Inverse Airfoil Design Using Attention-Enhanced Deep Neural Networks
Year: 2025
Type: conference-paper
Abstract: This paper presents an attention-enhanced deep learning-based framework for airfoil inverse design that leverages the PARSEC parameterization methodology. A model is developed to predict the PARSEC parameters that define a given airfoil shape using various aerodynamic coefficients as inputs. The model is obtained by training a neural network utilizing an aerodynamic database generated using XFOIL. The proposed model successfully predicts airfoil shapes, demonstrating its ability to capture the relationship between aerodynamic coefficients and airfoil geometries. By leveraging the attention mechanism, the model identified and prioritized critical input features, enhancing its robustness. Results showed that the model could generalize well across a variety of airfoil shapes.
DOI: 10.2514/6.2025-3231
Authors: Gorkem Eris, Ahmet C. Ozgoren, Oguz Uzol
Institution: Middle East Technical University
Language: English
Subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4412486926