Date of Award


Degree Name

Master of Science


Mechanical and Aerospace Engineering

First Advisor

Dr. Kapseong Ro

Second Advisor

Dr. Jennifer Hudson

Third Advisor

Dr. Tianshu Liu


Genetic, optimization, design, unmanned, aircraft

Access Setting

Masters Thesis-Open Access


Unmanned Aerial Vehicles (UAVs) are currently at the forefront of aerospace technologies. The design of these aircraft is complex and often performance characteristics are coupled to multiple design attributes. At the early design phase both discrete and continuous design choices are present limiting the feasibility of traditional derivative based optimization techniques. In place of these methods, the design space can be explored using a genetic algorithm that mimics the process of natural selection, providing a capable and reliable base airframe constructed from the required performance metrics. By incorporating a genetic multidisciplinary optimization algorithm early in the conceptual design phase, aircraft can be moved faster and more cost effectively through the product development cycle thus reducing research and development costs, the time necessary to deliver a finished product, and the program and unit costs, while delivering a vehicle with superior performance characteristics.