Smart wing rotation and trailing-edge vortices enable high frequency mosquito flight

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Abstract

Mosquitoes exhibit unusual wing kinematics; their long, slender wings flap at remarkably high frequencies for their size (>800 Hz)and with lower stroke amplitudes than any other insect group1. This shifts weight support away from the translation-dominated, aerodynamic mechanisms used by most insects2, as well as by helicopters and aeroplanes, towards poorly understood rotational mechanisms that occur when pitching at the end of each half-stroke. Here we report free-flight mosquito wing kinematics, solve the full Navier–Stokes equations using computational fluid dynamics with overset grids, and validate our results with in vivo flow measurements. We show that, although mosquitoes use familiar separated flow patterns, much of the aerodynamic force that supports their weight is generated in a manner unlike any previously described for a flying animal. There are three key features: leading-edge vortices (a well-known mechanism that appears to be almost ubiquitous in insect flight), trailing-edge vortices caused by a form of wake capture at stroke reversal, and rotational drag. The two new elements are largely independent of the wing velocity, instead relying on rapid changes in the pitch angle (wing rotation) at the end of each half-stroke, and they are therefore relatively immune to the shallow flapping amplitude. Moreover, these mechanisms are particularly well suited to high aspect ratio mosquito wings.

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Figure 1: Low-amplitude mosquito kinematics.
Figure 2: Validation of CFD with PIV quantitative flow fields.
Figure 3: Aerodynamic forces generated by wings and the mechanisms that produce them: trailing-edge vortices, leading-edge vortices and rotational drag.
Figure 4: Wing pronation.

Change history

  • 05 April 2017

    The y-axis units were corrected in Fig. 3.

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Acknowledgements

The authors were supported by the EPSRC (EP/H004025/1 and EP/M003698/1), BBSRC (BB/J001244/1). R.J.B. was supported by an EPSRC Career Acceleration Fellowship. S.M.W. was supported by a Royal Society University Research Fellowship. The work reported in this paper was funded by the Autonomous Systems Underpinning Research (ASUR) programme under the auspices of the Defence Science and Technology Laboratory (Dstl), UK Ministry of Defence. The authors acknowledge useful discussions with I. Russell and G. Gibson, P. Simoes for rearing the mosquitoes, and F. Albert-Davie and M. Inglis for assistance during raw data collection. The authors thank G. Taylor for the loan of four high-speed cameras purchased on European Research Council (ERC) grant 204513, and H. Liu for the permission to use the simulator and surface pressure distribution of the fruit fly wing.

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Contributions

R.J.B. and S.M.W. conceived the experimental design; N.P. and S.M.W. designed and constructed the apparatus and N.P. led the data collection; all authors contributed to data collection; S.M.W. processed the raw data to extract detailed kinematics; T.N. performed the CFD simulations; N.P., T.N. and R.J.B. collected and processed the PIV data; R.J.B. drafted the manuscript; all authors contributed to data interpretation and manuscript preparation.

Corresponding author

Correspondence to Richard J. Bomphrey.

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The authors declare no competing financial interests.

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Reviewer Information Nature thanks S. Swartz and the other anonymous reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Figure 1 Mosquito kinematics acquisition rig, wing lengths and mean kinematic patterns.

a, b, CAD representation (a) and photograph of the apparatus (b) used to record the body motion and wing kinematics of mosquitoes. The recording volume lies at the intersection of the fields of view of eight high-speed cameras, each creating a silhouette image of the mosquito by the shadow from high power IR-LED illumination. c, Wing-length estimates for mosquitoes captured in each of 15 sequences (M01-M15). Each estimate shows the median as a black line with shading representing the 95% confidence interval based upon all wingbeats from each sequence. Green and blue boxes group sequences that could not be reliably separated using Tukey’s honestly significant difference criterion, although they may come from different individuals of very similar size. As such, our fully processed data set of 15 sequences comprises between 12 and 15 individual mosquitoes. d, Mean wingbeat kinematics for all wingbeats in each of 15 recorded sequences. With reference to c, M01, M06 and M09, coloured green, may be from the same individual. Similarly, M05 and M11, coloured blue, may also be from a single individual. Source data

Extended Data Figure 2 Wing surface pressure distribution and fluid flow visualized by streamlines showing consistency across each of the 15 mosquito sequences.

Each image corresponds to key instant t1. Formation of the trailing-edge vortex owing to capture of the induced flow from the preceding upstroke causes a distinct region of low pressure on the posterior portion of the wing. Source data

Extended Data Figure 3 Wing surface pressure distribution and fluid flow visualized by streamlines showing consistency across each of the 15 mosquito sequences.

Each image corresponds to key instant t2. The downstroke force peak is dominated by a leading-edge vortex and corresponding low pressure on the anterior portion of the wing. The trailing-edge vortex has usually shed by this point in the stroke cycle.

Extended Data Figure 4 Wing surface pressure distribution and fluid flow visualized by streamlines showing consistency across each of the 15 mosquito sequences.

Each image corresponds to key instant t3. A low-pressure region is evident on the posterior portion of the wing caused by lift from rotational drag as the wing rotates around an axis close to the leading edge. Source data

Extended Data Figure 5 Wing surface pressure distribution and fluid flow visualized by streamlines showing consistency across each of the 15 mosquito sequences.

Each image corresponds to key instant t4. Formation of a trailing-edge vortex on the aerodynamic upper, (anatomical ventral) surface of the wing during the upstroke due to capture of the induced flow from the preceding downstroke causes a distinct region of low pressure on the posterior portion of the wing.

Extended Data Figure 6 Wing surface pressure distribution and fluid flow visualized by streamlines showing consistency across each of the 15 mosquito sequences.

Each image corresponds to key instant t5. A low-pressure region exists over much of the aerodynamic upper, (anatomical ventral) surface of the wing as the result of a combination of rotational drag (caused by wing rotation around an axis close to the leading edge) and the remnants of the leading-edge vortex of the upstroke (which is no longer coherent in most examples but is retained in M03, M04, M06, M08, M11).

Extended Data Figure 7 Comparison of the local flow conditions at the trailing edge of the wings of mosquitoes and fruit flies during pronation (t/T = 0.09).

The comparatively higher local angle of attack of the mosquito wing is caused by the induced flow from the preceding upstroke. This is a product of kinematic tuning and a form of wake capture that leads to roll up of a transient, coherent, trailing-edge vortex. The vortex contributes to weight support along much of the length of the slender mosquito wing, despite it having little ground velocity during the rotational phase of the stroke cycle.

Extended Data Figure 8 Comparison of computed CFD lift force (black) compared against a simple quasi-steady model (grey) for each of 15 mosquito flight sequences.

Orange shading shows where the quasi-steady model over-predicts the force estimate from the CFD simulation, whereas green shows under-prediction. (See also Fig. 3)

Extended Data Figure 9 Lift and drag polars from high-fidelity CFD simulations of the mosquito wing model in continuous rotational sweep at four Reynolds numbers.

These were used to create dynamic lift coefficients for the blade element modelling with quasi-steady assumption. Coefficients are calculated for the third rotation, to account for the reduction in effective angle of attack when wings operate in the induced downwash from the preceding wing stroke.

Extended Data Figure 10 Morphology extraction and the CFD grid used for simulations.

a, b, We used the mean wing planform of three mosquitoes, extracted from microscope images of recently excised wings, to generate the wing grids used in our CFD simulations. c, d, The body shape was approximated from the silhouettes in the raw video data by fitting ellipses normal to the central axis of the body taken from each of the eight camera views. e, f, Local and background grids used for CFD. g, CFD grid and time-step independence was verified after performing simulations with variable cell density and time-step intervals. Source data

Supplementary information

Wing stroke cycle

Video showing the experimental apparatus, raw data, wing geometry routine, kinematics, vortex wake (using isosurfaces of the Q-criterion), and pressure distribution and instantaneous flow fields at key instants (t1-t5) throughout the wing stroke cycle. (MOV 29036 kb)

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Bomphrey, R., Nakata, T., Phillips, N. et al. Smart wing rotation and trailing-edge vortices enable high frequency mosquito flight. Nature 544, 92–95 (2017). https://doi.org/10.1038/nature21727

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