TY - JOUR
T1 - Improving the propulsion speed of a heaving wing through artificial evolution of shape
AU - Ramananarivo, Sophie
AU - Mitchel, Thomas
AU - Ristroph, Leif
N1 - Funding Information:
We acknowledge support from an NYU Global Seed grant to L.R. and from the Direction Générale de l’Armement (2013.60.0018) to S.R.
Funding Information:
Data accessibility. The dataset supporting this article, along with a movie of the experimental apparatus, have been uploaded as part of the electronic supplementary material. Authors’ contributions. S.R. and L.R. conceived and designed research; S.R. and T.M. performed research and data acquisition; S.R., T.M. and L.R. analysed and interpreted data; S.R. and L.R. wrote the paper. All authors gave final approval for publication. Competing interests. The authors declare no competing interests. Funding. We acknowledge support from an NYU Global Seed grant to L.R. and from the Direction Générale de l’Armement (2013.60.0018) to S.R. Acknowledgements. We thank S. Childress, M. Shelley and J. Zhang for discussions.
Publisher Copyright:
© 2019 The Author(s) Published by the Royal Society. All rights reserved.
PY - 2019/1/1
Y1 - 2019/1/1
N2 - Aeronautical studies have shown that subtle changes in aerofoil shape substantially alter aerodynamic forces during fixed-wing flight. The link between shape and performance for flapping locomotion involves distinct mechanisms associated with the complex flows and unsteady motions of an air- or hydro-foil. Here, we use an evolutionary scheme to modify the cross-sectional shape and iteratively improve the speed of three-dimensional printed heaving foils in forward flight. In this algorithmic-experimental method, ‘genes’ are mathematical parameters that define the shape, ‘breeding’ is the combination of genes from parent wings to form a daughter, and a wing’s measured speed is its ‘fitness’ that dictates its likelihood of breeding. Repeated over many generations, this process automatically discovers a fastest foil whose cross-section resembles a slender teardrop. We conduct an analysis that uses the larger population to identify what features of this shape are most critical, implicating slenderness, location of maximum thickness and fore-aft asymmetries in edge sharpness or bluntness. This analysis also reveals a tendency towards extremely thin and cusp-like trailing edges. These findings demonstrate artificial evolution in laboratory experiments as a successful strategy for tailoring shape to improve propulsive performance. Such a method could be used in related optimization problems, such as tuning kinematics or flexibility for flapping propulsion, and for flow–structure interactions more generally.
AB - Aeronautical studies have shown that subtle changes in aerofoil shape substantially alter aerodynamic forces during fixed-wing flight. The link between shape and performance for flapping locomotion involves distinct mechanisms associated with the complex flows and unsteady motions of an air- or hydro-foil. Here, we use an evolutionary scheme to modify the cross-sectional shape and iteratively improve the speed of three-dimensional printed heaving foils in forward flight. In this algorithmic-experimental method, ‘genes’ are mathematical parameters that define the shape, ‘breeding’ is the combination of genes from parent wings to form a daughter, and a wing’s measured speed is its ‘fitness’ that dictates its likelihood of breeding. Repeated over many generations, this process automatically discovers a fastest foil whose cross-section resembles a slender teardrop. We conduct an analysis that uses the larger population to identify what features of this shape are most critical, implicating slenderness, location of maximum thickness and fore-aft asymmetries in edge sharpness or bluntness. This analysis also reveals a tendency towards extremely thin and cusp-like trailing edges. These findings demonstrate artificial evolution in laboratory experiments as a successful strategy for tailoring shape to improve propulsive performance. Such a method could be used in related optimization problems, such as tuning kinematics or flexibility for flapping propulsion, and for flow–structure interactions more generally.
KW - Evolutionary algorithm
KW - Flapping flight
KW - Flow–structure interaction
KW - Optimal locomotion
KW - Undulatory swimming
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U2 - 10.1098/rspa.2018.0375
DO - 10.1098/rspa.2018.0375
M3 - Article
AN - SCOPUS:85061328469
SN - 1364-5021
VL - 475
JO - Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences
JF - Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences
IS - 2221
M1 - 20180375
ER -