Experimental Studies and Probabilistic Neural Network Prediction on Flow Pattern of Viscous Oil–Water Flow through a Circular Horizontal Pipe (original) (raw)

Industrial & Engineering Chemistry Research, 2013

Abstract

ABSTRACT We report detailed analysis on the flow patterns of moderately viscous oil–water two-phase flow through a circular horizontal pipe with an internal diameter of 0.025 m. Lubricating oil and water with viscosity and density ratio of 107 and 0.889, respectively, have been selected as system fluids with interfacial tension 0.024 N/m. We have applied visual and imaging techniques to identify different flow patterns (viz., plug flow, slug flow, wavy stratified flow, stratified mixed flow, dispersion of oil in water, and dispersion of water in oil flow) for a wide range of superficial velocities of oil (USO = 0.015 to 1.25m/s) and water (USW = 0.1 to 1.1 m/s). The present map has also been correlated with the prediction by probabilistic neural network (PNN) along with six other flow pattern maps from the literature, having the wide variation of system properties to establish the PNN as a predictive tool for flow pattern map. For the construction of PNN, phase superficial velocities, conduit diameter, pipe inclination, viscosity, interfacial tension, and density are used as governing parameters of the flow patterns.

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