Modeling and Multi-Objective Optimization of 4-Digit NACA Airfoils Using Genetic Algorithms
نویسندگان
چکیده
In the present study, multi-objective optimization of 4-digit NACA airfoils is performed at three steps. At the first step, lift (CL) and drag (CD) coefficient in a set of 4-digit NACA airfoils are numerically investigated using commercial software NUMECA. Two meta-models based on the evolved Group Method of Data Handling (GMDH) type neural networks are obtained, at the second step, for modeling of CL and CD with respect to geometrical design variables and the angle of attack. Finally, using obtained polynomial neural networks, multi-objective genetic algorithms are used for Pareto based optimization of 4-digit NACA airfoils considering two conflicting objectives, CL and CD. It is shown that some interesting and important relationships as useful optimal design principles involved in the performance of 4-digit NACA airfoils can be discovered by Pareto based multi-objective optimization of the obtained polynomial meta-models.
منابع مشابه
Applying evolutionary optimization on the airfoil design
In this paper, lift and drag coefficients were numerically investigated using NUMECA software in a set of 4-digit NACA airfoils. Two metamodels based on the evolved group method of data handling (GMDH) type neural networks were then obtained for modeling both lift coefficient (CL) and drag coefficient (CD) with respect to the geometrical design parameters. After using such obtained polynomial n...
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