IDENTIFICATION OF GAS-LIQUID FLOW REGIMES IN A HORIZONTAL FLOW USING NEURAL NETWORK-中国期刊网

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

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IDENTIFICATION OF GAS-LIQUID FLOW REGIMES IN A HORIZONTAL FLOW USING NEURAL NETWORK

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IDENTIFICATION OF GAS-LIQUID FLOW REGIMES IN A HORIZONTAL FLOW USING NEURAL NETWORK

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