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      Design and application of a fish-shaped lateral line probe for flow measurement.

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          Abstract

          We introduce the lateral line probe (LLP) as a measurement device for natural flows. Hydraulic surveys in rivers and hydraulic structures are currently based on time-averaged velocity measurements using propellers or acoustic Doppler devices. The long-term goal is thus to develop a sensor system, which includes spatial gradients of the flow field along a fish-shaped sensor body. Interpreting the biological relevance of a collection of point velocity measurements is complicated by the fact that fish and other aquatic vertebrates experience the flow field through highly dynamic fluid-body interactions. To collect body-centric flow data, a bioinspired fish-shaped probe is equipped with a lateral line pressure sensing array, which can be applied both in the laboratory and in the field. Our objective is to introduce a new type of measurement device for body-centric data and compare its output to estimates of conventional point-based technologies. We first provide the calibration workflow for laboratory investigations. We then provide a review of two velocity estimation workflows, independent of calibration. Such workflows are required as existing field investigations consist of measurements in environments where calibration is not feasible. The mean difference for uncalibrated LLP velocity estimates from 0 to 50 cm/s under in a closed flow tunnel and open channel flume was within 4 cm/s when compared to conventional measurement techniques. Finally, spatial flow maps in a scale vertical slot fishway are compared for the LLP, direct measurements, and 3D numerical models where it was found that the LLP provided a slight overestimation of the current velocity in the jet and underestimated the velocity in the recirculation zone.

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          Most cited references22

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          The functioning and significance of the lateral-line organs.

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            Distant touch hydrodynamic imaging with an artificial lateral line.

            Nearly all underwater vehicles and surface ships today use sonar and vision for imaging and navigation. However, sonar and vision systems face various limitations, e.g., sonar blind zones, dark or murky environments, etc. Evolved over millions of years, fish use the lateral line, a distributed linear array of flow sensing organs, for underwater hydrodynamic imaging and information extraction. We demonstrate here a proof-of-concept artificial lateral line system. It enables a distant touch hydrodynamic imaging capability to critically augment sonar and vision systems. We show that the artificial lateral line can successfully perform dipole source localization and hydrodynamic wake detection. The development of the artificial lateral line is aimed at fundamentally enhancing human ability to detect, navigate, and survive in the underwater environment.
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              THE IPOS FRAMEWORK: LINKING FISH SWIMMING PERFORMANCE IN ALTERED FLOWS FROM LABORATORY EXPERIMENTS TO RIVERS

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                Author and article information

                Journal
                Rev Sci Instrum
                The Review of scientific instruments
                AIP Publishing
                1089-7623
                0034-6748
                Apr 2016
                : 87
                : 4
                Affiliations
                [1 ] SJE Ecohydraulic Engineering GmbH, Viereichenweg 12, Stuttgart 70569, Germany.
                [2 ] Centre for Biorobotics, Tallinn University of Technology, Akadeemia tee 15A-111, Tallinn 12618, Estonia.
                [3 ] Department of Signal Processing, Tampere University of Technology, P.O. Box 553, Tampere FI-33101, Finland.
                [4 ] Institute of Water and River Basin Management, Karlsruhe Institute of Technology, Kaiserstraße 12, Karlsruhe 76131, Germany.
                [5 ] Institute for Modelling Hydraulic and Environmental Systems, University of Stuttgart, Pfaffenwaldring 61, Stuttgart 70569, Germany.
                Article
                10.1063/1.4946765
                27131710
                6a39bfd1-56f0-4943-8218-bcf2a6aa1893
                History

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