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Global Aero-Structural Optimization of Composite Forward-Swept Wings Considering Natural Laminar Flow

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Publicado en:Aerospace vol. 12, no. 12 (2025), p. 1076-1097
Autor principal: Wang, Kai
Otros Autores: Wang, Xiaoguang, Han Xiujie, Xiao, Bo, Shan Zhiyuan, Ding, Jie, Wu, Tao
Publicado:
MDPI AG
Materias:
Laminar flow
Aircraft
Swept forward wings
Geometric constraints
Design optimization
Simulation
Multidisciplinary design optimization
Turbulence models
Drag coefficients
Aerodynamics
Laminar composites
Reynolds number
Reynolds averaged Navier-Stokes method
Viscous flow
Shear strength
Composite materials
Materials failure
Acceso en línea:Citation/Abstract
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Resumen:Forward-swept wings are more suitable for natural laminar flow than backward-swept wings. However, in order to reduce the difficulty of optimization, most aero-structural optimization studies of forward-swept wings do not consider the automatic laminar–turbulent transition, discrete variables, or large-scale constraints, which may result in undesirable optimization results. In this article, an efficient aero-structural optimization method for the composite forward-swept natural laminar flow (FSNLF) wing is proposed, which can solve MDO problems with those issues. Reynolds-averaged Navier–Stokes (RANS) equations coupled with the dual eN transition method are used to simulate subsonic viscous flows. A surrogate-based optimization (SBO) algorithm combining a discrete variable handling method is developed to solve the multidisciplinary design optimization (MDO) problem involving many discrete ply thickness variables of predefined angles (0°/±45°/90°). The Kreisselmeier–Steinhauser (KS) method is employed to handle large-scale geometric constraints, ply fraction constraints and material failure constraints. To verify the effectiveness of the proposed method, we perform the aero-structural optimization of an A320-class composite FSNLF wing. Results show that the proposed method offers great potential in the aero-structural optimization of the composite FSNLF wing. It can handle 32 discrete variables and 11,089 constraints, the drag coefficient and mass of the wing are reduced significantly, and the area of the laminar flow region on the wing upper surface is increased by 24.3% compared with the baseline.
ISSN:2226-4310
DOI:10.3390/aerospace12121076
Fuente:Advanced Technologies & Aerospace Database