Optimisation of a Dragonfly-Inspired Flapping Wing-Actuation System
An optimisation method using both global and local
optimisation is implemented to determine the flapping profile which
will produce the most lift for an experimental wing-actuation system.
The optimisation method is tested using a numerical quasi-steady
analysis. Results of an optimised flapping profile show a 20% increase
in lift generated as compared to flapping profiles obtained by high
speed cinematography of a Sympetrum frequens dragonfly. Initial
optimisation procedures showed 3166 objective function evaluations.
The global optimisation parameters - initial sample size and stage
one sample size, were altered to reduce the number of function
evaluations. Altering the stage one sample size had no significant
effect. It was found that reducing the initial sample size to 400
would allow a reduction in computational effort to approximately
1500 function evaluations without compromising the global solvers
ability to locate potential minima. To further reduce the optimisation
effort required, we increase the local solver’s convergence tolerance
criterion. An increase in the tolerance from 0.02N to 0.05N decreased
the number of function evaluations by another 20%. However, this
potentially reduces the maximum obtainable lift by up to 0.025N.
[1] S. Ashley, "Palm-size spy plane,” Mechanical Engineering, vol. 120,
no. 2, pp. 1–11, 1998.
[2] J. Sirohi, "Microflyers: inspiration from nature,” in SPIE Smart
Structures and Materials+ Nondestructive Evaluation and Health
Monitoring. International Society for Optics and Photonics, 2013, pp.
86 860U–86 860U–15.
[3] R. Wootton, "The fossil record and insect flight,” in Symp. R. Entomol.
Soc. Lond, vol. 7, 1976, pp. 235–254.
[4] M. L. May, "Heat exchange and endothermy in protodonata,” Evolution,
pp. 1051–1058, 1982.
[5] G. R¨uppell, "Kinematic analysis of symmetrical flight manoeuvres of
odonata,” Journal of Experimental Biology, vol. 144, no. 1, pp. 13–42,
1989.
[6] J. Chahl, G. Dorrington, and A. Mizutani, "The dragonfly flight
envelope and its application to micro uav research and development,” in
AIAC15: 15th Australian International Aerospace Congress. Australian
International Aerospace Congress, p. 278.
[7] D. E. Alexander, "Wind tunnel studies of turns by flying dragonflies,”
Journal of Experimental Biology, vol. 122, no. 1, pp. 81–98, 1986.
[8] A. Mizutani, J. S. Chahl, and M. V. Srinivasan, "Insect behaviour:
Motion camouflage in dragonflies,” Nature, vol. 423, no. 6940, pp.
604–604, 2003.
[9] Sviderski˘ı, "Structural-functional peculiarities of wing appatatus of
insects that have and do not have maneuver flight].”
[10] J. M. Kok and J. Chahl, "Resonance versus aerodynamics for energy
savings in agile natural flyers,” in SPIE Smart Structures and Materials+
Nondestructive Evaluation and Health Monitoring. International
Society for Optics and Photonics, 2014, pp. 905 504–905 504.
[11] Z. J. Wang, "Dissecting insect flight,” Annu. Rev. Fluid Mech., vol. 37,
pp. 183–210, 2005.
[12] P. Simmons, "The neuronal control of dragonfly flight. i. anatomy,” The
Journal of Experimental Biology, vol. 71, no. 1, pp. 123–140, 1977.
[13] R. Dudley, The biomechanics of insect flight: form, function, evolution.
Princeton University Press, 2002.
[14] A. Azuma and T.Watanabe, "Flight performance of a dragonfly,” Journal
of Experimental Biology, vol. 137, no. 1, pp. 221–252, 1988.
[15] A. Azuma, S. Azuma, I. Watanabe, and T. Furuta, "Flight mechanics
of a dragonfly,” Journal of experimental biology, vol. 116, no. 1, pp.
79–107, 1985.
[16] J. Wakeling and C. P. Ellington, "Dragonfly flight. i. gliding flight and
steady-state aerodynamic forces,” Journal of Experimental Biology, vol.
200, no. 3, pp. 543–556, 1997.
[17] G. J. Berman and Z. Wang, "Energy-minimizing kinematics in hovering
insect flight,” Journal of Fluid Mechanics, vol. 582, pp. 153–168, 2007.
[18] M. Ghommem, M. R. Hajj, L. T. Watson, D. T. Mook, R. Snyder,
and P. S. Beran, "Deterministic global optimization of flapping wing
motion for micro air vehicles,” in Proceedings of the 13th AIAA/ISSMO
Multidisciplinary Analysis Optimization Conference, AIAA Paper, no.
2006-1852, 2010.
[19] S. L. Thomson, C. A. Mattson, M. B. Colton, S. P. Harston, D. C.
Carlson, and M. Cutler, "Experiment-based optimization of flapping
wing kinematics,” in Proceedings of the 47th Aerospace sciences
meeting, 2009.
[20] Z. Ugray, L. Lasdon, J. Plummer, F. Glover, J. Kelly, and R. Mart´ı,
"Scatter search and local nlp solvers: A multistart framework for global
optimization,” INFORMS Journal on Computing, vol. 19, no. 3, pp.
328–340, 2007.
[21] F. Glover, M. Laguna, and R. Mart´ı, "Fundamentals of scatter search and
path relinking,” Control and cybernetics, vol. 39, no. 3, pp. 653–684,
2000.
[22] P. E. Gill, W. Murray, M. A. Saunders, and M. H. Wright, "Procedures
for optimization problems with a mixture of bounds and general linear
constraints,” ACM Transactions on Mathematical Software (TOMS),
vol. 10, no. 3, pp. 282–298, 1984.
[23] P. Gill, W. Murray, and M. Wright, "Numerical linear algebra and
optimization vol. 1, 1991.”
[24] T. Weis-Fogh, "Quick estimates of flight fitness in hovering
animals, including novel mechanisms for lift production,” Journal of
Experimental Biology, vol. 59, no. 1, pp. 169–230, 1973.
[25] R. A. Norberg, "Hovering flight of the dragonfly aeschna juncea l.,
kinematics and aerodynamics,” Swimming and flying in nature, vol. 2,
pp. 763–781, 1975.
[26] S. P. Sane and M. H. Dickinson, "The aerodynamic effects of wing
rotation and a revised quasi-steady model of flapping flight,” Journal of
Experimental Biology, vol. 205, no. 8, pp. 1087–1096, 2002.
[27] Z. J. Wang, J. M. Birch, and M. H. Dickinson, "Unsteady forces
and flows in low reynolds number hovering flight: two-dimensional
computations vs robotic wing experiments,” Journal of Experimental
Biology, vol. 207, no. 3, pp. 449–460, 2004.
[28] Z. J. Wang, "The role of drag in insect hovering,” Journal of
Experimental Biology, vol. 207, no. 23, pp. 4147–4155, 2004.
[29] M. H. Dickinson, F.-O. Lehmann, and S. P. Sane, "Wing rotation and
the aerodynamic basis of insect flight,” Science, vol. 284, no. 5422, pp.
1954–1960, 1999.
[30] R. k. Norberg, "The pterostigma of insect wings an inertial regulator
of wing pitch,” Journal of comparative physiology, vol. 81, no. 1, pp.
9–22, 1972.
[1] S. Ashley, "Palm-size spy plane,” Mechanical Engineering, vol. 120,
no. 2, pp. 1–11, 1998.
[2] J. Sirohi, "Microflyers: inspiration from nature,” in SPIE Smart
Structures and Materials+ Nondestructive Evaluation and Health
Monitoring. International Society for Optics and Photonics, 2013, pp.
86 860U–86 860U–15.
[3] R. Wootton, "The fossil record and insect flight,” in Symp. R. Entomol.
Soc. Lond, vol. 7, 1976, pp. 235–254.
[4] M. L. May, "Heat exchange and endothermy in protodonata,” Evolution,
pp. 1051–1058, 1982.
[5] G. R¨uppell, "Kinematic analysis of symmetrical flight manoeuvres of
odonata,” Journal of Experimental Biology, vol. 144, no. 1, pp. 13–42,
1989.
[6] J. Chahl, G. Dorrington, and A. Mizutani, "The dragonfly flight
envelope and its application to micro uav research and development,” in
AIAC15: 15th Australian International Aerospace Congress. Australian
International Aerospace Congress, p. 278.
[7] D. E. Alexander, "Wind tunnel studies of turns by flying dragonflies,”
Journal of Experimental Biology, vol. 122, no. 1, pp. 81–98, 1986.
[8] A. Mizutani, J. S. Chahl, and M. V. Srinivasan, "Insect behaviour:
Motion camouflage in dragonflies,” Nature, vol. 423, no. 6940, pp.
604–604, 2003.
[9] Sviderski˘ı, "Structural-functional peculiarities of wing appatatus of
insects that have and do not have maneuver flight].”
[10] J. M. Kok and J. Chahl, "Resonance versus aerodynamics for energy
savings in agile natural flyers,” in SPIE Smart Structures and Materials+
Nondestructive Evaluation and Health Monitoring. International
Society for Optics and Photonics, 2014, pp. 905 504–905 504.
[11] Z. J. Wang, "Dissecting insect flight,” Annu. Rev. Fluid Mech., vol. 37,
pp. 183–210, 2005.
[12] P. Simmons, "The neuronal control of dragonfly flight. i. anatomy,” The
Journal of Experimental Biology, vol. 71, no. 1, pp. 123–140, 1977.
[13] R. Dudley, The biomechanics of insect flight: form, function, evolution.
Princeton University Press, 2002.
[14] A. Azuma and T.Watanabe, "Flight performance of a dragonfly,” Journal
of Experimental Biology, vol. 137, no. 1, pp. 221–252, 1988.
[15] A. Azuma, S. Azuma, I. Watanabe, and T. Furuta, "Flight mechanics
of a dragonfly,” Journal of experimental biology, vol. 116, no. 1, pp.
79–107, 1985.
[16] J. Wakeling and C. P. Ellington, "Dragonfly flight. i. gliding flight and
steady-state aerodynamic forces,” Journal of Experimental Biology, vol.
200, no. 3, pp. 543–556, 1997.
[17] G. J. Berman and Z. Wang, "Energy-minimizing kinematics in hovering
insect flight,” Journal of Fluid Mechanics, vol. 582, pp. 153–168, 2007.
[18] M. Ghommem, M. R. Hajj, L. T. Watson, D. T. Mook, R. Snyder,
and P. S. Beran, "Deterministic global optimization of flapping wing
motion for micro air vehicles,” in Proceedings of the 13th AIAA/ISSMO
Multidisciplinary Analysis Optimization Conference, AIAA Paper, no.
2006-1852, 2010.
[19] S. L. Thomson, C. A. Mattson, M. B. Colton, S. P. Harston, D. C.
Carlson, and M. Cutler, "Experiment-based optimization of flapping
wing kinematics,” in Proceedings of the 47th Aerospace sciences
meeting, 2009.
[20] Z. Ugray, L. Lasdon, J. Plummer, F. Glover, J. Kelly, and R. Mart´ı,
"Scatter search and local nlp solvers: A multistart framework for global
optimization,” INFORMS Journal on Computing, vol. 19, no. 3, pp.
328–340, 2007.
[21] F. Glover, M. Laguna, and R. Mart´ı, "Fundamentals of scatter search and
path relinking,” Control and cybernetics, vol. 39, no. 3, pp. 653–684,
2000.
[22] P. E. Gill, W. Murray, M. A. Saunders, and M. H. Wright, "Procedures
for optimization problems with a mixture of bounds and general linear
constraints,” ACM Transactions on Mathematical Software (TOMS),
vol. 10, no. 3, pp. 282–298, 1984.
[23] P. Gill, W. Murray, and M. Wright, "Numerical linear algebra and
optimization vol. 1, 1991.”
[24] T. Weis-Fogh, "Quick estimates of flight fitness in hovering
animals, including novel mechanisms for lift production,” Journal of
Experimental Biology, vol. 59, no. 1, pp. 169–230, 1973.
[25] R. A. Norberg, "Hovering flight of the dragonfly aeschna juncea l.,
kinematics and aerodynamics,” Swimming and flying in nature, vol. 2,
pp. 763–781, 1975.
[26] S. P. Sane and M. H. Dickinson, "The aerodynamic effects of wing
rotation and a revised quasi-steady model of flapping flight,” Journal of
Experimental Biology, vol. 205, no. 8, pp. 1087–1096, 2002.
[27] Z. J. Wang, J. M. Birch, and M. H. Dickinson, "Unsteady forces
and flows in low reynolds number hovering flight: two-dimensional
computations vs robotic wing experiments,” Journal of Experimental
Biology, vol. 207, no. 3, pp. 449–460, 2004.
[28] Z. J. Wang, "The role of drag in insect hovering,” Journal of
Experimental Biology, vol. 207, no. 23, pp. 4147–4155, 2004.
[29] M. H. Dickinson, F.-O. Lehmann, and S. P. Sane, "Wing rotation and
the aerodynamic basis of insect flight,” Science, vol. 284, no. 5422, pp.
1954–1960, 1999.
[30] R. k. Norberg, "The pterostigma of insect wings an inertial regulator
of wing pitch,” Journal of comparative physiology, vol. 81, no. 1, pp.
9–22, 1972.
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:67968", author = "Jia-Ming Kok and Javaan Chahl", title = "Optimisation of a Dragonfly-Inspired Flapping Wing-Actuation System", abstract = "An optimisation method using both global and local
optimisation is implemented to determine the flapping profile which
will produce the most lift for an experimental wing-actuation system.
The optimisation method is tested using a numerical quasi-steady
analysis. Results of an optimised flapping profile show a 20% increase
in lift generated as compared to flapping profiles obtained by high
speed cinematography of a Sympetrum frequens dragonfly. Initial
optimisation procedures showed 3166 objective function evaluations.
The global optimisation parameters - initial sample size and stage
one sample size, were altered to reduce the number of function
evaluations. Altering the stage one sample size had no significant
effect. It was found that reducing the initial sample size to 400
would allow a reduction in computational effort to approximately
1500 function evaluations without compromising the global solvers
ability to locate potential minima. To further reduce the optimisation
effort required, we increase the local solver’s convergence tolerance
criterion. An increase in the tolerance from 0.02N to 0.05N decreased
the number of function evaluations by another 20%. However, this
potentially reduces the maximum obtainable lift by up to 0.025N.
", keywords = "Flapping wing, Optimisation, Quasi-steady model.", volume = "8", number = "9", pages = "1565-8", }
