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运动学模态分解和箭鱼的流体-尾鳍相互作用。

Modal decompositions of the kinematics of Crevalle jack and the fluid-caudal fin interaction.

机构信息

Institute of Ocean Research, Peking University, Beijing, People's Republic of China.

Key State Laboratory of Turbulence and Complex Systems, Department of Mechanics and Engineering Science, Peking University, Beijing, People's Republic of China.

出版信息

Bioinspir Biomim. 2020 Dec 9;16(1). doi: 10.1088/1748-3190/abc294.

Abstract

To understand the governing mechanisms of bio-inspired swimming has always been challenging due to intense interactions between flexible bodies of natural aquatic species and water around them. Advanced modal decomposition techniques provide us with tools to develop more in-depth understating about these complex dynamical systems. In this paper, we employ proper orthogonal decomposition (POD) and dynamic mode decomposition (DMD) techniques to extract energetically strongest spatio-temporal orthonormal components of complex kinematics of a Crevalle jack () fish. Then, we present a computational framework for handling fluid-structure interaction related problems in order to investigate their contributions towards the overall dynamics of highly nonlinear systems. We find that the undulating motion of this fish can be described by only two standing-wave like spatially orthonormal modes. Constructing the data set from our numerical simulations for flows over the membranous caudal fin of the jack fish, our modal analyses reveal that only the first few modes receive energy from both the fluid and structure, but the contribution of the structure in the remaining modes is minimal. For the viscous and transitional flow conditions considered here, both spatially and temporally orthonormal modes show strikingly similar coherent flow structures. Our investigations are expected to assist in developing data-driven reduced-order mathematical models to examine the dynamics of bio-inspired swimming robots and develop new and effective control strategies to bring their performance closer to real fish species.

摘要

由于天然水生生物的柔性体与周围水之间的强烈相互作用,理解仿生游泳的控制机制一直具有挑战性。先进的模态分解技术为我们提供了开发更深入理解这些复杂动力系统的工具。在本文中,我们采用了适当的正交分解(POD)和动态模态分解(DMD)技术,以提取鲷鱼()复杂运动学的能量最强的时空正交分量。然后,我们提出了一种处理流固相互作用相关问题的计算框架,以研究它们对高度非线性系统整体动力学的贡献。我们发现,这种鱼的波动运动可以仅用两个驻波状的空间正交模式来描述。从我们对鲷鱼膜状尾鳍上的流动进行数值模拟的数据集中,我们的模态分析表明,只有前几个模式从流体和结构中获得能量,但结构在其余模式中的贡献最小。对于这里考虑的粘性和过渡流条件,空间和时间正交模式都显示出惊人相似的相干流动结构。我们的研究有望协助开发基于数据的降阶数学模型,以研究仿生游泳机器人的动力学,并开发新的有效控制策略,使它们的性能更接近真实鱼类。

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