Since 1963
ARTICLE
Feed-forward artificial neural network as surrogate model to predict lift and drag coefficient of NACA airfoil and searching of maximum lift-to-drag ratio
1
Military University of Technology, Faculty of Mechatronics, Armament and Aerospace, Warsaw, Poland
Submission date: 2023-10-31
Final revision date: 2023-12-20
Acceptance date: 2024-06-07
Online publication date: 2024-07-31
Publication date: 2024-07-31
Corresponding author
Borys Syta
Faculty of Mechatronics, Armament and Aerospace, Institute of Aviation Technology, Military University of Technology, gen. Sylwestra Kaliskiego 2, 00-908, Warsaw, Poland
Faculty of Mechatronics, Armament and Aerospace, Institute of Aviation Technology, Military University of Technology, gen. Sylwestra Kaliskiego 2, 00-908, Warsaw, Poland
Journal of Theoretical and Applied Mechanics 2024;62(3):521-534
KEYWORDS
TOPICS
ABSTRACT
The problem of computation time in numerical calculations of aerodynamics has been studied
by many research centres. In this work, a feed forward artificial neural network (FF-ANN)
was used to determine the dependence of lift and drag coefficients on the angle of attack for
NACA four-digit families. A panel method was used to generate the data needed to train
the FF-ANNs. Optimisation using a genetic algorithm and a neural metamodel resulted
in a non-standard NACA aerofoil for which the optimal angle of attack was determined
with a maximum L/D ratio. The optimisation results were validated using the finite volume
method.
REFERENCES (26)
1.
Abbott H., Doenhoff E., Lous S., Stivers J., 1945, Summary of airfoil data, National Ad-visory Commitee for Aeronautics Report, 824.
2.
Aramendia I., Fernandez-Gamiz U., Zulueta E., Saenz-Aguirre A., Teso-Fz-Betoño D., 2019, Parametric study of a Gurney flap implementation in a DU91W (2) 250 airfoil, Energies, 12, 2, 294.
3.
Bakewell H.P., Lumley J.L., 1967, Viscous sublayer and adjacent wall region in turbulent pipe flow, Physics Fluids, 10, 1880-1889.
4.
Berkooz G., Holmes P., Lumley J.L., 1993, The proper orthogonal decomposition in the analysis of turbulent flows, Annual Review of Fluid Mechanics, 25, 539-575.
5.
Butterweck A., Głuch J., 2014, Neural network simulator’s application to reference performance determination of turbine blading in the heat-flow diagnostics, [In:] Intelligent Systems in Technical and Medical Diagnostics, Springer, Berlin, Heidelberg.
6.
Conn A.R., Gould N.I. Toint P., 1991, A globally convergent augmented Lagrangian algorithm for optimization with general constraints and simple bounds, SIAM Journal on Numerical Analysis, 28, 2, 545-572.
7.
Dhileep K., et al., 2020, Numerical study of camber morphing in NACA0012 airfoil, AIAA Aviation 2020 Forum, 2781.
8.
Drela M., 1989, XFOIL: An analysis and design system for low Reynolds number airfoils, [In:] Low Reynolds Number Aerodynamics. Proceedings of the Conference Noire Dame, Indiana, USA, Springer Berlin Heidelberg, 1-12
9.
Fukami K., Fukagata K., Taira K., 2021, Machine-learning-based spatio-temporal super resolution reconstruction of turbulent flows, Journal of Fluid Mechanics, 909.
10.
Günel O., Ko E. Yavuz T. 2016, CFD vs. XFOIL of airfoil analysis at low Reynolds numbers, IEEE International Conference on Renewable Energy Research and Applications (ICRERA), 628-632.
11.
Hsiao F.B., Bai C.J., Chong W.T., 2013, The performance test of three different horizontal axis wind turbine (HAWT) blade shapes using experimental and numerical methods, Energies, 6, 6, 2784-2803.
12.
https://web.stanford.edu/ cantwell/AA200 Course Material/The%20NACA%20airfoil%20series.pdf, [Online 2021.10.25].
13.
Khan A.Y., Ahmad Z., Sultan T., Alshahrani S., Hayat K., Imran M., 2022, Optimization of photovoltaic panel array configurations to reduce lift force using genetic algorithm and CFD, Energies, 15, 24, 9580.
14.
Kharal A., Saleem A., 2012, Neural networks based airfoil generation for a given Cp using Bezier-PARSEC parameterization, Aerospace Science and Technology, 23, 1, 330-344.
15.
López-Briones Y.F., Sánchez-Rivera L.M., Arias-Montano A., 2020, Aerodynamic analysis for the mathematical model of a dual-system UAV, 17th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE).
17.
Porta Ko A., Smidt S., Schmehl R., Mandru M., 2023, Optimisation of a multi-element airfoil for a fixed-wing airborne wind energy system, Energies, 16, 8, 3521.
18.
Saad M.M.M., Mohd S.B., Zulkafli M.F., Shibani W.M, 2017, Numerical analysis for comparison of aerodynamic characteristics of six airfoils, AIP Conference Proceedings, AIP Publishing.
19.
San O., Maulik R., 2018, Neural network closures for nonlinear model order reduction, Advances in Computational Mathematics, 44, 1717-1750.
20.
Sessarego M., Feng J., Ramos-García N., Horcas S.G., 2020, Design optimization of a curved wind turbine blade using neural networks and an aero-elastic vortex method under turbulent inflow, Renewable Energy, 146, 1524-1535.
21.
Sekar V., Jiang Q., Shu, C., Khoo B.C., 2019, Fast flow field prediction over airfoils using deep learning approach, Physics of Fluids, 31, 5, 057103.
22.
Sobieczky H., 1999, Parametric airfoils and wings, [In:] Recent Development of Aerodynamic Design Methodologies, Fujii K., Dulikravich G.S. (Edit.), Notes on Numerical Fluid Mechanics (NNFM), vol 65, Vieweg+Teubner Verlag.
23.
Sun G., Sun Y., Wang S., 2015, Artificial neural network based inverse design: Airfoils and wings, Aerospace Science and Technology, 42, 415-428.
24.
Thirumalainambi R., Bardina J., 2003, Training data requirement for a neural network to predict aerodynamic coefficients, [In:] Independent Component Analyses, Wavelets, and Neural Networks, International Society for Optics and Photonics, 92-103.
25.
Verma N., Baloni B.D., 2021, Artificial neural network-based meta-models for predicting the aerodynamic characteristics of two-dimensional airfoils for small horizontal axis wind turbine, Clean Technologies and Environmental Policy, 24, 2, 563-577.
26.
Viquerat J., Hachem E., 2020, A supervised neural network for drag prediction of arbitrary 2D shapes in laminar flows at low Reynolds number, Computers and Fluids, 210, 104645.
Share
RELATED ARTICLE
| eISSN: | 2543-6309 |
| ISSN: | 1429-2955 |
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
