Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/654
Title: Optimization of Airfoil Shapes for Maximum Lift-to-Drag Coefficient Using Genetic Algorithms
Authors: Firas Ziad Saleh Hamadeh 
Supervisor: Dr. Raed I. Bourisli
Keywords: Airfoil : Genetic Algorithms
Issue Date: 2017
Publisher:  Kuwait university - college of graduate studies
Abstract: Nowadays, the booming interest in green energy such as solar, wind and their other derivatives is deemed essential to the well-being of the planet and the future of the species. This is due to the fact that emission of fossil fuels and their byproducts have direct negative impact on the environment. The more efficient green sources of energy become, the less fossil fuels we have to use. This thesis focuses on ways that more energy can be generated by wind, since it is one of the major sources of green energy. One of the major elements in designing an airfoil is having a shape that delivers the highest possible aerodynamic outcome, i.e., higher energy harvested from the wind. However, the optimal shape design for one location is not necessarily the optimal one for another, because the wind speed distribution and direction may vary between locations. In addition, the specific application of the airfoil plays a critical role in its “optimum” design. The turbine with highest efficiency in one location and wind conditions is not necessarily the optimal turbine for other conditions. The efficiency and cost of a wind turbine depends on how the various design parameters match location’s wind speed, conditions and direction. The aim here is to find the optimum airfoil that delivers the maximum lift-todrag ratio for a given wind speed and direction by changing the MPTH number (Maximum Camber, Position of Maximum Camber and Maximum Thickness) as well as the coefficients of the thickness function of the NACA four-digit airfoils. A series of MATLAB routines are used that run a genetic algorithm to optimize the shape of the airfoil for application in a low-speed wind turbine. Genetic Algorithms follow the Darwinian principle of natural selection. The Algorithm uses the ‘Fluent’ computational fluid dynamics (CFD) code to differentiate between airfoils based on their lift-to-drag ratio. The optimization study resulted in enhancements of up to 20% in lift-to-drag ratio. Results include a number of optimized airfoil shapes for different flow conditions and angles of attack (AOA). The methodology proposed here, namely, linking GA with Fluent as a fitness function, proves useful in topological optimization in any number of fluid and heat flow applications.
URI: http://hdl.handle.net/123456789/654
Appears in Programs:0630 Mechanical Engineering

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