Browse > Article
http://dx.doi.org/10.12989/acd.2020.5.3.291

Predicting the 2-dimensional airfoil by using machine learning methods  

Thinakaran, K. (Computer Science Engeneering., Saveetha School of Engineering, SIMATS)
Rajasekar, R. (Aeronautical Engineering, MVJ Engineering College)
Santhi, K. (Sreenivasa Institute of Technology and Management Studies)
Nalini, M. (Computer Science Engeneering., Saveetha School of Engineering, SIMATS)
Publication Information
Advances in Computational Design / v.5, no.3, 2020 , pp. 291-304 More about this Journal
Abstract
In this paper, we develop models to design the airfoil using Multilayer Feed-forward Artificial Neural Network (MFANN) and Support Vector Regression model (SVR). The aerodynamic coefficients corresponding to series of airfoil are stored in a database along with the airfoil coordinates. A neural network is created with aerodynamic coefficient as input to produce the airfoil coordinates as output. The performance of the models have been evaluated. The results show that the SVR model yields the lowest prediction error.
Keywords
support vector regression model; neural networks; airfoil design; inverse design; backpropagation;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
연도 인용수 순위
1 Zayid, E.I.M. and Akay, M.F. (2012), "Predicting the performance measures of a message-passing multiprocessor architecture using artificial neural networks", Neural Comput. Appl., 23(7-8), 2481-2491. https://doi.or/10.1007/s00521-012-1267-9.   DOI
2 Abid, F.F. and Najim, M. (2001), "A fast feedforward training algorithm using a modified form of the standard backpropagation algorithm", IEEE Trans. Neural Networks, 12, 424-430.   DOI
3 Allen, M. and Maute, K. (2004), "Reliability-Based Design Optimization of Aeroelastic Structures", Struct. Multidisciplinary Optimization, 27(4), 228-242. https://doi.org/10.1007/s00158-004-0384-1.   DOI
4 Kurtoglu, A.E. (2018), "Patch load resistance of longitudinally stiffened webs: Modeling via support vector machines", Steel Compos. Struct., 29(3).
5 Cristianini, N. and Shawe-Taylor, J. (2000), An Introduction to Support Vector Machines and Other Kernel-based Learning Methods, Cambridge University Press.
6 Dowell, E. and Tang, D. (2002), "Nonlinear aero elasticity and unsteady aerodynamics", AIAA J., 40(9), 1697-1707. https://doi.org/10.2514/2.1853.   DOI
7 Finlay, D.D., Nugent, C.D., McCullagh, P.J., Black, N.D. and Lopez, J.A. (2003), "Evaluation of a statistical prediction model used in the design of neural network based ECG classifiers: A multiple linear regression approach", 258-26.
8 Garabedian, P.R. and Korn, D.G. (1971), "Numerical design of transonic airfoils", Numerical Solution of Differential Equations - II, Academic Press, Elsevier, Germany.
9 Genbrugged, D. and Eeckhout, L. (2007), "Statistical simulation of chip multiprocessors running multi-program workloads", Proceedings of 25th International Conference on Computer Design, IEEE, 464-471.
10 Han, F., Ling, Q.H. and D.S. Huang (2008), "Modified constrained learning algorithms incorporating additional functional constraints into neural networks", Information Sci., 178, 907-919.   DOI
11 Hertz, J., Krogh, A. and Palmer, R.G., (1991), Introduction to the Theory of Neural Computation, Addison-Wesley Publishing Co., California, USA.
12 Alpaydin, E. (2004), Introduction to Machine Learning, MIT Press, Cambridge, USA.
13 Jeong, S.Y. and Lee, S.Y. (2000), "Adaptive learning algorithms to incorporate additional functional constraints into neural networks", Neuro Comput., 35,73-90.
14 Fan, J. (2014), "Accelerating the modified Levenberg-Marquardt method for nonlinear equations", Math. Comp., 1173-1187.
15 Lee, B., Jiang, L. and Wong, Y. (1998), "Flutter of an Airfoil with Cubic Restoring Force, Journal of Fluids and Structures", 13(1), 75-101. https://doi.org/10.1006/jfls.1998.0190.   DOI
16 Maute, K., Nikbay, M. and Farhat, C. (2003), "Sensitivity analysis and design optimization of three-dimensional nonlinear aeroelastic systems by the adjoint method", J. Numerical Methods Eng., 56(6), 911-933.   DOI
17 Patternson, D.W. (1996), Artificial Neural Network: Theory and Applications, Prentice-Hall, Englewood Cliffs, NJ, USA.
18 Saltan, M. and Terzi, S. (2008), "Modeling deflection basin using artificial neural networks with cross-validation technique in backcalculating flexible pavement layer moduli", Adv. Eng. Software, 39(7), 588-592, https://doi.org/10.1016/j.advengsoft.2007.06.002.   DOI
19 Pettit, C. (2004), "Uncertainty quantification in aeroelasticity-Recent results and research challenges", J. Aircraft, 41(5), 1217-1229. https://doi.org/10.2514/1.3961.   DOI
20 Pires, J.C.M., Martins, F.G., Sousa, S.I.V. and Alvim, M.C.M. (2008), "Pereira M.C. Selection and validation of parameters in multiple linear and principal component regressions", Environ. Modelling Software, 23(1), 50-55. https://doi.org/10.1016/j.envsoft.2007.04.012.   DOI
21 Sejnowski, T. and Rosenberg, C. (1987), "Parallel networks that learn to pronounce English text", Complex Syst., 1, 143-168.
22 Kousen, K. and Bendiksen, O. (1994), "Limit cycle phenomena in computational transonic aeroelasticity," J. Aircraft, 31(6), 1257-1263. https://doi.org/10.2514/3.46644.   DOI
23 Dutta, S., Samui, P. and Kim, D. (2018), "Comparison of machine learning techniques to predict compressive strength of concrete", Comput. Concrete, 21(4), 463-470. https://doi.org/10.12989/cac.2018.21.4.463.   DOI