Data-Driven Hydrodynamic Force Estimation of Clapping Propulsion With Distributed Sensing

Sea lions use unique clapping propulsion for highly efficient and exceptional agile motion. A deep understanding of how hydrodynamic forces were generated over their foreflippers is the key to revealing the working mechanism of clapping propulsion. However, there have not been any effective and efficient methods to measure the complex interactions between solid flippers and the surrounding fluid. This article proposes a data-driven hydrodynamic force estimation method using measurement from sparsely distributed pressure sensors. This method can quickly and accurately predict hydrodynamic forces without requiring the complex computation of coupled fluid-solid interactions. First, a four-channeled data structure, consisting of the pressure measurement and position/orientation of individual sensors, is created to model the state of a biomimetic flipper. Second, five data-driven convolutional neural networks (CNNs) with different structures are designed to model the relationship between the propulsion force and pressured measurements from the sparsely distributed sensors. Finally, simulations are carried out to verify the effectiveness of the proposed hydrodynamic force estimation method. The results show that a small number of sensors can estimate propulsion force with good accuracy and speed. The prediction performances of five neural networks with different architectures are compared. Increasing the depth of the model is conducive to improving the prediction accuracy, but continuing to increase the depth will produce overfitting. Inception’s strategy of using multisized convolution kernels outperforms others, providing a higher accuracy while requiring a lighter network architecture.