Various marine animals possess the ability to track their preys and navigate dark aquatic environments using hydrodynamic sensing of the surrounding flow. In the present study, a deep-learning model is applied to a biomimetic sensor for underwater position detection of a wake-generating body. The sensor is composed of a bundle of spatially-distributed optical fibers that act as artificial seal-like whiskers and interact with the body’s wake in the form of time-variant (bending) deflections. Supervised learning is employed to relate the vibrations of the artificial whiskers to the position of an upstream cylinder. The labeled training data are prepared based on the processing and reduction of the recorded bending responses of the artificial whiskers while the cylinder is placed at various locations. An iterative training algorithm is performed on two neural-network models while using the 10-fold cross-validation technique. The models are able to predict the coordinates of the cylinder in the two-dimensional (2D) space with a high degree of accuracy. The current implementation of the sensor can passively sense the wake generated by the cylinder at Re ≃ 6000 and estimate its position with an average error smaller than the characteristic diameter D of the cylinder and for inter-distances (in the water tunnel) up to 25-times D.

This article investigates the formation of spontaneous coordination in a row of flexible 2D flaps (artificial cilia) in a chamber filled with a high viscous liquid (Re = 0.12). Each flap is driven individually to oscillate by a rotary motor with the root of the flap attached to its spindle axle. A computer-vision control loop tracks the flap tips online and toggles the axle rotation direction when the tips reach a pre-defined maximum excursion. This is a vision-controlled implementation of the so-called "geometric clutch" hypothesis. When running the control loop with the flaps in an inviscid reference situation (air), they remain in their individual phases for a long term. Then, the flaps are studied in the chamber filled with a highly viscous liquid, and the same control loop is started. The flexible flaps now undergo bending due to hydrodynamic coupling and come, after a maximum of 15 beats, into a synchronous metachronal coordination. The study proves in a macroscopic lab experiment that viscous coupling is sufficient to achieve spontaneous synchronization, even for a symmetric cilia shape and beat pattern.

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In this study we present a model that simulates hydrodynamic self-coordination in a row of flexible flaplets. We control the flaplets in order that their tips follow a fixed-amplitude oscillatory motion profile. When brought together at a low Reynolds-number environment, the flaplets interact with each other in the form of bending deflections at their tips, which causes the frequency of the individual oscillations to vary until a coordinated steady state is reached. The model design steps are experimentally verified and the coordination results of both the experiment and the model are compared. The model's internal states are then analysed for a better understanding of the synchronization collective effect. The coordination of the flaplets is found to settle in the direction of propulsion forces ascent. The stability of the resulted synchronization and propulsion forces are examined over long periods. The model is meant to be simplified and mostly linear so that it can be utilized for state forecasting in a real-time control application of a swimmer robot. Finally, we experimentally study the propulsion performance of five beating flaplets that follow prescribed oscillation profiles forming a metachronal wave. The flow results show that the flaplets, that beat in coordination, are efficient at generating a uni-directional steady-streaming transport of the fluid at their surface.

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