Adaptive Selection of Pareto Optimal Engine Technology Solution Sets

Abstract

Successful selection of propulsion system technologies for development and incorporation into new engine designs requires careful balance among many competing design objectives (i.e. performance, cost, risk, etc.). One seldom has sufficient development resources available to fully explore all promising concepts and must therefore choose a few technologies that show the greatest promise to meet program objectives. This paper describes a method of selecting optimal combinations of engine technologies. This method employs a technology impact forecasting environment in conjunction with genetic algorithms to find Pareto-optimal technology solution sets. These results are illustrated using Technology State Transition Diagrams to show how technologies move into and out of the Pareto-optimal sets. An edge search procedure is introduced as a means to efficiently characterize the objective space, the results of which are presented in the form of ternary plots. These plots show how technologies benefit multiple (oftenconflicting) objectives and help find robust or compromise technology combinations. Finally, these methods are applied to select engine technology combinations for a commercial engine system of current interest.