This method focuses on the overall methodology and a mesh generated in other application has been used. The first method uses simpleFoam to demonstrate steady state and incompressible flow over the aerofoil. For example, the tutorial section demonstrates two methods. The construction of OpenFOAM cases for flow over an aerofoil can be done in many ways. Keane, Recent advances in surrogate-based optimization.External Aerodynamics: Parametric modeling of of an airfoil using blockMeshDict in OpenFOAM Flow over Aerofoil Balu, Recent developments and challenges in surrogate model based optimal design of engineering systems. Jiang, Aerodynamic optimization design of airfoil based on particle swarm optimization. Kennedy, A new optimizer using particle swarm theory, in Proceedings of IEEE International Symposium on Micro Machine and Human Science (Nagoya, Japan, 1995), pp. Eberhart, Particle swarm optimization, in Proceedings of IEEE International Conference on Neural Networks, vol. Plotkin, Low-Speed Aerodynamics from Wing Theory to Panel Methods (McGraw-Hill Inc, New York, 1991) Hess, Panel methods in computational fluid mechanics. 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 Sobieczky, Parametric Airfoils and Wings, in Notes on Numerical Fluid Mechanics (Vieweg, 1998), pp. Castonguay, Effect of shape parameterization on aerodynamic shape optimization, in 45th AIAA Aerospace Science Meeting and Exhibit, Reno, Nevada, 8–11 January (2007) Department of Mechanical Engineering, College of Engineering, Thiruvananthapuram, Kerala, India (2010) Anil Lal, Inverse design of airfoil using vortex element method. The method of coupling the SMs with a suitable optimisation scheme to carryout an aerodynamic design optimisation process for airfoil shapes is also discussed. 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 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. Various approaches such as Kriging, neural networks, polynomials, Gaussian processes etc are used to construct the approximation models. 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. 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. In most of the cases, the computational resources and time required per simulation are large. 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.
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