Advertisement

Detecting exotic wakes with hydrodynamic sensors

  • Mengying Wang
  • Maziar S. HematiEmail author
Original Article

Abstract

Wake sensing for bioinspired robotic swimmers has been the focus of much investigation owing to its relevance to locomotion control, especially in the context of schooling and target following. Many successful wake sensing strategies have been devised based on models of von Kármán-type wakes; however, such wake sensing technologies are invalid in the context of exotic wake types that commonly arise in swimming locomotion. Indeed, exotic wakes can exhibit markedly different dynamics, and so must be modeled and sensed accordingly. Here, we propose a general wake detection protocol for distinguishing between wake types from measured hydrodynamic signals alone. An ideal-flow model is formulated and used to demonstrate the general wake detection framework in a proof-of-concept study. We show that wakes with different underlying dynamics impart distinct signatures on a fish-like body, which can be observed in time-series measurements at a single location on the body surface. These hydrodynamic wake signatures are used to construct a wake classification library that is then used to classify unknown wakes from hydrodynamic signal measurements. Under ideal settings, the wake detection protocol is found to have an accuracy rate of over 95% in the majority of performance studies conducted. Further, proper tuning can lead to accuracy rates of 80% or better in low signal-to-noise environments. Thus, exotic wake detection is shown to be a viable concept, suggesting that such technologies have the potential to become key enablers of multiple-model sensing and locomotion control strategies in the future.

Keywords

Wake detection Flow classification Bioinspired sensing Machine learning Vertex dynamics Hydrodynamic signals 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

References

  1. 1.
    Akanyeti, O., Thornycroft, P.J.M., Lauder, G.V., Yanagitsuru, Y.R., Peterson, A.N., Liao, J.C.: Fish optimize sensing and respiration during undulatory swimming. Nat. Commun. 7, 11044 (2016).  https://doi.org/10.1038/ncomms11044 CrossRefGoogle Scholar
  2. 2.
    Aref, H., Stremler, M.A., Ponta, F.L.: Exotic vortex wakes–point vortex solutions. J. Fluids Struct. 22(6–7), 929–940 (2006).  https://doi.org/10.1016/j.jfluidstructs.2006.04.015 CrossRefGoogle Scholar
  3. 3.
    Arnold, G.P.: Rheotropism in fishes. Biol. Rev. 49(4), 515–576 (1974).  https://doi.org/10.1111/j.1469-185X.1974.tb01173.x CrossRefGoogle Scholar
  4. 4.
    Basu, S., Stremler, M.A.: On the motion of two point vortex pairs with glide-reflective symmetry in a periodic strip. Phys. Fluids 27(10), 103603 (2015)CrossRefGoogle Scholar
  5. 5.
    Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, Berlin (2006)zbMATHGoogle Scholar
  6. 6.
    Bleckmann, H.: Reception of Hydrodynamic Stimuli in Aquatic and Semiaquatic Animals, Progress in Zoology, vol. 41. Gustav Fischer Verlag, New York (1994)Google Scholar
  7. 7.
    Bleckmann, H., Zelick, R.: Lateral line system of fish. Integr. Zool. 4(1), 13–25 (2009)CrossRefGoogle Scholar
  8. 8.
    Borazjani, I., Sotiropoulos, F.: Numerical investigation of the hydrodynamics of carangiform swimming in the transitional and inertial flow regimes. J. Exp. Biol. 211(10), 1541–1558 (2008)CrossRefGoogle Scholar
  9. 9.
    Bouffanais, R., Weymouth, G.D., Yue, D.K.: Hydrodynamic object recognition using pressure sensing. In: Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, p. rspa20100095. The Royal Society (2010)Google Scholar
  10. 10.
    Chambers, L., Akanyeti, O., Venturelli, R., Ježov, J., Brown, J., Kruusmaa, M., Fiorini, P., Megill, W.: A fish perspective: detecting flow features while moving using an artificial lateral line in steady and unsteady flow. J. R. Soc. Interface 11(99), 20140467 (2014)CrossRefGoogle Scholar
  11. 11.
    Colvert, B., Kanso, E.: Fishlike rheotaxis. J. Fluid Mech. 793, 656–666 (2016).  https://doi.org/10.1017/jfm.2016.141 MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Cottet, G.H., Koumoutsakos, P.D.: Vortex Methods: Theory and Practice. Cambridge University Press, New York (2000)CrossRefzbMATHGoogle Scholar
  13. 13.
    DeVries, L., Lagor, F.D., Lei, H., Tan, X., Paley, D.: Distributed flow estimation and closed-loop control of an underwater vehicle with a multi-modal artificial lateral line. Bioinspiration Biomim. 10(2), 025002 (2015).  https://doi.org/10.1088/1748-3190/10/2/025002 CrossRefGoogle Scholar
  14. 14.
    Franosch, J.M.P., Hagedorn, H.J.A., Goulet, J., Engelmann, J., Van Hemmen, J.L.: Wake tracking and the detection of vortex rings by the canal lateral line of fish. Phys. Rev. Lett. 103(7), 1–4 (2009).  https://doi.org/10.1103/PhysRevLett.103.078102 CrossRefGoogle Scholar
  15. 15.
    Gemmell, B.J., Adhikari, D., Longmire, E.K.: Volumetric quantification of fluid flow reveals fish’s use of hydrodynamic stealth to capture evasive prey. J. R. Soc. Interface 11(90), 20130880 (2014).  https://doi.org/10.1098/rsif.2013.0880 CrossRefGoogle Scholar
  16. 16.
    Hemati, M.S.: Learning wake regimes from snapshot data. In: AIAA Paper 2016-3781. 46th AIAA Fluid Dynamics Conference, AIAA Aviation, Washington, DC (2016)Google Scholar
  17. 17.
    Katz, J., Plotkin, A.: Low-Speed Aerodynamics, 2nd edn. Cambridge University Press, New York (2001)CrossRefzbMATHGoogle Scholar
  18. 18.
    Kern, S., Koumoutsakos, P.: Simulations of optimized anguilliform swimming. J. Exp. Biol. 209(24), 4841–4857 (2006)CrossRefGoogle Scholar
  19. 19.
    Klein, A., Bleckmann, H.: Determination of object position, vortex shedding frequency and flow velocity using artificial lateral line canals. Beilstein J. Nanotechnol. 2(1), 276–283 (2011).  https://doi.org/10.3762/bjnano.2.32 CrossRefGoogle Scholar
  20. 20.
    Lentink, D., Muijres, F.T., Donker-Duyvis, F.J., van Leeuwen, J.L.: Vortex-wake interactions of a flapping foil that models animal swimming and flight. J. Exp. Biol. 211(Pt 2), 267–273 (2008).  https://doi.org/10.1242/jeb.006155 CrossRefGoogle Scholar
  21. 21.
    Liao, J.C., Beal, D.N., Lauder, G.V., Triantafyllou, M.S.: Fish exploiting vortices decrease muscle activity. Science 302, 1566–1569 (2003)CrossRefGoogle Scholar
  22. 22.
    Marras, S., Killen, S.S., Lindström, J., McKenzie, D.J., Steffensen, J.F., Domenici, P.: Fish swimming in schools save energy regardless of their spatial position. Behav. Ecol. Sociobiol. 69, 219–226 (2015)CrossRefGoogle Scholar
  23. 23.
    Montgomery, J.C., Coombs, S., Baker, C.F.: The mechanosensory lateral line system of the hypogean form of astyanax fasciatus. Environ. Biol. Fishes 62(1), 87–96 (2001).  https://doi.org/10.1023/A:1011873111454 Google Scholar
  24. 24.
    Moored, K.W., Dewey, P.A., Smits, A.J., Haj-Hariri, H.: Hydrodynamic wake resonance as an underlying principle of efficient unsteady propulsion. J. Fluid Mech. 708, 329–348 (2012)MathSciNetCrossRefzbMATHGoogle Scholar
  25. 25.
    Müller, U.K., van den Boogaart, J.G.M., van Leeuwen, J.L.: Flow patterns of larval fish: undulatory swimming in the intermediate flow regime. J. Exp. Biol. 211(Pt 2), 196–205 (2008).  https://doi.org/10.1242/jeb.005629 CrossRefGoogle Scholar
  26. 26.
    Newton, P.K.: The \(N\)-Vortex Problem: Analytical Techniques. Springer, New York (2001)CrossRefzbMATHGoogle Scholar
  27. 27.
    Pitcher, T.J., Partridge, B.L., Wardle, C.S.: A blind fish can school. Science (New York, N.Y.) 194(4268), 963–965 (1976).  https://doi.org/10.1126/science.982056 CrossRefGoogle Scholar
  28. 28.
    Pohlmann, K., Grasso, F.W., Breithaupt, T.: Tracking wakes: the nocturnal predatory strategy of piscivorous catfish. Proc. Natl. Acad. Sci. 98(13), 7371–7374 (2001).  https://doi.org/10.1073/pnas.121026298 CrossRefGoogle Scholar
  29. 29.
    Ren, Z., Mohseni, K.: A model of the lateral line of fish for vortex sensing. Bioinspiration Biomim. 7(3), 036016 (2012).  https://doi.org/10.1088/1748-3182/7/3/036016 CrossRefGoogle Scholar
  30. 30.
    Saffman, P.: Vortex Dynamics. Cambridge University Press, New York (1992)zbMATHGoogle Scholar
  31. 31.
    Schnipper, T., Andersen, A., Bohr, T.: Vortex wakes of a flapping foil. J. Fluid Mech. 633, 411–423 (2009)CrossRefzbMATHGoogle Scholar
  32. 32.
    Sichert, A.B., Bamler, R., van Hemmen, J.L.: Hydrodynamic object recognition: when multipoles count. Phys. Rev. Lett. 102(5), 058104 (2009)CrossRefGoogle Scholar
  33. 33.
    Smits, A.J., Moored, K.W., Dewey, P.A.: Fluid-structure-sound interactions and control. In: Proceedings of the 2nd Symposium on Fluid-Structure-Sound Interactions and Control, Chap. The Swimming of Manta Rays, pp. 291–300. Springer, Berlin (2014).  https://doi.org/10.1007/978-3-642-40371-2_43
  34. 34.
    Spedding, G.R.: Wake signature detection. Annu. Rev. Fluid Mech. 46, 273–302 (2014)MathSciNetCrossRefzbMATHGoogle Scholar
  35. 35.
    Stremler, M.A.: Relative equilibria of singly periodic point vortex arrays. Phys. Fluids 15(12), 3767–3775 (2003)MathSciNetCrossRefzbMATHGoogle Scholar
  36. 36.
    Stremler, M.A.: On relative equilibria and integrable dynamics of point vortices in periodic domains. Theor. Computat. Fluid Dyn. 24(1), 25–37 (2010)CrossRefzbMATHGoogle Scholar
  37. 37.
    Stremler, M.A., Basu, S.: On point vortex models of exotic bluff body wakes. Fluid Dyn. Res. 46(6), 061410 (2014).  https://doi.org/10.1088/0169-5983/46/6/061410 MathSciNetCrossRefGoogle Scholar
  38. 38.
    Stremler, M.A., Salmanzadeh, A., Basu, S., Williamson, C.H.K.: A mathematical model of 2P and 2C vortex wakes. J. Fluids Struct. 27(5–6), 774–783 (2011).  https://doi.org/10.1016/j.jfluidstructs.2011.04.004 CrossRefGoogle Scholar
  39. 39.
    Tan, P.N., Steinbach, M., Kumar, V.: Introduction to Data Mining. Pearson, Boston (2006)Google Scholar
  40. 40.
    Triantafyllou, M.S., Weymouth, G.D., Miao, J.: Biomimetic survival hydrodynamics and flow sensing. Annu. Rev. Fluid Mech. 48(1), 150720185944007 (2016).  https://doi.org/10.1146/annurev-fluid-122414-034329 MathSciNetCrossRefzbMATHGoogle Scholar
  41. 41.
    Tytell, E.D., Borazjani, I., Sotiropoulos, F., Baker, T.V., Anderson, E.J., Lauder, G.V.: Disentangling the functional roles of morphology and motion in the swimming of fish. Integr. Comp. Biol. 50(6), 1140–1154 (2010)CrossRefGoogle Scholar
  42. 42.
    Wang, M., Hemati, M.: Classifying exotic wakes with a flow speed sensor. In: AIAA Paper, pp. 2018–1289 (2018)Google Scholar
  43. 43.
    Weihs, D., Webb, P.: Optimal avoidance and evasion tactics in predator–prey interactions. J. Theor. Biol. 106(2), 189–206 (1984).  https://doi.org/10.1016/0022-5193(84)90019-5 CrossRefGoogle Scholar
  44. 44.
    Williamson, C., Roshko, A.: Vortex formation in the wake of an oscillating cylinder. J. Fluids Struct. 2(4), 355–381 (1988)CrossRefGoogle Scholar
  45. 45.
    Windsor, S.P., Norris, S.E., Cameron, S.M., Mallinson, G.D., Montgomery, J.C.: The flow fields involved in hydrodynamic imaging by blind mexican cave fish (astyanax fasciatus). part i: open water and heading towards a wall. J. Exp. Biol. 213(22), 3819–3831 (2010).  https://doi.org/10.1242/jeb.040741 CrossRefGoogle Scholar
  46. 46.
    Windsor, S.P., Norris, S.E., Cameron, S.M., Mallinson, G.D., Montgomery, J.C.: The flow fields involved in hydrodynamic imaging by blind mexican cave fish (astyanax fasciatus). part ii: gliding parallel to a wall. J. Exp. Biol. 213(22), 3832–3842 (2010).  https://doi.org/10.1242/jeb.040790 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Aerospace Engineering and MechanicsUniversity of MinnesotaMinneapolisUSA

Personalised recommendations