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RISS 인기검색어
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- 저자 : G.H. Roshani (검색결과 2 건)
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E. Nazemi,S.A.H. FEGHHI,G.H. Roshani,R. Gholipour Peyvandi,S. Setayeshi 한국원자력학회 2016 Nuclear Engineering and Technology Vol.48 No.1
Void fraction is an important parameter in the oil industry. This quantity is necessary forvolume rate measurement in multiphase flows. In this study, the void fraction percentagewas estimated precisely, independent of the flow regime in gaseliquid two-phase flows byusing g-ray attenuation and a multilayer perceptron neural network. In all previous studiesthat implemented a multibeam g-ray attenuation technique to determine void fractionindependent of the flow regime in two-phase flows, three or more detectors were usedwhile in this study just two NaI detectors were used. Using fewer detectors is of advantagein industrial nuclear gauges because of reduced expense and improved simplicity. In thiswork, an artificial neural network is also implemented to predict the void fraction percentageindependent of the flow regime. To do this, a multilayer perceptron neuralnetwork is used for developing the artificial neural network model in MATLAB. Therequired data for training and testing the network in three different regimes (annular,stratified, and bubbly) were obtained using an experimental setup. Using the techniquedeveloped in this work, void fraction percentages were predicted with mean relative errorof <1.4%.
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Nazemi, E.,Feghhi, S.A.H.,Roshani, G.H.,Gholipour Peyvandi, R.,Setayeshi, S. Korean Nuclear Society 2016 Nuclear Engineering and Technology Vol.48 No.1
Void fraction is an important parameter in the oil industry. This quantity is necessary for volume rate measurement in multiphase flows. In this study, the void fraction percentage was estimated precisely, independent of the flow regime in gas-liquid two-phase flows by using ${\gamma}-ray$ attenuation and a multilayer perceptron neural network. In all previous studies that implemented a multibeam ${\gamma}-ray$ attenuation technique to determine void fraction independent of the flow regime in two-phase flows, three or more detectors were used while in this study just two NaI detectors were used. Using fewer detectors is of advantage in industrial nuclear gauges because of reduced expense and improved simplicity. In this work, an artificial neural network is also implemented to predict the void fraction percentage independent of the flow regime. To do this, a multilayer perceptron neural network is used for developing the artificial neural network model in MATLAB. The required data for training and testing the network in three different regimes (annular, stratified, and bubbly) were obtained using an experimental setup. Using the technique developed in this work, void fraction percentages were predicted with mean relative error of <1.4%.
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