Aeroelastic and structural similarity optimization of scaled wing models

• Κύριος συγγραφέας: Φιλίππου, Ευάγγελος
• Άλλοι συγγραφείς: Filippou, Evangelos
• Γλώσσα: English
• Έκδοση: 2023
• Θέματα: Aeroelasticity; NASA common research model; Scaling; Fluid-structure interaction; Design optimization; Αεροελαστικότητα; Κλιμάκωση πτερύγων; Αλληλεπίδραση ρευστού-στερεού; Βελτιστοποίηση σχεδίασης

The ongoing pursuit for more efficient transportation has led engineers to design lighter and blended wings in order to reduce fuel consumption and emissions. However, these designs introduce strong aeroelastic couplings that can result in failure. As a result, aeroelastic analysis and optimization have become crucial aspects of aircraft design in modern times. Additionally, aeroelastic testing of scaled models is another key phase in the development of aircrafts. The accurate prediction of aeroelastic response through the construction of models is vital for cost reduction and avoiding risky flight testing. This is achieved by maximizing the similarity of stiffness and mass distribution along with the flow field similarity during the creation of the scaled model. However, in today's lightweight aircrafts, the exact same geometry cannot be scaled down, requiring the use of a different configuration. This variation in configuration affects the aeroelastic response, making the use of computational aeroelasticity tools and optimization algorithms necessary. This computational technique constitutes a modern approach to aeroelastic scaling. This thesis focuses on developing an aeroelastic scaling framework using multidisciplinary optimization. Specifically, a parametric wing finite element model (FEM) is created that incorporates both thickness and geometry parameterization, primarily using shell elements. The aerodynamic loads are calculated using the DLM method along with twist and camber correction factors, and the coupling is established through the use of infinite plate splines. The aeroelastic model is then integrated with an Ant Colony Optimization (ACO) algorithm to achieve static and dynamic similarity between the scaled model and the reference wing. The key contribution of this thesis is the inclusion of internal geometry parameterization within the framework. The results of the implementation of a two-step optimization technique indicated high similarity in both the static aeroelastic and modal response of the two wings. The root mean square error (RMSE) of the in-flight shapes converged to 0.055m in the full scale and the mean MAC value of the first 5 modes was 0.934.