Modelization and Kinematics Optimization for a Flapping-Wing Microair Vehicle

Rakotomamonjy, Thomas; Ouladsine, Mustapha; Moing, Thierry Le

doi:10.2514/1.22960

Cited by 33 publications

(12 citation statements)

References 16 publications

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“…Conventional parametric studies, which merely estimate the sensitivity of the design objectives with respect to each of the design variables independently, do not exploit potential interrelationships between variables. Rakotomamonjy et al [21] conducted optimization using genetic algorithm to maximize the mean lift. They used a neural network approach to generate functional forms describing the wing motion.…”

mentioning

confidence: 99%

Design Optimization of Flapping Ornithopters: The Pterosaur Replica in Forward Flight

Zakaria

Taha

Hajj

2016

Journal of Aircraft

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There has been a recent interest to explore the shape and kinematics parameters of distinct pterosaurs from their fossil records. Clearly, far more evidence is needed to understand the nuances of dinosaurs flight. A multiobjective aerodynamic optimization problem of the wing kinematics and planform of a pterosaur replica ornithopter designed by Aerovironment is performed. Objective functions include minimization of the required cycle-averaged aerodynamic power and maximization of the propulsive efficiency. It is found that inclusion of the inertial power requirements is necessary for a physical and proper formulation of the optimization problem. Furthermore, the mere addition of the inertial power requirements is not enough to obtain reasonable results. Rather, one has to consider a partial (or even zero) elastic energy storage. The minimum power kinematic parameters closely match those of the previously designed pterosaur replica. Nevertheless, the obtained efficiency for such a design (minimum power) is 10%, which is considerably lower than the maximum possible efficiency for the used planform (40%). Furthermore, the optimized planform for maximum efficiency of the pterosaur yields to an increase in the propulsive efficiency by 6%.

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mentioning

confidence: 99%

Design Optimization of Flapping Ornithopters: The Pterosaur Replica in Forward Flight

Zakaria

Taha

Hajj

2016

Journal of Aircraft

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“…9. This discrepancy is due to the vortex interactions and strong vortex traces due to high angles of attack [13,21,25]. The rotational phase is found to be good approximated (small a* values) however the translational phase has higher lift coefficients than the predicted data.…”

Section: Results and Analysismentioning

confidence: 78%

“…Milano and Gharib [12] considered an experiment in which a rectangular flat plate is flapped with two degrees of freedom and a genetic algorithm tunes its trajectory parameters so as to achieve maximum average lift force, thus evolving a population of trajectories all yielding optimal lift forces. A flight dynamics oriented simulation model for flapping wing micro air vehicle based on insects or hummingbird flapping flight has been developed by Rakotomamonjy et al [13]. A neural network has been designed to reproduce various function shapes modelling the wing movements, which maximize the mean lift.…”

Section: Introductionmentioning

confidence: 99%

Ability to forecast unsteady aerodynamic forces of flapping airfoils by artificial neural network

Kurtuluş

2008

Neural Comput & Applic

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The ability of artificial neural networks (ANN) to model the unsteady aerodynamic force coefficients of flapping motion kinematics has been studied. A neural networks model was developed based on multi-layer perception (MLP) networks and the Levenberg-Marquardt optimization algorithm. The flapping kinematics data were divided into two groups for the training and the prediction test of the ANN model. The training phase led to a very satisfactory calibration of the ANN model. The attempt to predict aerodynamic forces both the lift coefficient and drag coefficient showed that the ANN model is able to simulate the unsteady flapping motion kinematics and its corresponding aerodynamic forces. The shape of the simulated force coefficients was found to be similar to that of the numerical results. These encouraging results make it possible to consider interesting and new prospects for the modelling of flapping motion systems, which are highly non-linear systems.

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“…Three basic levels of computational fidelity can be reasonably considered for hovering wing simulations, and each have been used for kinematic optimization: lower-fidelity quasi-steady blade element methods (see Berman and Wang [3], Rakotomamonjy et al [4], Kurdi et al [5]), moderate-fidelity vortex-lattice/vorton methods (see Chabalko et al. [6], Stanford and Beran [7]), and high-fidelity Navier-Stokes solvers (see Shyy et al [1], Soueid et al [8]).…”

Section: Introductionmentioning

confidence: 99%

Variable-Fidelity Kinematic Optimization of a Two-Dimensional Hovering Wing

Moore

Stanford

McClung

et al. 2011

49th AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition

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In this paper, we investigate several gradient-based multi-fidelity methods for optimizing the flapping kinematics of a two-dimensional hovering wing. Two levels of fidelity are assessed in this work. The high fidelity tool is chosen to be the OVERTURE CFD package, and the low fidelity model is a computationally inexpensive blade element method. Both trigonometric and spline-based kinematics are assessed using a variable-fidelity framework. Power and lift constrained optimization problems are considered in addition to an unconstrained lift maximization case. The efficacy of the multi-fidelity method is found to be highly dependent on the quality of the low fidelity model. If the low fidelity model is capable of accurately modeling the dominant flow features, convergence can be substantially faster than the baseline single-fidelity method.

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Modelization and Kinematics Optimization for a Flapping-Wing Microair Vehicle

Cited by 33 publications

References 16 publications

Design Optimization of Flapping Ornithopters: The Pterosaur Replica in Forward Flight

Design Optimization of Flapping Ornithopters: The Pterosaur Replica in Forward Flight

Ability to forecast unsteady aerodynamic forces of flapping airfoils by artificial neural network

Variable-Fidelity Kinematic Optimization of a Two-Dimensional Hovering Wing

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