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多相流动科学与技术
SJR: 0.124 SNIP: 0.222 CiteScore™: 0.26

ISSN 打印: 0276-1459
ISSN 在线: 1943-6181

多相流动科学与技术

DOI: 10.1615/MultScienTechn.v27.i1.10
pages 1-26

VISCOUS OIL-WATER FLOW THROUGH AN INCLINED PIPELINE: EXPERIMENTATION AND PREDICTION OF FLOW PATTERNS

Anjali Dasari
Department of Chemical Engineering, Indian Institute of Technology Guwahati, Guwahati, Assam, India
Bharath Kumar Goshika
Department of Chemical Engineering, Indian Institute of Technology Guwahati, Guwahati-781039, Assam, India
Subrata Kumar Majumder
Department of Chemical Engineering, Indian Institute of Technology Guwahati, Guwahati, PIN-781039, Assam, India
Tapas K Mandal
Department of Chemical Engineering, Indian Institute of Technology Guwahati, Guwahati, Assam, India

ABSTRACT

We identify and predict the flow patterns observed during concurrent flow of viscous oil (viscosity 107 m Pa s, density 889 kg/m3) and water through a +5 deg inclined circular Perspex pipe with internal diameter of 0.025 m. Flow patterns have been identified with the help of visual and photographic techniques in a wide range of superficial velocities of both the fluids (USO = 0.052−1.38 m/s and USW = 0.068−1.23 m/s). Seven different flow patterns (namely, plug, slug, wavy stratified, stratified mixed, annular, dispersion of oil in water, and dispersion of water in oil flow) have been identified and a flow pattern map has been developed for the present system. Flow pattern transition boundaries have been predicted by analytical models and probabilistic neural network (PNN) technique. Transition of wavy stratified to stratified mixed flow pattern has been predicted following the drop formation mechanism at interface proposed by Al-wahibi, Smith, and Angeli, (Transition between Stratified and Non-Stratified Horizontal Oil-Water Flows: Part II. Mechanism of Drop Formation, Chem. Eng. Sci., vol. 62, pp. 2929−2940, 2007). During the development of PNN, superficial velocities of oil and water, pipe diameter, viscosity ratio, density ratio, interfacial tension, and pipe inclination have been considered as governing parameters of the flow patterns. The trained PNN gives a better prediction over the analytical models with accuracy of ∼90%.


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