Wing Geometry and Kinematic Parameters Optimization of Flapping Wing Hovering Flight

Ke, Xi-Jun; Zhang, Weiping

doi:10.3390/app6120390

Cited by 7 publications

(3 citation statements)

References 67 publications

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“…The analytical model accounting for only the translational mechanism was also adopted by Nabawy and Crowther [8] to acquire the minimum power constrained by a given lift, and they concluded that the flapping angle with a triangular profile and the pitching angle with a rectangular profile consume the lowest power with the given wing geometry parameters. X. Ke and W. Zhang [30] adopted a revised quasi-steady aerodynamic model to evaluate the wing performance of dynamically scaled fruit flies. Optimizations of wing geometry and kinematic parameters were conducted, revealing that the optimal flapping angle followed a harmonious profile, while the optimal pitching angle exhibited a rounded trapezoidal profile with a faster time scale of pitching reversal.…”

Section: Introductionmentioning

confidence: 99%

Optimization of a Twistable Hovering Flapping Wing Inspired by Giant Hummingbirds Using the Unsteady Blade Element Theory

et al. 2023

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Due to the complexity of tailoring the wing flexibility and selecting favorable kinematics, the design of flapping wings is a considerably challenging problem. Therefore, there is an urgent need to investigate methods that can be used to design wings with high energy efficiency. In this study, an optimization model was developed to improve energy efficiency by optimizing wing geometric and kinematic parameters. Then, surrogate optimization was used to solve the design optimization model. Finally, the optimal design parameters and the associated sensitivity were provided. The optimized flapping wing, inspired by hummingbirds, features large geometrical parameters, a moderate amplitude of the flapping angle, and low frequency. With the spanwise twisting deformation considered in the parameterization model, the optimization solver gave an optimized wing with a pitching amplitude of approximately 39 deg at the root and 76 deg at the tip. According to the sensitivity analysis, the length of the wing, flapping frequency, and flapping amplitude are the three critical parameters that determine both force generation and power consumption. The amplitude of the pitching motion at the wing root contributes to lowering power consumption. These results provide some guidance for the optimal design of flapping wings.

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Section: Introductionmentioning

confidence: 99%

Optimization of a Twistable Hovering Flapping Wing Inspired by Giant Hummingbirds Using the Unsteady Blade Element Theory

et al. 2023

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“…Two main strategies exist to study the aerodynamic characteristics: an experiment-based approach [17][18][19] or a model-based approach [20][21][22]. The disadvantage of the purely experimental approach is that a limited number of possible configurations can be considered and a model-supported approach may solve this issue.…”

Section: Introductionmentioning

confidence: 99%

Aerodynamic Model Updating Using Wind-Tunnel Setup for Hummingbirdlike Flapping Wing Nanorobot

2022

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The complex wing motion of insects and small birds leads to high lift production in nature. Specific lift-enhancing mechanismsarise from the combination of a horizontal stroke motion and a secondary wing pitching motion. Research around flapping wing nano air vehicles (FWNAVs) copies this wing motion to fly small drones, in an effort to benefit from the same mechanisms. A flapping wing aerodynamic model can predict the force production of the FWNAV and helps to understand the influence of the wing kinematics thereon. This work starts from a quasi-steady model expression for the aerodynamics and proposes a wind tunnel procedure to identify the force coefficients in the model empirically. The procedure is based on a test case performed by the AVT-202 research team which combines wing translation and wing rotation, imitating the aerodynamic condition of the flapping wing in a simplified set-up. Comparison of measurements and model simulations demonstrates that the procedure can be used to update the model parameters for our specific FWNAV configuration, leading to an empirical relation for the force coefficients. Simulations with the updated model using the empirical relations show good agreement with measured force production for a wide range of input variables.

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“…A final design was reached fast and consistently by a genetic algorithm, including different sampling criteria and multiple surrogates in the gene evolution process, despite noisy data measurements and small manufacturing inaccuracies. The combined optimization of wing shape and kinematics by Ke and Zhang [26] led to solutions with lower flapping frequency, larger wing geometries and lower power density in comparison to the solutions from the individual optimization of shape and kinematics. Very recently, Lee and Lua [27] focused on the pitching motion of a hawk moth in hovering flight and used a two-stage optimization method to identity the influence of the pitch angle evolution to the flapping wing performance of complex, insect-like motion profiles.…”

Section: Introductionmentioning

confidence: 99%

Genetic Algorithm Based Optimization of Wing Rotation in Hover

Gehrke

Guyon-Crozier

Mülleners

2018

Fluids

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The pitching kinematics of an experimental hovering flapping wing setup are optimized by means of a genetic algorithm. The pitching kinematics of the setup are parameterized with seven degrees of freedom to allow for complex non-linear and non-harmonic pitching motions. Two optimization objectives are considered. The first objective is maximum stroke average efficiency, and the second objective is maximum stroke average lift. The solutions for both optimization scenarios converge within less than 30 generations based on the evaluation of their fitness. The pitching kinematics of the best individual of the initial and final population closely resemble each other for both optimization scenarios, but the optimal kinematics differ substantially between the two scenarios. The most efficient pitching motion is smoother and closer to a sinusoidal pitching motion, whereas the highest lift-generating pitching motion has sharper edges and is closer to a trapezoidal motion. In both solutions, the rotation or pitching motion is advanced with respect to the sinusoidal stroke motion. Velocity field measurements at selected phases during the flapping motions highlight why the obtained solutions are optimal for the two different optimization objectives. The most efficient pitching motion is characterized by a nearly constant and relatively low effective angle of attack at the start of the half stroke, which supports the formation of a leading edge vortex close to the airfoil surface, which remains bound for most of the half stroke. The highest lift-generating pitching motion has a larger effective angle of attack, which leads to the generation of a stronger leading edge vortex and higher lift coefficient than in the efficiency optimized scenario.

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Wing Geometry and Kinematic Parameters Optimization of Flapping Wing Hovering Flight

Cited by 7 publications

References 67 publications

Optimization of a Twistable Hovering Flapping Wing Inspired by Giant Hummingbirds Using the Unsteady Blade Element Theory

Optimization of a Twistable Hovering Flapping Wing Inspired by Giant Hummingbirds Using the Unsteady Blade Element Theory

Aerodynamic Model Updating Using Wind-Tunnel Setup for Hummingbirdlike Flapping Wing Nanorobot

Genetic Algorithm Based Optimization of Wing Rotation in Hover

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