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dc.contributor.authorRoth, Bryce Alexanderen_US
dc.contributor.authorGraham, Matthewen_US
dc.contributor.authorMavris, Dimitri N.en_US
dc.contributor.authorMacsotai, Noel I.en_US
dc.date.accessioned2005-05-26T13:58:44Z
dc.date.available2005-05-26T13:58:44Z
dc.date.issued2002en_US
dc.identifier.urihttp://hdl.handle.net/1853/6252
dc.descriptionPresented at the 2002 ICAS Conference, Toronto, ICAS2002-5.9.4.en_US
dc.description.abstractSuccessful 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.en_US
dc.format.extent322270 bytes
dc.format.extent1905 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypetext/plain
dc.language.isoen_USen_US
dc.publisherGeorgia Institute of Technologyen_US
dc.relation.ispartofseriesASDL; ICAS2002-5.9.4en_US
dc.subjectCombinatorial optimization problemsen_US
dc.subjectCompeting design objectivesen_US
dc.subjectEngine designen_US
dc.subjectGenetic algorithmsen_US
dc.subjectPareto-optimal technology solution setsen_US
dc.subjectPropulsion systemsen_US
dc.subjectTechnology impact forecastingen_US
dc.subjectTechnology selectionen_US
dc.titleAdaptive Selection of Pareto Optimal Engine Technology Solution Setsen_US
dc.typePaperen_US


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