of swimmers. We employ Bayesian experimental design coupled with numerical simulations of the two-dimensional Navier Stokes equations for multiple self-propelled swimmers. The follower tracks the school using information from its own surface pressure and shear stress. We demonstrate that the optimal sensor distribution of the follower is qualitatively similar to the distribution of neuromasts on fish. Our results show that it is possible to identify accurately the center of mass and the number of the leading swimmers using surface only information.

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Fish navigate in their habitats by processing visual and hydrodynamic cues from their aqueous environment
We find that the identified optimal sensor locations for the two-dimensional artificial swimmers have similarities to biological systems indicating common governing physical mechanisms for the hydrodynamics of natural and artificial swimmers
We examine the optimal arrangement of pressure gradient and shear stress sensors on the surface of a swimmer trailing a school of self-propelled swimmers

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Fish navigate in their habitats by processing visual and hydrodynamic cues from their aqueous environment. Such cues may serve to provide awareness of their neighbors as fish adapt their swimming gaits in groups. More recent studies have shown that even blinded fish can keep station in a school [2]. Such capabilities are of particular importance in flow environments where vision capabilities may be limited [3]. The flow environment is replete with mechanical disturbances (pressure, shear) that can convey information about the sources that generated them. Using the information contained in the flow, the cylinder diameter, the flow velocity, and the position relative to the generated Kármán vortex

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