Adaptive selection of engine technology solution sets from a large combinatorial space

Abstract

This paper describes a method to assist in selecting technology concepts from amongst a pool of candidates such that the resulting concepts yield the best compromise between conflicting sign performance and technology risk. The heart of this method is a unique technology impact forecasting environment that is used in conjunction with a genetic algorithm as a tool to efficiently explore the technology combinatorial space. The technique is applied to a commercial turbofan engine technology selection problem of practical interest. A pool of forty technology concepts is proposed and evaluated, the objective being to determine which subset of technologies is the best candidate to go forward into development given conflicting objectives on performance, engine manufacturing cost, and design risk (i.e. cumulative technology readiness). Introduction manufacturing cost, design risk, etc. School of Aerospace, Georgia Tech. Member, AIAA. Director, ASDL. Associate Fellow, AIAA.