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Open AccessArticle

Optimal Flow Sensing for Schooling Swimmers

1
Computational Science and Engineering Laboratory, ETH Zürich, Clausiusstrasse 33, 8092 Zürich, Switzerland
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Collegium Helveticum, 8092 Zurich, Switzerland
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Department of Ocean and Mechanical Engineering, Florida Atlantic University, Boca Raton, FL 33431, USA
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Harbor Branch Oceanographic Institute, Florida Atlantic University, Fort Pierce, FL 34946, USA
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Department of Mechanical Engineering, University of Thessaly, Pedion Areos, GR-38334 Volos, Greece
*
Author to whom correspondence should be addressed.
Biomimetics 2020, 5(1), 10; https://doi.org/10.3390/biomimetics5010010
Received: 22 October 2019 / Revised: 17 February 2020 / Accepted: 26 February 2020 / Published: 9 March 2020
(This article belongs to the Special Issue Fluid Dynamic Interactions in Biological and Bioinspired Propulsion)
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. View Full-Text
Keywords: bayesian experimental design; optimal sensor placement; schooling; self-propelled swimmers; lateral line bayesian experimental design; optimal sensor placement; schooling; self-propelled swimmers; lateral line
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MDPI and ACS Style

Weber, P.; Arampatzis, G.; Novati, G.; Verma, S.; Papadimitriou, C.; Koumoutsakos, P. Optimal Flow Sensing for Schooling Swimmers. Biomimetics 2020, 5, 10.

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