Intelligent data-driven aerodynamic analysis and optimization of morphing configurations

In this paper, we develop an online, data-based framework for the aircraft airfoil to be able to optimally morph vertically. The proposed framework combines data-driven analysis, optimization, and control-theoretic tools to optimally morph airfoils while guaranteeing efficiency and safety. It incorp...

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Bibliographic Details
Published in:Aerospace science and technology 2022-02, Vol.121, p.107388, Article 107388
Main Authors: Magalhães Júnior, José M., Halila, Gustavo L.O., Kim, Yoobin, Khamvilai, Thanakorn, Vamvoudakis, Kyriakos G.
Format: Article
Language:English
Subjects:
Aerodynamic design
Aerodynamic shape optimization
Data-driven aerodynamic design
Model predictive control
Neural networks
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Summary:In this paper, we develop an online, data-based framework for the aircraft airfoil to be able to optimally morph vertically. The proposed framework combines data-driven analysis, optimization, and control-theoretic tools to optimally morph airfoils while guaranteeing efficiency and safety. It incorporates a surrogate model that is based on a deep neural network that is used to predict the aerodynamic coefficients while a meta-heuristic optimization algorithm is employed to find shapes with reduced value of drag coefficient that fulfill the geometric and lift constraints. Finally, a data-driven shape controller is used to morph the airfoil while following smooth trajectories and small aerodynamic coefficient variations. Experimental numerical results show the efficacy of the proposed framework for different flight conditions.
ISSN:1270-9638
1626-3219
DOI:10.1016/j.ast.2022.107388