Date of Award


Document Type


Degree Name

Master of Science


Department of Aeronautics and Astronautics

First Advisor

Stuart C. Kramer, PhD


Automated techniques for selecting jet engines that minimize overall fuel consumption for a given aircraft mission have recently been developed. However, the current techniques lack the efficiency required by Wright Laboratories. Two noted dependencies between turbine engine fan pressure ratio, bypass ratio, high pressure compressor pressure ratio and overall engine mass flow allows for a reduction in the number of independent design variables searched in the optimization process. Additionally, through the use of spatial statistics (specifically kriging estimation), it is possible to significantly reduce the number of time consuming response function evaluations required to obtain an optimal combination of engine parameters. A micro Genetic Algorithm (microGA) is employed to perform the nonlinear optimization process with these two computation saving techniques. Optimal engine solutions were obtained. in 25 percent of the time required by previous automated search algorithms.

AFIT Designator


DTIC Accession Number