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Linkage Learning Optimization of Aeroelastic and Structural Behavior of Composite Wings
(Springer, 2023) Rafiee, Roham; Farsadi, Touraj; Tehrani, Majid Ahmadi; Sharifi, Parsa
The paper aims to develop a systematic numerical design for composite wings optimization subject to aerodynamic loading and to assess the aeroelastic and structural performance of the optimized composite wing. Aeroelastic tailoring is a powerful method for utilizing the anisotropic features of composite materials used in lightweight aerospace structures. The present proposed methodology combines three different analysis tools: a commercial FE software commonly used in industry, an in-house reduced order aeroelastic framework for aeroelastic analyses with tailoring capabilities, LLGA, in-house linkage-learning genetic algorithms for optimization of stacking sequences. As a multidisciplinary problem where structural and aeroelastic behaviors are interacted, developed multi-level optimization scenario in this research converges to the optimal design in a very short time. The proposed methodology implemented as a computer code can effectively be applied to any arbitrary air vehicle's composite wing by changing input data.