Date of Award

Summer 8-2014

Author's Department

Mechanical Engineering & Materials Science

Degree Name

Master of Science (MS)

Degree Type



The focus of this thesis is on shape optimization using a genetic algorithm. A multi-objective genetic algorithm (MOGA) is employed to optimize the shape of an airfoil to improve its lift and drag characteristics, in particular to achieve two objectives simultaneously that is to increase its lift as well as its lift to drag ratio. The commercially available software FLUENT is employed to calculate the flow field on an adaptive structured mesh, which is generated by the commercial mesh generating software ICEM. The flow field is calculated using the Reynolds-Averaged Navier-Stokes (RANS) equations in conjunction with a two equation k-ω SST turbulence model. Bezier Curves are employed to generate airfoil shapes for a particular generation of the genetic algorithm; these shapes are tested by MOGA in conjunction with FLUENT to evaluate their fitness by calculating their lift and lift to drag ratio. The process is continued for a number of generations until the lift and lift to drag ratios converge to their optimal values. MOGA optimization method is used to optimize a well-known wind turbine airfoil S809 and NACA 4412 airfoil in ground effect. The results show significant improvement in both the lift coefficient and lift-to-drag ratio of the optimized airfoil compared to the original airfoil.


English (en)


Ramesh K Agarwal

Committee Members

Kenneth Jerina


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