Aerodynamics, sensing and control of insect-scale flapping-wing flight

Abstract

There are nearly a million known species of flying insects and 13 000 species of flying warm-blooded vertebrates, including mammals, birds and bats. While in flight, their wings not only move forward relative to the air, they also flap up and down, plunge and sweep, so that both lift and thrust can be generated and balanced, accommodate uncertain surrounding environment, with superior flight stability and dynamics with highly varied speeds and missions. As the size of a flyer is reduced, the wing-to-body mass ratio tends to decrease as well. Furthermore, these flyers use integrated system consisting of wings to generate aerodynamic forces, muscles to move the wings, and sensing and control systems to guide and manoeuvre. In this article, recent advances in insect-scale flapping-wing aerodynamics, flexible wing structures, unsteady flight environment, sensing, stability and control are reviewed with perspective offered. In particular, the special features of the low Reynolds number flyers associated with small sizes, thin and light structures, slow flight with comparable wind gust speeds, bioinspired fabrication of wing structures, neuron-based sensing and adaptive control are highlighted.

Keywords: biomimicry; flapping flight; insect scale.

Figure 1.

Characteristics of biological flapping flight based on the Reynolds number, flapping frequency and wing/body mass ratio.

Figure 2.

Numerical results of LEV structures at different Reynolds numbers. Adapted from [10]. (a) Hawkmoth, Re=6000, (b) fruit fly, Re=120 and (c) thrips, Re=10.

Figure 3.

Vortical flow structures around a low aspect ratio flapping wing. Spanwise vorticity iso-surfaces are coloured by the vorticity magnitude. Streamlines are coloured with the horizontal velocity magnitude.

Figure 4.

Comparison of (a) aerodynamic lift and (b) power. Navier–Stokes informed hybrid quasi-steady model (blue); Navier–Stokes equation solution (red). Adapted from [78].

Figure 5.

Spanwise flexural stiffness of various insect species versus wingspan from [80], overlaid by flexural stiffness of fabricated wings from [–100]. Adapted from [80].

Figure 6.

Schematic illustration of the wing fabrication using MEMS-based SCM process for centimetre-scale robotic wings in [97,105]. The process composed of (a) lamination of pre-machined layers, (b) singulation and (c) release.

Figure 7.

Scaling relationship the normalized time-averaged lift coefficient ${\overline{C}}_{\mathrm{L}}^{\ast }$ and γ [74].

Figure 8.

(Left) Vorticity contours for the optimal efficiency motion at (top) fruit fly and (bottom) water tunnel scales. (Right) Key vortex features are illustrated. Vortices indicated by dashed lines have smaller vorticity magnitudes than those with solid lines. Positive vorticity indicates counterclockwise rotating fluid elements. Negative vorticity implies clockwise rotation. Adapted from [132].

Figure 9.

Comparison of time history of lift for the optimal efficient motion at (red line) fruit fly scale with (black line) experimental data [24], (blue line) three-dimensional rigid wing computational data for fruit fly [52] and (red dashed line) water tunnel scale [22]. The band around the experimental curve indicates the upper and the lower bounds. Adapted from [132].

Figure 10.

Time-averaged lift coefficient ${\overline{C}}_{\mathrm{L}}$ as a function of phase difference φ for a flexible flapping wing in hover. Adapted from [74].

Figure 11.

(a–f) Passive pitch α and lift coefficient C_L on a flexible flapping wing in hover with various timing of passive wing rotation. Red lines, analytic model with Morison equation [73]; black lines, high-fidelity computations with Navier–Stokes equations [74]. Adapted from [73].

Figure 12.

(a) A hawkmoth Agrius convolvuli with complex wing venation. (b) A computational model of the anisotropic hawkmoth wing structure. Adapted from [139].

Figure 13.

Streamlines and pressure contours on the flexible (left) and rigid (right) wing surfaces, illustrating the effects of wing flexibility. Adapted from [139].

Figure 14.

(a) An ocelli mimicking device consisted of eight photodiodes. The sensor identifies the horizon based on the polarization of the sky. Adapted from [190]. (b) A 25 mg ocelli-inspired sensor with four phototransistors arranged in a pyramid shape. Adapted from [182].

Figure 15.

The optic flow experienced by an observer performing a rotation about a roll axis (a,b) and a translation in the frontal direction (c,d). Flow fields are shown in spherical coordinates (a,c) of the visual field (b,d). Adapted from [211].

Figure 16.

An insect flying through a corridor or complex obstacles can strategically modulate the flight speed by regulating the optic flow magnitude. The results in slower flight speed in more cluttered environments. In the meantime, the insect centres itself in the middle of the corridor by balancing the optic flow information from both sides.

Figure 17.

Diagram of bioinspired multi-mechanical systems [236].

Figure 18.

Schematic diagram of passive and active stability during flapping flight. During a latency period, fruit flies exhibit passive response without any active feedback control of wing and body kinematics [235].