Flowtaxis in the wakes of oscillating airfoils

Abstract

Many aquatic organisms from copepods to harbor seals are able to detect and respond to flow disturbances. The physiological mechanisms underlying such behavior remain a challenge for current and future research. Here, we propose a simplified flow sensing scenario in which a mobile sensor reorients its heading in response to local flow stimuli, with the goal of tracing the wakes created by oscillating airfoils to their source. Specifically, we engineer a feedback control strategy where the sensory measurements are based on transverse vorticity gradients. Through numerical experiments, we assess the efficacy of the sensor in following topologically distinct wakes. We demonstrate that the strategy is robust to variations in the wake itself, and we arrive at empirical rules that the sensor's initial position and orientation must satisfy in order to successfully locate the airfoil. We conclude by commenting on the relevance of the model and results to animal behavior and bio-inspired underwater robotics. We also discuss current and future opportunities for employing machine learning tools to devise and improve these sensory control strategies.

Interpretable and Generalizable Strategies for Stably Following Hydrodynamic Trails

“…The principles that allow these mechano-receptors to detect flows [11,12] have direct relevance to designing bio-inspired sensors [13][14][15][16][17]. However, how animals use and respond to detected flow signals for underwater navigation is far less understood [18,19]. Experimental evidence in fish [1,20,21], harbor seals [7,8], and copepods [22,23] suggests that aquatic animals use information contained in flows generated by predators, prey, conspecifics, and even abiotic sources, for underwater navigation not only over short distances, as in the final stage of mate or prey localization [1,9,10], but also to locate distant objects by following hydrodynamic trails over considerable length and time spans [20,24].…”

“…In this scenario, the Reynolds number, defined as the ratio of inertial to viscous forces, is moderate and both inertial and viscous effects are at play. This flow regime, though encompassing the wakes of nearly all intermediate sized organisms and robots, is far less explored [18, 19].…”

“…Hydrodynamic trails, whether generated by a swimming fish or a stubborn rock, manifest clear patterns of alternating-signed swirls or vortices that contain information about the sources generating them [25][26][27]. Much effort has been dedicated to cataloging wake types [28,29], mostly to uncover the thrust and drag forces exerted on the bodies generating the wakes [30,31], but far less is known about the response strategies that allow a follower to track a hydrodynamic trail to its generating source by relying solely on local flow sensing [18,24,32]. Such understanding, besides its immediate relevance to naval and robotic applications for ocean monitoring and marine life surveillance and maintenance [33][34][35][36][37], can be applied more broadly in the context of navigating changeable environments [19,32], and could guide future explorations of the neuronal mechanisms underlying trail following in aquatic animals [1,7,20,38,39].…”

“…This problem falls under the broader context of locally sensing a signal field and inferring unknown information about the field and its source [25,26,[40][41][42][43], including steering the sensor to locate the source [18,32,[44][45][46][47] or other target points [19,48]. When the signal field is transported by laminar and diffusive processes, gradient based methods perform admirably in locating the source [47,49,50] and identifying optimal sensor placements [27,43,51].…”

“…Two notable exceptions emerged recently for navigation at intermediate Reynolds numbers featuring a handcrafted policy [18] and a reinforcement learning (RL) approach [19]. RL is an optimization framework that, unlike classic trajectory optimization, seeks to learn feedback mechanisms based on experience [54][55][56][57][58].…”