Precise Void Fraction Measurement in Two-phase Flows Independent of the Flow Regime Using Gamma-ray Attenuation |
Nazemi, E.
(Young Researchers and Elite Club, Kermanshah Branch, Islamic Azad University)
Feghhi, S.A.H. (Radiation Application Department, Shahid Beheshti University) Roshani, G.H. (Radiation Application Department, Shahid Beheshti University) Gholipour Peyvandi, R. (Nuclear Science and Technology Research Institute) Setayeshi, S. (Department of Energy Engineering and Physics, Amirkabir University of Technology) |
1 | G.H. Roshani, S.A.H. Feghhi, A. Adineh-Vand, M. Khorsandi, Application of adaptive neuro-fuzzy inference system in prediction of fluid density for a gamma ray densitometer in petroleum products monitoring, Measurement 46 (2013) 3276-3281. DOI |
2 | M. Khorsandi, S.A.H. Feghhi, A. Salehizadeh, G.H. Roshani, Developing a gamma ray fluid densitometer in petroleum products monitoring applications using artificial neural network, Radiation Meas. 59 (2013) 183-187. DOI |
3 | G.H. Roshani, S.A.H. Feghhi, A. Mahmoudi-Aznaveh, E. Nazemi, A. Adineh-Vand, Precise volume fraction prediction in oil-water-gas multiphase flows by means of -ray attenuation and artificial neural networks using one detector, Measurement 51 (2014) 34-41. DOI |
4 | E. Nazemi, S.A.H. Feghhi, G.H. Roshani, Void fraction prediction in two-phase flows independent of the liquid phase density changes, Radiation Meas. 68 (2014) 49-54. DOI |
5 | C.G. Jing, G.Z. Xing, B. Liu, Q.G. Bai. Determination of gas and water volume fraction in oil water gas pipe flow using neural networks based on dual modality densitometry. Advances in Neural Networks, Lecture Notes in Computer Science, Springer, New York. 3973 (2006) 1248-1253. |
6 | C.M. Salgado, L.E.B. Brandao, C.M.N.A. Pereira, W.L. Salgado, Salinity independent volume fraction prediction in annular and stratified (water-gas-oil) multiphase flows using artificial neural networks, Prog. Nucl. Energy 76 (2014) 17-23. DOI |
7 | G.H. Roshani, S.A.H. Feghhi, F. Shama, A. Salehizadeh, E. Nazemi, Prediction of materials density according to number of scattered gamma photons using optimum artificial neural network, J. Comput. Meth. Phys. 2014 (2014) 305345. |
8 | G.A. Johansen, P. Jackson, Salinity independent measurement of gas volume fraction in oil/gas/water pipe flows, Appl. Radiation Isotopes 53 (2000) 595-601. DOI |
9 | J.G. Taylor, Neural Networks and Their Applications, John Wiley & Sons Ltd., Brighton, 1996. |
10 | A.R. Gallant, H. White, On learning the derivatives of an unknown mapping with multilayer feed forward networks, Neural Networks 5 (1992) 129-138. DOI |
11 | M.T. Hagan, M. Menhaj, Training feedforward networks with the Marquardt algorithm, IEEE Trans. Neural Networks 5 (1994) 989-993. DOI |
12 | Y. Jiang, K. Rezkallah, An experimental study of the suitability of using a gamma densitometer for void fraction measurements in gas-liquid flow in a small diameter tube, Meas. Sci. Technol. 4 (1993) 496. DOI |
13 | J.M. Delhaye, Recent advances in two-phase flow instrumentation, in: Eighth International Heat Transfer Conference, Hemisphere, Washington, DC, 1986, pp. 215-226. |
14 | C.W. Snoek, A selection of new developments in multiphase flow measurement techniques, Thermal Fluid Sci. 3 (1990) 60-73. DOI |
15 | A. El Abd, Intercomparison of gamma ray scattering and transmission techniques for gas volume fraction measurements in two phase pipe flow, Nucl. Instr. Meth. Phys. Res. A 735 (2014) 260-266. DOI |
16 | E. Abro, G.A. Johansen, Improved void fraction determination by means of multibeam gamma-ray attenuation measurements, Flow Meas. Instr 10 (1992) 99-108. |
17 | E. Abro, V.A. Khoryakov, G.A. Johansen, Determination of void fraction and flow regime using a neural network trained on simulated data based on gamma-ray densitometry, Meas. Sci. Technol. 10 (1999) 619-630. DOI |
18 | C.G. Jing, Q. Bai, Flow regime identification of gas/liquid two-phase flow in vertical pipe using RBF neural networks, in: Chinese Control and Decision Conference (CCDC), 2009. |
19 | C.M. Salgado, L.E.B. Brandao, R. Schirru, C.M.N.A. Pereira, A. Xavier da Silva, R. Ramos, Prediction of volume fractions in three-phase flows using nuclear technique and artificial neural network, Appl. Radiation Isotopes 67 (2009) 1812-1818. DOI |
20 | T. Cong, G. Su, S. Qiu, W. Tian, Applications of ANNs in flow and heat transfer problems in nuclear engineering: a review work, Prog. Nucl. Energy 62 (2013) 54-71. DOI |