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The boundary layer instability of a gliding fish helps rather than prevents object identification

Published online by Cambridge University Press:  19 September 2014

Audrey P. Maertens  and
Michael S. Triantafyllou
Audrey P. Maertens*
Affiliation:
Center for Ocean Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA
Michael S. Triantafyllou
Affiliation:
Center for Ocean Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA
*
†Email address for correspondence: [email protected]
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Abstract

Inspired by the function of the lateral line in aquatic animals, we study the shape identification of a stationary cylinder through pressure measurements made by sensors located on the surface of a steadily moving foil, modelling a fish gliding in close proximity to an object. Comparing experimental results, potential flow predictions and viscous simulations, we first show that the pressure in the boundary layer of the foil is significantly affected by unsteady viscous effects, especially in the posterior half of the foil. Therefore, even after the effects of the boundary layer thickness are accounted for, potential flow predictions are inaccurate. Subsequently, we show that the spatial features of the unsteady patterns developing when the foil is moving near a cylinder can be predicted accurately through linear stability analysis of the average boundary layer velocity profile under open water conditions. Because these unsteady patterns result from amplification of the potential flow-like disturbance caused in the front part of the foil, they are specific to the cylinder that generated them and could be used to identify its shape. We develop and demonstrate a methodology to calculate the unsteady pressure based on combining potential flow predictions with results from linear stability analysis of the boundary layer. The findings can be useful for object identification in underwater vehicles, and support the intriguing possibility that the significant viscous effects caused by nearby bodies on the fish boundary layer, far from preventing detection, could actually be used by animals to identify objects.

JFM classification

Instability: Absolute/convective instability Biological Fluid Dynamics: Biological Fluid Dynamics Boundary Layers: Boundary Layers
Type
Papers
Information
Journal of Fluid Mechanics , Volume 757 , 25 October 2014 , pp. 179 - 207
Copyright
© 2014 Cambridge University Press 

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References

Bers, A. 1983 Basic plasma physics I. In Handbook of Plasma Physics (ed. Rosenbluth, M. N. & Sagdeev, R. Z.), North-Holland Publishing Company.Google Scholar
von Campenhausen, C., Riess, I. & Weissert, R. 1981 Detection of stationary objects by the blind cave fish Anoptichthys jordani (Characidae) . J. Compar. Physiol. A 143 (3), 369–374.CrossRefGoogle Scholar
Castonguay, P., Liang, C. & Jameson, A. 2010 Simulation of transitional flow over airfoils using the spectral difference method. In 40th AIAA Fluid Dynamics Conference, Chicago, IL, American Institute of Aeronautics and Astronautics.Google Scholar
Chomaz, J.-M. 2005 Global instabilities in spatially developing flows: non-normality and nonlinearity. Annu. Rev. Fluid Mech. 37, 357–392.Google Scholar
Chu, W. S., Lee, K. T., Song, S. H., Han, M. W., Lee, J. Y., Kim, H. S., Kim, M. S., Park, Y. J., Cho, K. J. & Ahn, S. H. 2012 Review of biomimetic underwater robots using smart actuators. Intl J. Precis. Engng Manuf. 13 (7), 1281–1292.CrossRefGoogle Scholar
Consi, T. R., Atema, J., Goudey, C. A., Cho, J. & Chryssostomidis, C. 1994 AUV guidance with chemical signals. In Proceedings of the 1994 Symposium on Autonomous Underwater Vehicle Technology (AUV’94), pp. 450–455. IEEE.Google Scholar
Coombs, S., Bleckmann, H., Fay, R. & Popper, A. N. 2014 The Lateral Line System. Springer.Google Scholar
Coombs, S. & Braun, C. B. 2003 Information processing by the lateral line system. In Sensory Processing in Aquatic Environments, 1st edn (ed. Collin, S. P. & Marshall, N. J.), pp. 122–138. Springer.CrossRefGoogle Scholar
Coombs, S. & Montgomery, J. C. 1999 The enigmatic lateral line system. In Comparative Hearing: Fish and Amphibians (ed. Fay, R. & Popper, A. N.), pp. 319–362. Springer.CrossRefGoogle Scholar
Ćurčić-Blake, B. & van Netten, S. M. 2006 Source location encoding in the fish lateral line canal. J. Expl Biol. 209 (8), 1548–1559.Google Scholar
Engelmann, J., Hanke, W. & Bleckmann, H. 2002 Lateral line reception in still-and running water. J. Compar. Physiol. A 188 (7), 513–526.Google Scholar
Fan, J. & Zhang, W. 1999 Statistical estimation in varying coefficient models. Ann. Stat. 27 (5), 1491–1518.Google Scholar
Fernandez, V. I., Maertens, A., Yaul, F., Dahl, J., Lang, J. & Triantafyllou, M. 2011 Lateral-line-inspired sensor arrays for navigation and object identification. Mar. Technol. Soc. J. 45 (4), 130–146.Google Scholar
Goulet, J., Engelmann, J., Chagnaud, B. P., Franosch, J. M., Suttner, M. D. & van Hemmen, J. L. 2007 Object localization through the lateral line system of fish: theory and experiment. J. Compar. Physiol. A 194 (1), 1–17.Google ScholarPubMed
Hassan, E. S. 1985 Mathematical analysis of the stimulus for the lateral line organ. Biol. Cybern. 52 (1), 23–36.Google Scholar
Hassan, E. S. 1986 On the discrimination of spatial intervals by the blind cave fish (Anoptichthys jordani). J. Compar. Physiol. A 159 (5), 701–710.CrossRefGoogle ScholarPubMed
Hastie, T. & Tibshirani, R. 1993 Varying-coefficient models. J. R. Stat. Soc. B 55 (4), 757–796.Google Scholar
Huerre, P. & Monkewitz, P. A. 1990 Local and global instabilities in spatially developing flows. Annu. Rev. Fluid Mech. 22 (1), 473–537.Google Scholar
Liao, J. C., Beal, D. N., Lauder, G. V. & Triantafyllou, M. S. 2003 Fish exploiting vortices decrease muscle activity. Science 302 (5650), 1566–1569.Google Scholar
MacIver, M. A., Fontaine, E. & Burdick, J. W. 2004 Designing future underwater vehicles: principles and mechanisms of the weakly electric fish. IEEE J. Ocean. Engng 29 (3), 651–659.Google Scholar
Maertens, A. P. & Weymouth, G. D. 2014 Accurate Cartesian-grid simulations of near-body flows at intermediate Reynolds numbers. Comput. Meth. Appl. Mech. Engng (in press).Google Scholar
Marquillie, M. & Ehrenstein, U. 2003 On the onset of nonlinear oscillations in a separating boundary-layer flow. J. Fluid Mech. 490, 169–188.Google Scholar
McHenry, M. J. & Liao, J. C. 2014 The hydrodynamics of flow stimuli. In The Lateral Line System, pp. 73–98. Springer.Google Scholar
McHenry, M. J., Strother, J. A. & van Netten, S. M. 2008 Mechanical filtering by the boundary layer and fluid–structure interaction in the superficial neuromast of the fish lateral line system. J. Compar. Physiol. A 194 (9), 795–810.Google Scholar
Montgomery, J. C., Coombs, S. & Baker, C. F. 2001 The mechanosensory lateral line system of the hypogean form of Astyanax fasciatus . Environ. Biol. Fishes 62 (1), 87–96.Google Scholar
van Netten, S. M. 2006 Hydrodynamic detection by cupulae in a lateral line canal: functional relations between physics and physiology. Biol. Cybern. 94 (1), 67–85.Google Scholar
Oertel, H. 1990 Wakes behind blunt bodies. Annu. Rev. Fluid Mech. 22 (1), 539–562.Google Scholar
Orszag, S. A. 1971 Accurate solution of the Orr–Sommerfeld stability equation. J. Fluid Mech. 50 (4), 689–703.Google Scholar
Rapo, M. A., Jiang, H., Grosenbaugh, M. A. & Coombs, S. 2009 Using computational fluid dynamics to calculate the stimulus to the lateral line of a fish in still water. J. Expl Biol. 212 (10), 1494–1505.Google Scholar
Reed, H. L., Saric, W. S. & Arnal, D. 1996 Linear stability theory applied to boundary layers. Annu. Rev. Fluid Mech. 28 (1), 389–428.CrossRefGoogle Scholar
Salumäe, T. & Kruusmaa, M. 2013 Flow-relative control of an underwater robot. Proc. R. Soc. A 469 (2153), 20120671.Google Scholar
Teyke, T. 1988 Flow field, swimming velocity and boundary layer: parameters which affect the stimulus for the lateral line organ in blind fish. J. Compar. Physiol. A 163 (1), 53–61.Google Scholar
Theofilis, V. 2011 Global linear instability. Annu. Rev. Fluid Mech. 43, 319–352.CrossRefGoogle Scholar
Triantafyllou, G. S., Triantafyllou, M. S. & Chryssostomidis, C. 1986 On the formation of vortex streets behind stationary cylinders. J. Fluid Mech. 170, 461–477.Google Scholar
Uranga, A., Persson, P.-O., Drela, M. & Peraire, J. 2011 Implicit large eddy simulation of transition to turbulence at low Reynolds numbers using a discontinuous Galerkin method. Intl J. Numer. Meth. Engng 87 (1–5), 232–261.Google Scholar
Webb, J. F. 2000 Mechanosensory lateral line: functional morphology and neuroanatomy. In Handbook of Experimental Animals: The Laboratory Fish, pp. 236–244. Academic Press.Google Scholar
Weideman, J. A. & Reddy, S. C. 2000 A MATLAB differentiation matrix suite. ACM Trans. Math. Softw. 26 (4), 465–519.Google Scholar
Weymouth, G. D., Dommermuth, D. G., Hendrickson, K. & Yue, D. K.-P. 2006 Advancements in Cartesian-grid methods for computational ship hydrodynamics. In 26th Symposium on Naval Hydrodynamics, Rome, Italy, 17–22 September 2006, Office of Naval Research.Google Scholar
Weymouth, G. D. & Triantafyllou, M. S. 2013 Ultra-fast escape of a deformable jet-propelled body. J. Fluid Mech. 721, 367–385.Google Scholar
Weymouth, G. D. & Yue, D. K.-P. 2011 Boundary data immersion method for Cartesian-grid simulations of fluid–body interaction problems. J. Comput. Phys. 230 (16), 6233–6247.Google Scholar
Wibawa, M. S., Steele, S. C., Dahl, J. M., Rival, D. E., Weymouth, G. D. & Triantafyllou, M. S. 2012 Global vorticity shedding for a vanishing wing. J. Fluid Mech. 695, 112–134.Google Scholar
Windsor, S. P. & McHenry, M. J. 2009 The influence of viscous hydrodynamics on the fish lateral-line system. Integr. Compar. Biol. 49 (6), 691–701.CrossRefGoogle ScholarPubMed
Windsor, S. P., Norris, S. E., Cameron, S. M., Mallinson, G. D. & Montgomery, J. C. 2010a The flow fields involved in hydrodynamic imaging by blind Mexican cave fish (Astyanax fasciatus). Part I: open water and heading towards a wall. J. Expl Biol. 213 (22), 3819–3831.Google Scholar
Windsor, S. P., Norris, S. E., Cameron, S. M., Mallinson, G. D. & Montgomery, J. C. 2010b The flow fields involved in hydrodynamic imaging by blind Mexican cave fish (Astyanax fasciatus). Part II: gliding parallel to a wall. J. Expl Biol. 213 (22), 3832–3842.Google Scholar
Windsor, S. P., Tan, D. & Montgomery, J. C. 2008 Swimming kinematics and hydrodynamic imaging in the blind Mexican cave fish (Astyanax fasciatus). J. Expl Biol. 211 (18), 2950–2959.Google Scholar
Wu, X., Jacobs, R. G., Hunt, J. C. R. & Durbin, P. A. 1999 Simulation of boundary layer transition induced by periodically passing wakes. J. Fluid Mech. 398, 109–153.Google Scholar