Inspired by the lateral line systems of various aquatic organisms that are capable of hydrodynamic imaging using ambient flow information, this study develops a deep learning-based object localization model that can detect the location of objects using flow information measured from a moving sensor array. In numerical simulations with the assumption of a potential flow, a two-dimensional hydrofoil navigates around four stationary cylinders in a uniform flow and obtains two types of sensory data during a simulation, namely flow velocity and pressure, from an array of sensors located on the surface of the hydrofoil. Several neural network models are constructed using the flow velocity and pressure data, and these are used to detect the positions of the hydrofoil and surrounding objects. The model based on a long short-term memory network, which is capable of learning order dependence in sequence prediction problems, outperforms the other models. The number of sensors is then optimized using feature selection techniques. This sensor optimization leads to a new object localization model that achieves impressive accuracy in predicting the locations of the hydrofoil and objects with only 40$\%$ of the sensors used in the original model.

Motivated by drag-based propulsion of crinoids, the shape reconfiguration of a feather-like elastic structure under both steady and unsteady translational motions is investigated. The simplified elastic structure consists of a centre rod to which numerous side flaps are attached by elastic hinges. These side flaps fold in only one direction to realize a dramatic reduction in the area of the structure during the recovery stroke. Compared with experimental measurements, analytical methods developed to couple the dynamics of the centre rod and the side flaps successfully predict the drag force and three-dimensional reconfiguration of the elastic structure during both power and recovery strokes. A dimensionless speed given by the ratio of inertial fluid force to elastic bending force is proposed for the coupled deflections of the centre rod and side flaps, and is found to determine primarily the reconfiguration of the elastic structure. A reconfiguration number defined specifically for our model provides an appropriate characterization of the effect of side-flap folding on drag force reduction. Moreover, the ratio of drag forces between the power and recovery strokes is evaluated to find model conditions for the optimal force ratio.