Date of Award
Master of Science
Mechanical and Aerospace Engineering
Dr. Kapseong Ro
Dr. Jennifer Hudson
Dr. Tianshu Liu
Genetic, optimization, design, unmanned, aircraft
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.
Mull, "A Genetic Algorithm Incorporating Design Choice for the Preliminary Design of Unmanned Aerial Vehicles" (2016). Master's Theses. 746.