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1、APPLIEDENERGYAppliedEnergy84(2007)48–55www.elsevier.com/locate/apenergyNeural-networkbasedanalysisandpredictionofacompressor’scharacteristicperformancemapaa,*abYouhongYu,LingenChen,FengruiSun,ChihWuaPostgraduateSchool,NavalUniversityofEngineering,Wuhan430033,PRChinabMechanicalEngi
2、neeringDepartment,USNavalAcademy,Annapolis,MD21402,USAAvailableonline15June2006AbstractThedi?culties,duetoalackofinformationaboutstage-by-stageaxial-compressorperformance,areanalyzed.Toovercometheseissues,athree-layerback-propagationneural-networkappliedLevenberg–Marquardtalgorith
3、mispresentedanddiscussed.Theexperimentaldataprovidedbymanufacturersareusedfortheneural-networktraining.Throughtwicetraining,thecompressor’sperformancemapcanbepredicted.Theresultscanbeusedforthedevelopmentofano?-designmodeloroveralldynamicsimulationofthebehaviourofagas-turbinepower
4、-plant.ó2006ElsevierLtd.Allrightsreserved.Keywords:Compressor;Characteristicmap;Neural-network;Performanceprediction1.IntroductionTheincreasingsuccessofgas-turbinepower-plantsinindustrialandmarineapplicationsisowedpartlytotheirquickresponsestoloadvariations.Gas-turbinepower-plantp
5、erfor-manceunderISOconditions(burningareferencefuel,suchasnaturalgas,at150°C,atmo-sphericpressure,and60%relativehumidity)isinformationprovidedbymachinemanufactures.Nevertheless,gas-turbinepower-plantfrequentlyoperatesundero?-designconditions,forinstance,atpartloadorunderdi?erentat
6、mosphericconditions,andintheseconditions,thechangesofgas-turbinepower-plantperformancecanbedramatic.Inordertodescribetheo?-designperformanceofgas-turbinepower-plantaccurately,goodpredictions*Correspondingauthor.Tel.:+862783615046;fax:+862783638709.E-mailaddresses:lingenchen@hotmai
7、l.com,lgchenna@yahoo.com(L.Chen).0306-2619/$-seefrontmatteró2006ElsevierLtd.Allrightsreserved.doi:10.1016/j.apenergy.2006.04.005Y.Yuetal./AppliedEnergy84(2007)48–5549ofcharacteristiccurvesofgas-turbinepower-plantcomponentsareessential[1].Amajorproblem,however,isalackofinformationa
8、boutstage-by-stageperformance.The
