Inverse Airfoil Design Using Attention-Enhanced Deep Neural Networks

Date

2025-01-01

Author

Eriş, Görkem Mahir
Özgören, Ahmet Can
Uzol, Oğuz

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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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.

Subject Keywords

Aerodynamic Characteristics, Aerodynamic Performance, Airfoil Databases, Airfoil Geometry, Computational Fluid Dynamics, Design Optimization, Generative Adversarial Network, Lift to Drag Ratio, Machine Learning, Wind Turbine Airfoil

URI

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=105017845215&origin=inward
https://hdl.handle.net/11511/116391

Collections

Department of Aerospace Engineering, Conference / Seminar