Literature DB >> 25079867

A fish perspective: detecting flow features while moving using an artificial lateral line in steady and unsteady flow.

L D Chambers1, O Akanyeti2, R Venturelli3, J Ježov4, J Brown5, M Kruusmaa4, P Fiorini3, W M Megill6.   

Abstract

For underwater vehicles to successfully detect and navigate turbulent flows, sensing the fluid interactions that occur is required. Fish possess a unique sensory organ called the lateral line. Sensory units called neuromasts are distributed over their body, and provide fish with flow-related information. In this study, a three-dimensional fish-shaped head, instrumented with pressure sensors, was used to investigate the pressure signals for relevant hydrodynamic stimuli to an artificial lateral line system. Unsteady wakes were sensed with the objective to detect the edges of the hydrodynamic trail and then explore and characterize the periodicity of the vorticity. The investigated wakes (Kármán vortex streets) were formed behind a range of cylinder diameter sizes (2.5, 4.5 and 10 cm) and flow velocities (9.9, 19.6 and 26.1 cm s(-1)). Results highlight that moving in the flow is advantageous to characterize the flow environment when compared with static analysis. The pressure difference from foremost to side sensors in the frontal plane provides us a useful measure of transition from steady to unsteady flow. The vortex shedding frequency (VSF) and its magnitude can be used to differentiate the source size and flow speed. Moreover, the distribution of the sensing array vertically as well as the laterally allows the Kármán vortex paired vortices to be detected in the pressure signal as twice the VSF.
© 2014 The Author(s) Published by the Royal Society. All rights reserved.

Keywords:  Kármán vortex street; aquatic navigation; artificial lateral line; pressure sensing; three dimensional

Mesh:

Year:  2014        PMID: 25079867      PMCID: PMC4233726          DOI: 10.1098/rsif.2014.0467

Source DB:  PubMed          Journal:  J R Soc Interface        ISSN: 1742-5662            Impact factor:   4.118


  22 in total

1.  Hydrodynamic pressure sensing with an artificial lateral line in steady and unsteady flows.

Authors:  Roberto Venturelli; Otar Akanyeti; Francesco Visentin; Jaas Ježov; Lily D Chambers; Gert Toming; Jennifer Brown; Maarja Kruusmaa; William M Megill; Paolo Fiorini
Journal:  Bioinspir Biomim       Date:  2012-04-12       Impact factor: 2.956

2.  Biorobotic insights into how animals swim.

Authors:  Promode R Bandyopadhyay; David N Beal; Alberico Menozzi
Journal:  J Exp Biol       Date:  2008-01       Impact factor: 3.312

3.  What information do Kármán streets offer to flow sensing?

Authors:  Otar Akanyeti; Roberto Venturelli; Francesco Visentin; Lily Chambers; William M Megill; Paolo Fiorini
Journal:  Bioinspir Biomim       Date:  2011-06-13       Impact factor: 2.956

4.  Fractional rate of change of swim-bladder volume is reliably related to absolute depth during vertical displacements in teleost fish.

Authors:  Graham K Taylor; Robert Iain Holbrook; Theresa Burt de Perera
Journal:  J R Soc Interface       Date:  2010-02-26       Impact factor: 4.118

5.  Three-dimensional spatial cognition: information in the vertical dimension overrides information from the horizontal.

Authors:  Robert I Holbrook; Theresa Burt de Perera
Journal:  Anim Cogn       Date:  2011-03-31       Impact factor: 3.084

6.  Self-motion effects on hydrodynamic pressure sensing: part I. forward-backward motion.

Authors:  Otar Akanyeti; Lily D Chambers; Jaas Ježov; Jennifer Brown; Roberto Venturelli; Maarja Kruusmaa; William M Megill; Paolo Fiorini
Journal:  Bioinspir Biomim       Date:  2013-03-06       Impact factor: 2.956

7.  Pressure distribution on the body surface of swimming fish.

Authors:  A B Dubois; G A Cavagna; R S Fox
Journal:  J Exp Biol       Date:  1974-06       Impact factor: 3.312

8.  Mechanical control of swimming speed: stiffness and axial wave form in undulating fish models

Authors:  M J McHenry; C A Pell; J H Long
Journal:  J Exp Biol       Date:  1995       Impact factor: 3.312

9.  Determination of object position, vortex shedding frequency and flow velocity using artificial lateral line canals.

Authors:  Adrian Klein; Horst Bleckmann
Journal:  Beilstein J Nanotechnol       Date:  2011-06-06       Impact factor: 3.649

10.  Kármán vortex street detection by the lateral line.

Authors:  Boris P Chagnaud; Horst Bleckmann; Michael H Hofmann
Journal:  J Comp Physiol A Neuroethol Sens Neural Behav Physiol       Date:  2007-05-15       Impact factor: 2.389

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  8 in total

1.  Artificial fish skin of self-powered micro-electromechanical systems hair cells for sensing hydrodynamic flow phenomena.

Authors:  Mohsen Asadnia; Ajay Giri Prakash Kottapalli; Jianmin Miao; Majid Ebrahimi Warkiani; Michael S Triantafyllou
Journal:  J R Soc Interface       Date:  2015-10-06       Impact factor: 4.118

2.  Form and function of the teleost lateral line revealed using three-dimensional imaging and computational fluid dynamics.

Authors:  Hendrik Herzog; Birgit Klein; Alexander Ziegler
Journal:  J R Soc Interface       Date:  2017-05       Impact factor: 4.118

3.  Head width influences flow sensing by the lateral line canal system in fishes.

Authors:  Yuzo R Yanagitsuru; Otar Akanyeti; James C Liao
Journal:  J Exp Biol       Date:  2018-10-29       Impact factor: 3.312

Review 4.  Behavior, Electrophysiology, and Robotics Experiments to Study Lateral Line Sensing in Fishes.

Authors:  Melanie Haehnel-Taguchi; Otar Akanyeti; James C Liao
Journal:  Integr Comp Biol       Date:  2018-11-01       Impact factor: 3.326

Review 5.  A Review of Artificial Lateral Line in Sensor Fabrication and Bionic Applications for Robot Fish.

Authors:  Guijie Liu; Anyi Wang; Xinbao Wang; Peng Liu
Journal:  Appl Bionics Biomech       Date:  2016-12-27       Impact factor: 1.781

6.  Research on Flow Field Perception Based on Artificial Lateral Line Sensor System.

Authors:  Guijie Liu; Mengmeng Wang; Anyi Wang; Shirui Wang; Tingting Yang; Reza Malekian; Zhixiong Li
Journal:  Sensors (Basel)       Date:  2018-03-11       Impact factor: 3.576

7.  Hydrodynamic object identification with artificial neural models.

Authors:  Sreetej Lakkam; B T Balamurali; Roland Bouffanais
Journal:  Sci Rep       Date:  2019-08-02       Impact factor: 4.379

8.  Three-dimensional multi-source localization of underwater objects using convolutional neural networks for artificial lateral lines.

Authors:  Ben J Wolf; Jos van de Wolfshaar; Sietse M van Netten
Journal:  J R Soc Interface       Date:  2020-01-22       Impact factor: 4.118

  8 in total

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