Aerodynamic shape optimization using a time spectral coupled adjoint for nonlinear aeroelastic problems

This paper presents a machine learning-based airfoil shape optimization framework designed to increase flutter resistance and reduce drag. Using the National Advisory Committee for Aeronautics airfoil as the base design and a Hicks–Henne bump function, we employ multi-objective Bayesian optimization and harmonic balance-based flutter prediction. The optimization process yields a Pareto front revealing trade-off relationships between the flutter speed index and drag coefficient. The optimized airfoils, resembling those of evolved marine animals, outperform the base design in terms of flutter resistance and drag. These results demonstrate the framework's potential to enhance aircraft performance and safety by addressing aeroelastic factors.

show abstract

Overview of Computational Methods to Predict Flutter in Aircraft

Antimirova,

Jung,

Zhang

et al. 2024

Journal of Applied Mechanics

View full text Add to dashboard Cite

Aeroelastic flutter is a dynamically complex phenomenon that has adverse and unstable effects on elastic structures. It is crucial to better predict the phenomenon of flutter within the scope of aircraft structures to improve upon the design of their wings. This review aims to establish fundamental guidelines for flutter analysis across subsonic, transonic, supersonic, and hypersonic flow regimes providing a thorough overview of established analytic, numerical, and reduced-order models as applicable to each flow regime. The review will shed light on the limitations and missing components within the previous literature on these flow regimes by highlighting the challenges involved in simulating flutter. Additionally, popular methods that employ the aforementioned analyses for optimizing wing structures under the effects of flutter, a subject currently garnering significant research attention, are also discussed. Our discussion offers new perspectives that encourages collaborative effort in the area of computational methods for flutter prediction and optimization.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Aerodynamic shape optimization using a time spectral coupled adjoint for nonlinear aeroelastic problems

Cited by 6 publications

References 28 publications

Nonlinear stochastic flutter analysis of a three-degree-of-freedom wing in a two-dimensional flow field under stochastic perturbations

Nonlinear stochastic flutter analysis of a three-degree-of-freedom wing in a two-dimensional flow field under stochastic perturbations

Data-driven airfoil shape optimization framework for enhanced flutter performance

Overview of Computational Methods to Predict Flutter in Aircraft

Contact Info

Product

Resources

About