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Date of Award

Summer 8-2015

Author's School

School of Engineering & Applied Science

Author's Department

Mechanical Engineering & Materials Science

Degree Name

Master of Science (MS)

Degree Type

Thesis

Abstract

The focus of this thesis is on the shape optimization of the Busemann-type biplane airfoil for drag reduction under both nonlifting and lifting conditions using genetic algorithms. The concept of the Busemann-type biplane airfoil was first introduced by Adolf Busemann in 1935. Under its design condition at a specific supersonic flow speed, the Busemann biplane airfoil eliminates all wave drag due to its symmetrical biplane configuration; however it produces zero lift. Previous research has shown that the original Busemann biplane airfoil design has a poor performance under off-design conditions as well. In order to solve this problem of zero lift and to improve the off-design-condition performance of the biplane airfoil, shape optimization of the asymmetric biplane airfoil is performed to minimize the drag while maximizing the lift. In this thesis, the commercially available CFD solver ANSYS FLUENT is employed for computing the inviscid flow past the biplane airfoil. An unstructured mesh is generated using ICEM software. A second-order accurate steady density-based solver is employed to compute the supersonic flow field. A single-objective genetic algorithm (SOGA) is employed to optimize the Busemann biplane airfoil shape under nonlifting condition to minimize the drag coefficient and a multi-objective genetic algorithm (MOGA) is employed to optimize the Busemann biplane airfoil shape under lifting condition to maximize both the lift coefficient and the lift to drag ratio simultaneously. Both results obtained by using SOGA and MOGA show significant improvement in the design and off-design-condition performance of the optimized Busemann biplane airfoil compared to the original one.

Language

English (en)

Chair

Ramesh Agarwal

Committee Members

David Peters, Swami Karunamoorthy

Comments

Permanent URL: http://dx.doi.org/10.7936/K79P2ZT1

Available for download on Wednesday, August 15, 2018

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