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Local flow characterization using bioinspired sensory information

Published online by Cambridge University Press:  31 March 2017

Brendan Colvert ,
Kevin Chen Open the ORCID record for Kevin Chen [Opens in a new window]  and
Eva Kanso Open the ORCID record for Eva Kanso [Opens in a new window]
Brendan Colvert
Affiliation:
Department of Aerospace and Mechanical Engineering, University of Southern California, Los Angeles, CA 90089, USA
Kevin Chen
Affiliation:
Department of Aerospace and Mechanical Engineering, University of Southern California, Los Angeles, CA 90089, USA
Eva Kanso*
Affiliation:
Department of Aerospace and Mechanical Engineering, University of Southern California, Los Angeles, CA 90089, USA
*
†Email address for correspondence: kanso@usc.edu
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Abstract

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.

JFM classification

Biological Fluid Dynamics: Biological Fluid Dynamics Mathematical Foundations: Mathematical Foundations Biological Fluid Dynamics: Swimming/flying
Type
Papers
Information
Journal of Fluid Mechanics , Volume 818 , 10 May 2017 , pp. 366 - 381
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Copyright
© 2017 Cambridge University Press 

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References

Boxshall, G. A., Yen, J. & Strickler, R. J. 1997 Functional significance of the sexual dimorphism in the cephalic appendages of Euchaeta rimana Bradford. Bull. Mar. Sci. 61, 387–398.Google Scholar
Candes, E. J. 2008 The restricted isometry property and its implications for compressed sensing. C. R. Mathematique 346 (9), 589–592.Google Scholar
Chen, K. K., Tu, J. H. & Rowley, C. W. 2012 Variants of dynamic mode decomposition: boundary condition, Koopman, and Fourier analyses. J. Nonlinear Sci. 22, 887–915.CrossRefGoogle Scholar
Colvert, B. & Kanso, E. 2016 Fishlike rheotaxis. J. Fluid Mech. 793, 656–666.CrossRefGoogle Scholar
Coombs, S. & Van Netten, S. 2005 The hydrodynamics and structural mechanics of the lateral line system. Fish Biomechanics, Fish Physiology. vol. 23, pp. 103–139. Academic.CrossRefGoogle Scholar
Dehnhardt, G., Mauck, B., Hanke, W. & Bleckmann, H. 2001 Hydrodynamic trail-following in harbor seals (Phoca vitulina). Science 293, 102–104.Google Scholar
Engelmann, J., Hanke, W., Mogdans, J. & Bleckmann, H. 2000 Neurobiology: hydrodynamic stimuli and the fish lateral line. Nature 408 (6808), 51–52.Google Scholar
Fernandez, V. I. 2011 Performance analysis for lateral-line-inspired sensor arrays. PhD thesis, Massachusetts Institute of Technology.Google Scholar
Gazzola, M., Tchieu, A. A., Alexeev, D., de Brauer, A. & Koumoutsakos, P. 2016 Learning to school in the presence of hydrodynamic interactions. J. Fluid Mech. 789, 726–749.CrossRefGoogle Scholar
Golub, G. H. & Van Loan, C. F. 1996 Matrix Computations, 3rd edn. Johns Hopkins University Press.Google Scholar
Haller, G. 2005 An objective definition of a vortex. J. Fluid Mech. 525, 1–26.Google Scholar
Hunt, J. C. R., Wray, A. A. & Moin, P. 1988 Eddies, streams, and convergence zones in turbulent flows. In Proceedings of the Summer Program 1988, pp. 193–208. Center for Turbulence Research.Google Scholar
Jeong, J. & Hussain, F. 1995 On the identification of a vortex. J. Fluid Mech. 285, 69–94.Google Scholar
Kroese, A. B. & Schellart, N. A. 1992 Velocity- and acceleration-sensitive units in the trunk lateral line of the trout. J. Neurophys. 68 (6), 2212–2221.CrossRefGoogle ScholarPubMed
Lagnado, R. R. 1985 The stability of two-dimensional linear flows. PhD thesis, California Institute of Technology.Google Scholar
Marrucci, G. & Astarita, G. 1967 Linear, steady, two-dimensional flows of viscoelastic liquids. AIChE J. 13, 931–935.Google Scholar
Montgomery, J. C., Baker, C. F. & Carton, A. G. 1997 The lateral line can mediate rheotaxis in fish. Nature 389 (6654), 960–963.Google Scholar
Ristroph, L., Liao, J. C. & Zhang, J. 2015 Lateral line layout correlates with the differential hydrodynamic pressure on swimming fish. Phys. Rev. Lett. 114, 018102.Google Scholar
Schnipper, T., Andersen, A. & Bohr, T. 2009 Vortex wakes of a flapping foil. J. Fluid Mech. 633, 411–423.CrossRefGoogle Scholar
Venturelli, R., Akanyeti, O., Visentin, F., Ježov, J., Chambers, L. D., Toming, G., Brown, J., Kruusmaa, M., Megill, W. M. & Fiorini, P. 2012 Hydrodynamic pressure sensing with an artificial lateral line in steady and unsteady flows. Bioinspir. Biomim. 7, 036004.Google Scholar
Vergassola, M., Villermaux, E. & Shraiman, B. I. 2007 ‘Infotaxis’ as a strategy for searching without gradients. Nature 445 (7126), 406–409.Google Scholar
Williamson, C. H. K. & Roshko, A. 1988 Vortex formation in the wake of an oscillating cylinder. J. Fluids Struct. 2, 355–381.CrossRefGoogle Scholar
Yen, J. & Nicoll, N. T. 1990 Setal array on the first antennae of a carnivorous marine copepod, Euchaeta norvegica. J. Crustac. Biol. 10, 218–224.CrossRefGoogle Scholar
Yen, J. & Strickler, J. R. 1996 Advertisement and concealment in the plankton: What makes a copepod hydrodynamically conspicuous? Invertebrate Biology 115 (3), 191–205.CrossRefGoogle Scholar
Yen, J., Weissburg, M. J. & Doall, M. H. 1998 The fluid physics of signal perception by mate-tracking copepods. Phil. Trans. R. Soc. Lond. B 353, 787–804.Google Scholar