The flight of insects has fascinated physicists and biologists for more than a century. Yet, until recently, researchers were unable to rigorously quantify the complex wing motions of flapping insects or measure the forces and flows around their wings. However, recent developments in high-speed videography and tools for computational and mechanical modeling have allowed researchers to make rapid progress in advancing our understanding of insect flight. These mechanical and computational fluid dynamic models, combined with modern flow visualization techniques, have revealed that the fluid dynamic phenomena underlying flapping flight are different from those of non-flapping, 2-D wings on which most previous models were based. In particular, even at high angles of attack, a prominent leading edge vortex remains stably attached on the insect wing and does not shed into an unsteady wake, as would be expected from non-flapping 2-D wings. Its presence greatly enhances the forces generated by the wing, thus enabling insects to hover or maneuver. In addition, flight forces are further enhanced by other mechanisms acting during changes in angle of attack, especially at stroke reversal, the mutual interaction of the two wings at dorsal stroke reversal or wing-wake interactions following stroke reversal. This progress has enabled the development of simple analytical and empirical models that allow us to calculate the instantaneous forces on flapping insect wings more accurately than was previously possible. It also promises to foster new and exciting multi-disciplinary collaborations between physicists who seek to explain the phenomenology, biologists who seek to understand its relevance to insect physiology and evolution, and engineers who are inspired to build micro-robotic insects using these principles. This review covers the basic physical principles underlying flapping flight in insects, results of recent experiments concerning the aerodynamics of insect flight, as well as the different approaches used to model these phenomena.

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The aerodynamics of insect flight

The flow structure that is largely responsible for the good performance of insect wings has recently been identified as a leading-edge vortex. But because such vortices become detached from a wing in two-dimensional flow, an unknown mechanism must keep them attached to (three-dimensional) flapping wings. The current explanation, analogous to a mechanism operating on delta-wing aircraft, is that spanwise flow through a spiral vortex drains energy from the vortex core. We have tested this hypothesis by systematically mapping the flow generated by a dynamically scaled model insect while simultaneously measuring the resulting aerodynamic forces. Here we report that, at the Reynolds numbers matching the flows relevant for most insects, flapping wings do not generate a spiral vortex akin to that produced by delta-wing aircraft. We also find that limiting spanwise flow with fences and edge baffles does not cause detachment of the leading-edge vortex. The data support an alternative hypothesis-that downward flow induced by tip vortices limits the growth of the leading-edge vortex.

Spanwise flow and the attachment of the leading-edge vortex on insect wings

1. Introduction 2. Fixed, rigid wing aerodynamics 3. Flexible wing aerodynamics 4. Flapping wing aerodynamics.

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Aerodynamics of Low Reynolds Number Flyers

Nomenclature Ar = rotor disk area CD = sectional drag coefficient CD0 = zero-lift drag coefficient Clα = lift-curve slope CP = power coefficient CPi = induced power coefficient CP0 = profile power coefficient CT = thrust coefficient c = chord length D = drag force D.L . = disk loading L = lift force m = mass P.L . = power loading SF = separated flow T = rotor thrust V = local wind velocity perceived by flap W = weight W f = final weight Wo = gross takeoff weight α = blade section angle of attack η = efficiency μ = dynamic viscosity ρ = air density σ = rotor solidity = flapping amplitude (peak to peak)

Challenges Facing Future Micro-Air-Vehicle Development

Large-eddy simulation (LES), coupled with a wind-turbine model, is used to investigate the characteristics of a wind-turbine wake in a neutral turbulent boundary-layer flow. The tuning-free Lagrangian scale-dependent dynamic subgrid-scale (SGS) model is used for the parametrisation of the SGS stresses. The turbine-induced forces (e.g., thrust, lift and drag) are parametrised using two models: (a) the ‘standard’ actuator-disk model (ADM-NR), which calculates only the thrust force and distributes it uniformly over the rotor area; and (b) the actuator-disk model with rotation (ADM-R), which uses the blade-element theory to calculate the lift and drag forces (that produce both thrust and rotation), and distribute them over the rotor disk based on the local blade and flow characteristics. Simulation results are compared to high-resolution measurements collected with hot-wire anemometry in the wake of a miniature wind turbine at the St. Anthony Falls Laboratory atmospheric boundary-layer wind tunnel. In general, the characteristics of the wakes simulated with the proposed LES framework are in good agreement with the measurements in the far-wake region. The ADM-R yields improved predictions compared with the ADM-NR in the near-wake region, where including turbine-induced flow rotation and accounting for the non-uniformity of the turbine-induced forces appear to be important. Our results also show that the Lagrangian scale-dependent dynamic SGS model is able to account, without any tuning, for the effects of local shear and flow anisotropy on the distribution of the SGS model coefficient.

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Large-Eddy Simulation of Wind-Turbine Wakes: Evaluation of Turbine Parametrisations

The aerodynamic characteristics of airfoils operating at Re = 4 X 10 3 were examined, varying the parameters related to the airfoil shape such as thickness, camber, and roughness. Airfoils with good aerodynamic performance at this Re have the following shape characteristics : (1) they are thinner than airfoils for higher Re numbers, (2) they have a sharp leading edge, and (3) they have a camber of about five percent with its maximum camber at about mid-chord. The characteristics of airfoils are strongly affected by leading edge vortices. The measured two-dimensional airfoil characteristics indicate that the planform, which greatly affects the flight performance of the three-dimensional wing at high Reynolds numbers, has little effect on the flight performance at this Reynolds number.

Airfoil Section Characteristics at a Low Reynolds Number

Unsteady e uid dynamic forces acting on a two-dimensional wing in sinusoidal plunging and pitching motions in still water were measured. The measured e uid dynamic forces were larger than those estimated by quasi-steady analysis, where the effect of unsteady separated vortices on the e uid dynamic forces is not considered. Vortex capture is a phenomenon related to the larger e uid dynamic forces in wing motion that is similar to wing motion of real insects. By using the measured e uid dynamic forces, we identie ed combinations of plunging and pitching motions for maximum time-averaged thrust and for maximum efe ciency. Nomenclature a = dimensionless position of the rotational axis of the pitching motion b = span length of the wing immersed in water C`;Cd = lift and drag coefe cients Cn;Ct = normal and tangential force coefe cients CX;CZ = force coefe cients in X and Z directions c = chord length c = chord vector Fn; Ft = e uid dynamic forces normal and tangential to airfoil chord FX; FZ = e uid dynamic forces in X and Z directions Fx; Fz = forces in x and z directions f = frequency of both pitching and plunging motions

Unsteady Forces on a Two-Dimensional Wing in Plunging and Pitching Motions

[] This paper presents an on-line navigation and adaptive flight control for waypoint and line tracking of Unmanned Aerial Vehicle (UAV) using neural network (NN). This system consists of two parts, a navigation system for making rolling angle command from the relative position of the aircraft and the target, and the Feedback Error Learning (FEL) part for rolling angle control. For a navigation system of waypoint tracking, Proportional Navigation (PN), a guidance law which dictates the aircraft to rotate at a rate proportional to the rotation rate of the line of sight, is used. In line tracking, the rolling angle command is created by the deviation from the target line. FEL is a learning scheme of NN which is added parallel to a conventional feedback (CFB) controller. By minimizing CFB signals through a learning process, the NN obtains approximate inverse dynamics of a desired plant. It thus contributes to the improvement of the control performance including the time delay in response and overshoot. Moreover, the adaptability of NN enables the entire control system to show good performance against disturbance such as wind gust and change in system dynamics such as actuator failure. This paper applies a linear NN with a single layer for FEL, because the control system must have high convergence rate and must be simple for a small UAV. A simulation is performed using this method. The results show that the proposed navigation control system is able to improve the whole control performance.

Lateral Guidance Control of UAV Using Feedback Error Learning

[] This paper considers a real time adaptive flight control system using a neural network (NN). In this system, the NN aims to obtain the inverse dynamics of an aircraft, and the feedback error learning (FEL) strategy proposed by Kawato et al. is used as its learning scheme. In FEL, a NN is placed parallel to a conventional feedback (CFB) controller and works as a feedforward controller to complement the time delay in the CFB controller. Moreover, the adaptability of the NN enables the entire control system to exhibit a good performance under the fluctuation of aircraft dynamic characteristics. Numerical simulations and flight tests were carried out by imitating aileron actuator failure and fluctuation of aircraft dynamics. These results show that the proposed control system is able to improve the entire control performance.

Akitoshi Sakaguchi

Papers

Airfoil Section Characteristics at a Low Reynolds Number

Unsteady Forces on a Two-Dimensional Wing in Plunging and Pitching Motions

Lateral Guidance Control of UAV Using Feedback Error Learning

Neural Network Based Adaptive Flight Control Using Feedback Error Learning