With the overall goal being a better understanding of the sensing environment from the local perspective of a situated agent, we studied uniform flows and Kármán vortex streets in a frame of reference relevant to a fish or swimming robot. We visualized each flow regime with digital particle image velocimetry and then took local measurements using a rigid body with laterally distributed parallel pressure sensor arrays. Time and frequency domain methods were used to characterize hydrodynamically relevant scenarios in steady and unsteady flows for control applications. Here we report that a distributed pressure sensing mechanism has the capability to discriminate Kármán vortex streets from uniform flows, and determine the orientation and position of the platform with respect to the incoming flow and the centre axis of the Kármán vortex street. It also enables the computation of hydrodynamic features which may be relevant for a robot while interacting with the flow, such as vortex shedding frequency, vortex travelling speed and downstream distance between vortices. A Kármán vortex street was distinguished in this study from uniform flows by analysing the magnitude of fluctuations present in the sensor measurements and the number of sensors detecting the same dominant frequency. In the Kármán vortex street the turbulence intensity was 30% higher than that in the uniform flow and the sensors collectively sensed the vortex shedding frequency as the dominant frequency. The position and orientation of the sensor platform were determined via a comparative analysis between laterally distributed sensor arrays; the vortex travelling speed was estimated via a cross-correlation analysis among the sensors.

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Fish passing downstream through hydraulic structures and turbines may be exposed to an elevated risk of injury and mortality. The majority of live fish studies are single-species laboratory investigations and field studies of Kaplan turbines, with a limited number of studies in Francis and screw turbines. In addition to these studies, the physical conditions during turbine passage can be directly measured using passive sensors. In this study, we investigate the multispecies risk of injury and mortality during downstream passage through a large Archimedes hydrodynamic screw for bream (Abramis brama), eel (Anguilla anguilla), and roach (Rutilus rutilus) in conjunction with passive sensors that record the pressure, acceleration, and rate of rotation. This work proposes several new metrics to assess downstream passage including the times and durations of impact events, the kinetic energies of translation and rotation, and the pressure gradient. The major findings of this work are three-fold: (1) Significant differences in injury and mortality were observed between the three investigated species with 37% mortality for bream, 19% for roach, and 3% for eel on average. (2) The operational scenario was found to be significant only for a limited number of species-specific injuries and mortality rates. (3) In contrast to studies in Kaplan turbines, the sensor data revealed highly chaotic physical conditions in the Archimedes hydrodynamic screw, showing little difference in the physical metrics between operational scenarios.

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River system measurement and mapping using UAVs is both lean and agile, with the added advantage of increased safety for the surveying crew. A common parameter of fluvial geomorphological studies is the flow velocity, which is a major driver of sediment behavior. Advances in fluid mechanics now include metrics describing the presence and interaction of coherent structures within a flow field and along its boundaries. These metrics have proven to be useful in studying the complex turbulent flows but require time‐resolved flow field data, which is normally unavailable in geomorphological studies. Contactless UAV‐based velocity measurement provides a new source of velocity field data for measurements of extreme hydrological events at a safe distance, and could allow for measurements of inaccessible areas. Recent works have successfully applied large‐scale particle image velocimetry (LSPIV) using UAVs in rivers, focusing predominantly on surficial flow estimation by tracking intensity differences between georeferenced images. The objective of this work is to introduce a methodology for UAV based real‐time particle tracking in rivers (RAPTOR) in a case study along a short test reach of the Brigach River in the German Black Forest. This methodology allows for large‐scale particle tracking velocimetry (LSPTV) using a combination of floating, infrared light‐emitting particles and a programmable embedded color vision sensor in order to simultaneously detect and track the positions of objects. The main advantage of this approach is its ability to rapidly collect and process the position data, which can be done in real time. The disadvantages are that the method requires the use of specialized light‐emitting particles, which in some cases cannot be retrieved from the investigation area, and that the method returns velocity data in unscaled units of px/s. This work introduces the RAPTOR system with its hardware, data processing workflow, and provides an example of unscaled velocity field estimation using the proposed method. First experiences with the method show that the tracking rate of 50 Hz allows for position estimation with sub‐pixel accuracy, even considering UAV self‐motion. A comparison of the unscaled tracks after Savitzky–Golay filtering shows that although the time‐averaged velocities remain virtually the same, the filter reduces the standard deviation by more than 40% and the maxima by 20%. Copyright © 2017 John Wiley & Sons, Ltd.