Pareto Optimization of Two-element Wing Models with Morphing Flap Using Computational Fluid Dynamics, Grouped Method of Data handling Artificial Neural Networks and Genetic Algorithms

A multi-objective optimization (MOO) of two-element wing models with morphing flap by using computational fluid dynamics (CFD) techniques, artificial neural networks (ANN), and non-dominated sorting genetic algorithms (NSGA II), is performed in this paper. At first, the domain is solved numerically in various two-element wing models with morphing flap using CFD techniques and lift (L) and drag (D) coefficients in wings are calculated. Afterward, for modeling L and D using grouped method of data handling (GMDH) type artificial neural networks, numerical data of the preceding step will be applied. Eventually, for Pareto based multi-objective optimization of two-element wing models with morphing flap using NSGA II algorithm, the modeling, which is accomplished by GMDH will be applied. It is shown that the achieved Pareto solution includes important design information on such wings.