简介：Theknowledgeofflowregimesisveryimportantinthestudyofatwo-phaseflowsystem.AnewflowregimeidentificationmethodbasedonaProbabilityDensityFunction(PDF)andaneuralnetworkisproposedinthispaper.Theinstantaneousdifferentialpressuresignalsofahorizontalflowwereacquiredwithadifferentialpressuresensor.ThecharactersofdifferentialpressuresignalsfordifferentflowregimesareanalyzedwiththePDF.Then,fourcharacteristicparametersofthePDFcurvesaredefined,thepeaknumber(K1),themaximumpeakvalue(K2),thepeakposition(K3)andthePDFvariance(K4).Thecharacteristicvectorswhichconsistofthefourcharacteristicparametersastheinputvectorstraintheneuralnetworktoclassifytheflowregimes.Experimentalresultsshowthatthisnovelmethodforidentifyingair-watertwo-phaseflowregimeshastheadvantageswithahighaccuracyandafastresponse.Theresultsclearlydemonstratethatthisnewmethodcouldprovideanaccurateidentificationofflowregimes.