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 (Caranx hippos) 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.

由于天然水生物种的柔性体与其周围的水之间的强烈相互作用，了解仿生游泳的控制机制一直具有挑战性。高级模态分解技术为我们提供了更深入地了解这些复杂动力系统的工具。在本文中，我们采用适当的正交分解（POD）和动态模式分解（DMD）技术来提取Crevalle Jack（ Caranx hippos）复杂运动学的最强时空正交分量。） 鱼。然后，我们提出了一个用于处理流固耦合相关问题的计算框架，以研究它们对高度非线性系统的整体动力学的贡献。我们发现这条鱼的起伏运动只能用两种类似驻波的空间正交模式来描述。根据我们对杰克鱼尾鳍尾鳍膜流动的数值模拟构建数据集，我们的模态分析表明，只有前几个模式从流体和结构中接收能量，但结构在其余模式中的贡献是最小。对于这里考虑的粘性和过渡流动条件，空间和时间正交模式都显示出惊人相似的相干流动结构。