Transactions of Nanjing University of Aeronautics & Astronautics
Integrated Aerodynamic and Stealth Design of Wing Airfoil Based on Generative Model
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Abstract:
Generative design methods have been widely applied in modern aircraft aerodynamic design. However， the integrated optimization of aerodynamic and stealth performance in aircraft still relies on surrogate models and multi-objective optimization algorithms. To address the complex verification and optimization procedures in current integrated aerodynamic-stealth aircraft design， this paper proposes a rapid generative design method based on a conditional denoising diffusion probability model （CDDPM）. First， the class-shape transformation （CST） method is employed for parametric modeling of airfoils. To build the aerodynamic and stealth performance datasets， the vortex lattice method and the physical optics method for large-sized objects are used to compute the lift-to-drag ratio （L/D） and radar cross-section （RCS）， respectively. Based on the dataset， a generative conditional diffusion model is implemented to achieve the mapping relationship from target performance （L/D and RCS） to CST parameters of wing airfoils. Validation results indicate that the prediction errors for the generative model in aerodynamic-stealth performance are smaller than 6%. Meanwhile， the generated airfoils exhibit notable diversity. Furthermore， optimization design of airfoils considering both aerodynamic and stealth performance is conducted， where the diffusion model is utilized to generate new airfoils to expand the design space. The pareto front is obviously expanded with the minimum RCS decreased by 28.6%， and the maximum L/D increased by 7.5%. This study establishes a generative model-based framework for rapid aerodynamic-stealth optimization of airfoils， laying a foundation for AI-driven multidisciplinary design optimization （MDO） in aircraft design.
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Project Supported:
This work was supported by the Fundamental Research Funds for the Central Universities （No.226-2025-00075）， the Defense Industrial Technology Development Program （No.JCKY2023205B013）； and the Leading Talent Project for Scientific and Technological Innovation in Zhejiang Province （No.2023R5220）.
Clc Number:
V211.41
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