Weber Pascal, Arampatzis Georgios, Novati Guido, Verma Siddhartha, Papadimitriou Costas, Koumoutsakos Petros
Computational Science and Engineering Laboratory, ETH Zürich, Clausiusstrasse 33, 8092 Zürich, Switzerland.
Collegium Helveticum, 8092 Zurich, Switzerland.
Biomimetics (Basel). 2020 Mar 9;5(1):10. doi: 10.3390/biomimetics5010010.
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.
鱼群游动意味着游动的鱼能感知同伴的存在。在水流介导的环境中，除了视觉线索外，鱼体上的压力和剪切力传感器对于提供定量信息至关重要，这些信息有助于量化与其他鱼的接近程度。在此，我们研究人工游动体表面传感器的分布，以便它能最佳地识别一群领先的游动体。我们采用贝叶斯实验设计，并结合多个自推进游动体的二维纳维 - 斯托克斯方程的数值模拟。跟随者利用自身表面压力和剪应力的信息跟踪鱼群。我们证明，跟随者的最佳传感器分布在性质上与鱼身上神经丘的分布相似。我们的结果表明，仅使用表面信息就能准确识别领先游动体的质心和数量。
