Multidisciplinary multifidelity optimisation of a flexible wing aerofoil with reference to a small UAV

被引:1
|
作者
M. Berci
V. V. Toropov
R. W. Hewson
P. H. Gaskell
机构
[1] University of Leeds,School of Mechanical Engineering
[2] Queen Mary University of London,School of Engineering and Materials Science
[3] Imperial College,Department of Aeronautics
[4] University of Durham,School of Engineering and Computing
关键词
Multidisciplinary; Multifidelity; Optimisation; Flexible aerofoil; Small UAV;
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中图分类号
学科分类号
摘要
The preliminary Multidisciplinary Design and Optimisation of a flexible wing aerofoil apropos a small Unmanned Air Vehicle is performed using a multifidelity model-based strategy. Both the passively adaptive structure and the shape of the flexible wing aerofoil are optimised for best aerodynamic performance under aero-structural constraints, within a coupled aeroelastic formulation. A typical flight mission for surveillance purposes is considered and includes the potential occurrence of wind gusts. A metamodel for the high-fidelity objective function and each of the constraints is built, based on a tuned low-fidelity one, in order to improve the efficiency of the optimisation process. Both metamodels are based on solutions of the aeroelastic equations for a flexible aerofoil but employ different levels of complexity and computational cost for modelling aerodynamics and structural dynamics within a modal approach. The high-fidelity model employs nonlinear Computational Fluid Dynamics coupled with a full set of structural modes, whereas the low-fidelity one employs linear thin aerofoil theory coupled with a reduced set of structural modes. The low-fidelity responses are then corrected according to few high-fidelity responses, as prescribed by an appropriate Design of Experiment, by means of a suitable tuning technique. A standard Genetic Algorithm is hence utilised to find the global optimum, showing that a flexible aerofoil is characterised by higher aerodynamic efficiency than its rigid counterpart. Wing weight reduction is also accomplished when a Multiobjective Genetic Algorithm is adopted.
引用
收藏
页码:683 / 699
页数:16
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