Title:
Adaptive Selection of Pareto Optimal Engine Technology Solution Sets

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icas2002_paper_594.pdf (314.72 KB)
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Roth, Bryce Alexander
 Graham, Matthew
 Mavris, Dimitri N.
 Macsotai, Noel I.
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Daniel Guggenheim School of Aerospace Engineering
The Daniel Guggenheim School of Aeronautics was established in 1931, with a name change in 1962 to the School of Aerospace Engineering
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http://hdl.handle.net/1853/6252
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.
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2002
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322270 bytes
 1905 bytes
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