Precise Void Fraction Measurement in Two-phase Flows Independent of the Flow Regime Using Gamma-ray Attenuation
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 γ-ray attenuation and a...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2016-02-01
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| Series: | Nuclear Engineering and Technology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1738573315002144 |
| _version_ | 1819095237558009856 |
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| author | E. Nazemi S.A.H. Feghhi G.H. Roshani R. Gholipour Peyvandi S. Setayeshi |
| author_facet | E. Nazemi S.A.H. Feghhi G.H. Roshani R. Gholipour Peyvandi S. Setayeshi |
| author_sort | E. Nazemi |
| collection | DOAJ |
| description | 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 γ-ray attenuation and a multilayer perceptron neural network. In all previous studies that implemented a multibeam γ-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%. |
| first_indexed | 2024-12-21T23:40:07Z |
| format | Article |
| id | doaj.art-071dc730151d47a2b4bb1b6b044d9164 |
| institution | Directory Open Access Journal |
| issn | 1738-5733 |
| language | English |
| last_indexed | 2024-12-21T23:40:07Z |
| publishDate | 2016-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Nuclear Engineering and Technology |
| spelling | doaj.art-071dc730151d47a2b4bb1b6b044d91642022-12-21T18:46:15ZengElsevierNuclear Engineering and Technology1738-57332016-02-01481647110.1016/j.net.2015.09.005Precise Void Fraction Measurement in Two-phase Flows Independent of the Flow Regime Using Gamma-ray AttenuationE. Nazemi0S.A.H. Feghhi1G.H. Roshani2R. Gholipour Peyvandi3S. Setayeshi4Young Researchers and Elite Club, Kermanshah Branch, Islamic Azad University, Kermanshah, IranRadiation Application Department, Shahid Beheshti University, Tehran, IranRadiation Application Department, Shahid Beheshti University, Tehran, IranNuclear Science and Technology Research Institute, Tehran, IranDepartment of Energy Engineering and Physics, Amirkabir University of Technology, Tehran, IranVoid 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 γ-ray attenuation and a multilayer perceptron neural network. In all previous studies that implemented a multibeam γ-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%.http://www.sciencedirect.com/science/article/pii/S1738573315002144Artificial Neural NetworkGammaIndependent Flow RegimeMultilayer PerceptronVoid Fraction |
| spellingShingle | E. Nazemi S.A.H. Feghhi G.H. Roshani R. Gholipour Peyvandi S. Setayeshi Precise Void Fraction Measurement in Two-phase Flows Independent of the Flow Regime Using Gamma-ray Attenuation Nuclear Engineering and Technology Artificial Neural Network Gamma Independent Flow Regime Multilayer Perceptron Void Fraction |
| title | Precise Void Fraction Measurement in Two-phase Flows Independent of the Flow Regime Using Gamma-ray Attenuation |
| title_full | Precise Void Fraction Measurement in Two-phase Flows Independent of the Flow Regime Using Gamma-ray Attenuation |
| title_fullStr | Precise Void Fraction Measurement in Two-phase Flows Independent of the Flow Regime Using Gamma-ray Attenuation |
| title_full_unstemmed | Precise Void Fraction Measurement in Two-phase Flows Independent of the Flow Regime Using Gamma-ray Attenuation |
| title_short | Precise Void Fraction Measurement in Two-phase Flows Independent of the Flow Regime Using Gamma-ray Attenuation |
| title_sort | precise void fraction measurement in two phase flows independent of the flow regime using gamma ray attenuation |
| topic | Artificial Neural Network Gamma Independent Flow Regime Multilayer Perceptron Void Fraction |
| url | http://www.sciencedirect.com/science/article/pii/S1738573315002144 |
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