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http://dx.doi.org/10.20910/JASE.2022.16.4.17

Deep learning-based Approach for Prediction of Airfoil Aerodynamic Performance  

Cheon, Seongwoo (Department of AeroSpace Engineering, Jeonbuk National University)
Jeong, Hojin (Department of AeroSpace Engineering, Jeonbuk National University)
Park, Mingyu (ISAE-SUPAERO)
Jeong, Inho (Department of AeroSpace Engineering, Jeonbuk National University)
Cho, Haeseong (Department of AeroSpace Engineering, Jeonbuk National University)
Ki, Youngjung (Korea Aerospace Research Institute)
Publication Information
Journal of Aerospace System Engineering / v.16, no.4, 2022 , pp. 17-27 More about this Journal
Abstract
In this study, a deep learning-based network that can predict the aerodynamic characteristics of airfoils was designed, and the feasibility of the proposed network was confirmed by applying aerodynamic data generated by Xfoil. The prediction of aerodynamic characteristics according to the variation of airfoil thickness was performed. Considering the angle of attack, the coordinate data of an airfoil is converted into image data using signed distance function. Additionally, the distribution of the pressure coefficient on airfoil is expressed as reduced data via proper orthogonal decomposition, and it was used as the output of the proposed network. The test data were constructed to evaluate the interpolation and extrapolation performance of the proposed network. As a result, the coefficients of determination of the lift coefficient and moment coefficient were confirmed, and it was found that the proposed network shows benign performance for the interpolation test data, when compared to that of the extrapolation test data.
Keywords
Convolutional Neural Network; Proper Orthogonal Decomposition; Airfoil; Aerodynamic Coefficient; Pressure Coefficient; Signed Distance Function;
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1 K. Balla, R. Sevilla, O. Hassan and K. Morgan, "An Application of Neural Networks to the Prediction of Aerodynamic Coefficients of Aerofoils and Wings," Applied Mathematical Modelling, vol. 96, pp. 456-479, Aug. 2021   DOI
2 S. P. Pan, and Q. Yang, "A survey on transfer learning," IEEE Transactions on knowledge and data engineering, vol. 22, no. 10, pp. 1345-1359   DOI
3 G. Berkooz, P. Holmes, and J. L. Lumley, "The proper orthogonal decomposition in the analysis of turbulent flows," Annual review of fluid mechanics, vol. 25, no. 1, pp. 539-575, 1993   DOI
4 T. Murata, K. Fukami, and K. Fukagata, "Nonlinear mode decomposition with convolutional neural networks for luid dynamics," Journal of Fluid Mechanics, vol. 882, Jan. 2020
5 R. Shrestha, M. Benedict, V. Hrishikeshavan and I. Chopra, "Hover performance of a small-scale helicopter rotor for flying on mars," Journal of Aircraft, vol. 53, no. 4, pp. 1160-1167, May 2016   DOI
6 J. Winslow, H. Otsuka, B. Govindarajan and I. Chopra, "Basic Understanding of Airfoil Characteristics at Low Reynolds Numbers (104 - 105)," Journal of Aircraft, vol. 55, no. 3 pp.1050-1061, Dec. 2018   DOI
7 S. Bhatnagar, Y. Afshar, S. Pan, K. Duraisamy and S. Kaushik, "Prediction of Aerodynamic Flow Fields Using Convolutional Neural Networks," Computational Mechanics, vol. 64, no. 2, pp. 525-545, June 2019   DOI
8 D. P. Kingma, and J. Ba, "Adam: Amethod for stochastic optimization," arXiv preprint arXiv:1412.6980, Dec. 2014
9 A. F. Agarap, "Deep learning using rectified linear units(relu)," arXiv preprint arXiv: 1803.08375, Mar. 2018
10 S. Walton, O. Hassan, K. Morgan, "Reduced order modelling for unsteady fluid flow using proper orthogonal decomposition and radial basis functions," Applied Mathematical Modelling, vol. 37, no. 20-21, pp. 8930-8945, Nov. 2013   DOI
11 M. Drela, "Xfoil: An Analysis and Design System for Low Reynolds Number Airfoils," Low Reynolds number aerodynamics, pp. 1-12, Springer, Berlin, 1989.
12 J.-S. Wang and J.-J. Wang, "Wake-Induced Transition in the Low-Reynolds-Number Flow over a Multi-Element Airfoil," Journal of Fluid Mechanics, vol. 915, March 2021.
13 S. Wang, Y. Zhou, M. M. Alam and H. Yang, "Turbulent Intensity and Reynolds Number Effects on an Airfoil at Low Reynolds Numbers," Physics of Fluids, vol. 26, no. 11, p. 115107, Nov. 2014
14 E. Yilmaz, and B. German, "A Convolutional Neural Network Approach to Training Predctors for Airfoil Performance," 18th AIAA/ISSMO multidisciplinary analysis and optimization conference, Denver, Colorado, p. 3660, Jun. 2017
15 C. Duru, H. Alemdar, and O. U. Baran, "A deep learning approach for the transonic flow field predictions around airfoils," Computers & Fluids, vol. 236, p.105312, Mar. 2022
16 Y. Zhang, W. J. Sung and D. N. Mavris, "Application of convolutional neural network to predict airfoil lift coefficient," AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference 2018, Kissimmee, Florida, p. 1903, Jan. 2018
17 D. Scherer, A. Mulle,and S. Behnke, "Evaluation of pooling operations in convolutional architectures for object recognition," International conference on artificial neural networks, Thessaloniki, Greece, pp. 92-101, Sep. 2010