Unsteady flows contain information about the objects creating them. Aquatic organisms offer intriguing paradigms for extracting flow information using local sensory measurements. In contrast, classical methods for flow analysis require global knowledge of the flow field. Here, we train neural networks to classify flow patterns using local vorticity measurements. Specifically, we consider vortex wakes behind an oscillating airfoil and we evaluate the accuracy of the network in distinguishing between three wake types, 2S, 2P + 2S and 2P + 4S. The network uncovers the salient features of each wake type.
We consider the inverse problem of classifying flow patterns from local sensory measurements. This problem is inspired by the ability of various aquatic organisms to respond to ambient flow signals, and is relevant for translating these abilities to underwater robotic vehicles. In Colvert, Alsalman and Kanso, B&B (2018), we trained neural networks to classify vortical flows by relying on a single flow sensor that measures a 'time history' of the local vorticity. Here, we systematically investigate the effects of distinct types of sensors on the accuracy of flow classification. We consider four types of sensors-vorticity, flow velocities parallel and transverse to the direction of flow propagation, and flow speed-and show that the networks trained using transverse velocity outperform other networks, even when subjected to aggressive data corruption. We then train the network to classify flow patterns instantaneously, using a spatially-distributed array of sensors and a single 'one time' sensory measurement. The network, based on a handful of spatially-distributed sensors, exhibits remarkable accuracy in flow classification. These results lay the groundwork for developing learning algorithms for the dynamic deployment of sensory arrays in unsteady flows.
Fish rheotaxis, or alignment into flow currents, results from intertwined sensory, neural and actuation mechanisms, all coupled with hydrodynamics to produce a behaviour that is critical for upstream migration and position holding in oncoming flows. Among several sensory modalities, the lateral-line sensory system is thought to play a major role in the fish ability to sense minute water motions in their vicinity and, thus, in their rheotactic behaviour. Here, we propose a theoretical model consisting of a fishlike body equipped with lateral pressure sensors in oncoming uniform flows. We compute the optimal sensor locations that maximize the sensory output. Our results confirm recent experimental findings that correlate the layout of the lateral-line sensors with the distribution of hydrodynamic information at the fish surface. We then examine the behavioural response of the fishlike model as a function of its orientation and swimming speed relative to the background flow. Our working hypothesis is that fish respond to sensory information by adjusting their orientation according to the perceived difference in pressure. We find that, as in fish rheotaxis, the fishlike body responds by aligning into the oncoming flow. These findings may have significant implications on understanding the interplay between the sensory output and fish behaviour.
Most marine creatures exhibit remarkable flow sensing abilities. Their task of discerning hydrodynamic cues from local sensory information is particularly challenging because it relies on local and partial measurements to accurately characterize the ambient flow. This is in contrast to classical flow characterization methods, which invariably depend on the ability of an external observer to reconstruct the flow field globally and identify its topological structures. In this paper, we develop a mathematical framework in which a local sensory array is used to identify select flow features. Our approach consists of linearizing the flow field around the sensory array and providing a frame-independent parameterization of the velocity gradient tensor which reveals both the local flow ‘type’ and ‘intensity’. We show that a simple bioinspired sensory system that measures differences in flow velocities is capable of locally characterizing the flow type and intensity. We discuss the conditions under which such flow characterization is possible. Then, to demonstrate the effectiveness of this sensory system, we apply it in the canonical problem of a circular cylinder in uniform flow. We find excellent agreement between the sensed and actual flow properties. These findings will serve to direct future research on optimal sensory layouts and dynamic deployment of sensory arrays.
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