Modeling aerodynamic characteristics of a wing airfoil using artificial neural networks
DOI:
https://doi.org/10.7242/2658-705X/2025.4.6Keywords:
artificial neural networks, aerodynamic coefficients, inverse problem, NACA, airfoilAbstract
The paper considers the application of artificial neural networks to solve the direct and inverse aerodynamic modeling problems using the example of a two-dimensional NACA2415 wing profile. Based on the numerical solution of the steady-state Navier–Stokes equations, a training dataset is formed, which includes aerodynamic lift and drag coefficients for various values of the geometric
parameters and angle of attack. A neural network with two hidden layers of 10 neurons and a sigmoid activation function is built and trained on datasets with regular and random distribution of parameters. The feasibility of solving the inverse problem of recovering the geometric parameters of an airfoil and angle of attack from given aerodynamic coefficients with an error of no more than 5% is demonstrated. The results confirm the effectiveness of neural networks for modeling and inverse design of aerodynamic profiles.
References
Rai M.M., Madavan N.K. Aerodynamic design using neural networks // AIAA Journal. – 2000. – Vol. 38, No. 1. – P. 173–182. DOI: 10.2514/2.938
Sun G., Sun Y., Wang S. Artificial neural network based inverse design: Airfoils and wings // Aerospace Science and Technology. – 2015. – Vol. 42. – P. 415–428. DOI: 10.1016/j.ast.2015.01.030
Sekar V., Zhang M., Shu C., Khoo B.C. Inverse design of airfoil using a deep convolutional neural network // AIAA Journal. – 2019. – Vol. 57, No. 3. – P. 993–1003. DOI: 10.2514/1.J057894
Du X., Ren J., Leifsson L. Aerodynamic inverse design using multifidelity models and manifold mapping // Aerospace Science and Technology. – 2019. – Vol. 85. – P. 371–385. DOI: 10.1016/j.ast.2018.12.008
Li J., Du X., Martins J.R.R.A. Machine learning in aerodynamic shape optimization // Progress in Aerospace Sciences. – 2022. – Vol. 134. – 100849. DOI: 10.1016/j.paerosci.2022.100849
Samareh J.A. Survey of shape parameterization techniques for high-fidelity multidisciplinary shape optimization // AIAA Journal. – 2001. – Vol. 39, No. 5. – P. 877–884. DOI: 10.2514/2.1391
Kulfan B.M. Universal parametric geometry representation method // Journal of Aircraft. – 2008. – Vol. 45, No. 1. – P. 142–158. DOI: 10.2514/1.29958
Kharal A., Saleem A. Neural networks based airfoil generation for a given Cp using Bezier–PARSEC parameterization // Aerospace Science and Technology. – 2012. – Vol. 23. – P. 330–344. DOI: 10.1016/j.ast.2011.08.010
Jacobs E.N., Ward K.E., Pinkerton R.M. The characteristics of 78 related airfoil sections from tests in the variable-density wind tunnel // NACA Report. – 1933. – No. 460 https://ntrs.nasa.gov/api/citations/19930091108/downloads/19930091108.pdf
