Abstract
Engineering design problems always require enormous amount of real-time experiments and computational simulations in order to assess and ensure the design objectives of the problems subject to various constraints. In most of the cases, the computational resources and time required per simulation are large. In certain cases like sensitivity analysis, design optimisation etc where thousands and millions of simulations have to be carried out, it leads to have a life time of difficulty for designers. Nowadays approximation models, otherwise called as surrogate models (SM), are more widely employed in order to reduce the requirement of computational resources and time in analysing various engineering systems. Various approaches such as Kriging, neural networks, polynomials, Gaussian processes etc are used to construct the approximation models. The primary intention of this work is to employ the k-fold cross validation approach to study and evaluate the influence of various theoretical variogram models on the accuracy of the surrogate model construction. Ordinary Kriging and design of experiments (DOE) approaches are used to construct the SMs by approximating panel and viscous solution algorithms which are primarily used to solve the flow around airfoils and aircraft wings. The method of coupling the SMs with a suitable optimisation scheme to carryout an aerodynamic design optimisation process for airfoil shapes is also discussed.
Similar content being viewed by others
References
G.S. Avinash, S. Anil Lal, Inverse design of airfoil using vortex element method. Department of Mechanical Engineering, College of Engineering, Thiruvananthapuram, Kerala, India (2010)
S. Nadarajah, P. Castonguay, Effect of shape parameterization on aerodynamic shape optimization, in 45th AIAA Aerospace Science Meeting and Exhibit, Reno, Nevada, 8–11 January (2007)
H. Sobieczky, Parametric Airfoils and Wings, in Notes on Numerical Fluid Mechanics (Vieweg, 1998), pp. 71–88
J. Hajek, Parameterization of airfoils and its application in aerodynamic optimization, in WDS’07 Proceedings of Contributed Papers, Part I, ISBN 978-80-7378-023-4, 233–240, 2007
J.L. Hess, Panel methods in computational fluid mechanics. Annu. Rev. Fluid Mech. 22, 255–274 (1990)
J. Katz, A. Plotkin, Low-Speed Aerodynamics from Wing Theory to Panel Methods (McGraw-Hill Inc, New York, 1991)
J. Kennedy, R.C. Eberhart, Particle swarm optimization, in Proceedings of IEEE International Conference on Neural Networks, vol. 4 (Perth, Australia, 1995), pp. 1942–1948
R.C. Eberhart, J. Kennedy, A new optimizer using particle swarm theory, in Proceedings of IEEE International Symposium on Micro Machine and Human Science (Nagoya, Japan, 1995), pp. 39–43
P. Xu, C. Jiang, Aerodynamic optimization design of airfoil based on particle swarm optimization. Aircr. Des. 28(5), 6–9 (2008)
R. Balu, Recent developments and challenges in surrogate model based optimal design of engineering systems. J. Emerg. Technol. Mech. Sci. Eng. 13–26 (2010), ISSN NO: 0976-2558
A.I.J. Forrester, A.J. Keane, Recent advances in surrogate-based optimization. Computational Engineering and Design Group, School of Engineering Sciences, University of Southampton SO17 1BJ, UK (2009)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Mukesh, R., Lingadurai, K. & Selvakumar, U. Airfoil Shape Optimization based on Surrogate Model. J. Inst. Eng. India Ser. C 99, 1–8 (2018). https://doi.org/10.1007/s40032-017-0382-x
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40032-017-0382-x