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The void fraction is one of the most important parameters characterizing a multiphase flow. The prediction of the performance of any system operating with more than single phase relies on our knowledge and ability to measure the void fraction. In this work, a validated simulation study was performed in order to predict the void fraction independent of the flow pattern in gas-liquid two-phase flows using a gamma ray 60Co source and just one scintillation detector with the help of an artificial neural network (ANN) model of radial basis function (RBF). Three used inputs of ANN include a registered count under Compton continuum and counts under full energy peaks of 1173 and 1333 keV. The output is a void fraction percentage. Applying this methodology, the percentage of void fraction independent of the flow pattern of a gas-liquid two-phase flow was estimated with a mean relative error less than 1.17%. Although the error obtained in this study is almost close to those obtained in other similar works, only one detector was used, while in the previous studies at least two detectors were employed. Advantages of using fewer detectors are: cost reduction and system simplification.
Czasopismo
Rocznik
Tom
Strony
347--358
Opis fizyczny
Bibliogr. 33 poz., rys., tab., wykr., wzory
Twórcy
autor
- Kermanshah University of Technology, Electrical Engineering Department, Kermanshah, Iran
autor
- Nuclear Science and Technology Research Institute, Tehran, Iran
autor
- Islamic Azad University, Department of Electrical Engineering, Kermanshah Branch, Kermanshah, Iran
autor
- Kermanshah University of Medical Sciences, Medical Biology Research Centre, Kermanshah, Iran
autor
- Islamic Azad University, Young Researchers and Elite Club, Kermanshah Branch, Kermanshah, Iran
Bibliografia
- [1] Abro, E., Johansen, G.A. (1999). Improved Void Fraction Determination by Means of Multibeam Gamma-Ray Attenuation Measurements. Flow. Meas. Instrum., 10(2), 99-108.
- [2] Roshani, G.H., Nazemi, E., Feghhi, S.A.H., Setayeshi, S. (2015). Flow regime identification and void fraction prediction in two-phase flows based on gamma ray attenuation. Measurement., 62, 25-32.
- [3] Adineh, M., Nematollahi, M., Erfaninia, A. (2015). Experimental and numerical void fraction measurement for modeled two-phase flow inside a vertical pipe. Ann. Nucl. Energy., 83, 188-192.
- [4] Nazemi, E., Roshani, G.H., Feghhi, S.A.H, Gholipour Peyvandi, R., Setayeshi, S. (2016). Precise Void Fraction Measurement in Two-Phase Flows Independent of the Flow Regime using gamma-ray attenuation. Nucl. Eng. Technol., 48, 64-71.
- [5] Roshani, G.H., Nazemi, E., Roshani, M.M. (2017). Identification of flow regime and estimation of volume fraction independent of liquid phase density in gas-liquid two-phase flow. Prog. Nucl. Energ., 98, 29-37.
- [6] El Abd, A. (2014). Intercomparison of gamma ray scattering and transmission techniques for gas volume fraction measurements in two phase pipe flow. Nucl. Instrum. Meth. A., 735, 260-266.
- [7] Roshani, G.H., Nazemi, E., Roshani, M.M. (2017). Usage of Two Transmitted Detectors with Optimized Orientation In order to Three Phase Flow Metering. Measurement, 100, 122-130.
- [8] Hanus, R., Petryka, L., Zych, M. (2014). Velocity measurement of the liquid-solid flow in a vertical pipeline using gamma-ray absorption and weighted cross-correlation. Flow. Meas. Instrum., 40, 58-63.
- [9] Roshani, G.H., Nazemi, E., Roshani, M.M. (2017). Flow regime independent volume fraction estimation in three-phase flows using dual-energy broad beam technique and artificial neural network. Neural. Comput. Appl., 28, 1265.
- [10] Roshani, G.H., Feghhi, S.A.H., Mahmoudi-Aznaveh, A., Nazemi, E., Adineh-Vand, A. (2014). Precise volume fraction prediction in oil-water-gas multiphase flows by means of gamma-ray attenuation and artificial neural networks using one detector. Measurement., 51, 34-41.
- [11] Mosorov, V., Zych, M., Hanus, R., Petryka, L. (2016). Modelling of dynamic experiments in MCNP5 environment. Appl. Radiat. Isotopes., 112, 136-140.
- [12] Roshani, G.H., Hanus, R., Khazaei, A., Zych, M., Nazemi, E., Mosorov, V. (2018). Density and velocity determination for single-phase flow based on radiotracer technique and neural networks. Flow. Meas. Instrum., 61, 9-14.
- [13] Hanus, L., Zych, M., R., Petryka, Swisulski, D. (2014). Time delay estimation in two-phase flow investigation using the g-ray attenuation technique. Math. Probl. Eng., 2014, 1-10.
- [14] Roshani, G.H., Nazemi, E. (2017). A high performance gas–liquid two-phase flow meter based on gamma-ray attenuation and scattering. Nucl. Sci. Tech., 28(11), 169.
- [15] Nazemi, E., Roshani, G.H., Feghhi, S.A.H, Setayeshi, S., Gholipour Peyvandi, R. (2015). A radiation-based hydrocarbon two-phase flow meter for estimating of phase fraction independent of liquid phase density in stratified regime. Flow. Meas. Instrum., 46, 25-32.
- [16] Nazemi, E., Roshani, G.H., Feghhi, S.A.H, Setayeshi, S., Eftekhari Zadeh, E., Fatehi, A. (2016). Optimization of a method for identifying the flow regime and measuring void fraction in a broad beam gamma-ray attenuation technique. Int. J. Hydrogen. Energ., 41, 7438-444.
- [17] Roshani, G.H., Nazemi, E., Roshani, M.M. (2017). Application of radial basis function in densitometry of stratified regime of liquid-gas two phase flows. Radiat. Meas., 100, 9-17.
- [18] Zych, M., Petryka, L., Kępiński, J., Hanus, R., Bujak, T., Puskarczyk, E. (2014). Radioisotope investigations of compound two-phase flows in an open channel. Flow. Meas. Instrum., 35, 11-15.
- [19] Salgado, C.M., Pereira, C.M.N.A., Schirru, R., Brandao, L.E.B. (2010). Flow regime identification and volume fraction prediction in multiphase flows by means of gamma-ray attenuation and artificial neural networks. Prog. Nucl. Energ., 52, 555-562.
- [20] Yadollahi, A., Nazemi, E., Zolfaghari, A., Ajorloo, A.M. (2016). Application of artificial neural network for predicting the optimal mixture of radiation shielding concrete. Prog. Nucl. Energ., 89, 69-77.
- [21] Karami, A., Roshani, G.H., Salehizadeh, A., Nazemi, E. (2017). The Fuzzy Logic Application in Volume Fractions Prediction of the Annular Three-Phase Flows. J. Nondestruct. Eval., 36, 35.
- [22] Hanus, R. (2015). Application of the Hilbert Transform to measurements of liquid-gas flow using gamma ray densitometry. Int. J. Multiphas. Flow., 72, 210-217.
- [23] Yadollahi, A., Nazemi, E., Zolfaghari, A., Ajorloo, A.M. (2016). Optimization of thermal neutron shield concrete mixture using artificial neural network. Nucl. Eng. Des., 305, 146-155.
- [24] Roshani, G.H., Nazemi, E., Roshani, M.M. (2017). A novel method for flow pattern identification in unstable operational conditions using gamma ray and radial basis function. Appl. Radiat. Isotopes., 123, 60-68.
- [25] Roshani, G.H., Nazemi, E., Roshani, M.M. (2017). Intelligent recognition of gas-oil-water three-phase flow regime and determination of volume fraction using Radial Basis Function. Flow. Meas. Instrum., 54, 39-45.
- [26] Roshani, G.H., Karami, A., Salehizadeh, A., Nazemi, E. (2017). The capability of radial basis function to forecast the volume fractions of the annular three-phase flow of gas-oil-water. Appl. Radiat. Isotopes., 129, 156-62.
- [27] Eftekharizadeh, E., Sadighzadeh, A., Salehizadeh, A., Nazemi, E., Roshani, G.H. (2016). Neutron activation analysis for cement elements using an IECF device as a high energy neutron source. Analytical. Methods., 8(11), 2510-2514.
- [28] Roshani, G.H., Nazemi, E., Feghhi, S.A.H. (2016). Investigation of using 60Co source and one detector for determining the flow regime and void fraction in gas-liquid two-phase flows. Flow. Meas. Instrum., 50, 73-79.
- [29] Pelowitz, D.B. (2005). MCNP-X TM User’s Manual, Version 2.5.0. LA-CP-05e0369. Los Alamos National Laboratory.
- [30] Amid, S., Mesri Gundoshmian, T. (2017). Prediction of output energies for broiler production using linear regression, ANN (MLP, RBF), and ANFIS models. Environ. Prog. Sustain., 36(2), 577-585.
- [31] Rakhshkhorshid, M. (2017). A Robust RBF-ANN Model to Predict the Hot Deformation Flow Curves of API X65 Pipeline Steel. Iranian Journal of Materials Forming, 4(1), 12-20.
- [32] Niroomand-Toomaj, E., Etemadi, A., Shokrollahi, A. (2017). Radial basis function modeling approach to prognosticate the interfacial tension CO 2/Aquifer Brine. J. MO.L LIQ., 238, 540-544.
- [33] Wang, L., Liu, J., Yan, Y., Wang, X., Wang, T. (2017). Gas-Liquid Two-Phase Flow Measurement Using Coriolis Flowmeters Incorporating Artificial Neural Network, Support Vector Machine, and Genetic Programming Algorithms. IEEE. T. Instrum. Meas., 66(5), 852-868.
Uwagi
PL
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-f11621c9-8772-49a6-95c9-144b2d6d118e