Two‐phase flows play a vital role in refrigeration, air conditioning, and other industrial applications. This necessitates the development of precise techniques to characterize various two‐phase flow regimes. In the present work, characterization of two‐phase flow in horizontal tubes of diameters 4.7 mm and 3.4 mm is done by analyzing laser patterns. Laser patterns are recorded using a high‐speed camera. The area occupied by laser patterns for air‐water and air‐oil flows is analyzed by applying grayscale analysis and distance transformation techniques in image processing. A technique based on the movement of the centre of intensity of the laser pattern is used to characterize two‐phase flow regimes. Centre of intensity of a laser pattern is the point with maximum pixel intensity in the processed image. Probability density estimation together with the position‐time graph for centre of intensity is used to characterize two‐phase flow patterns. Bubbly, slug, and stratified flow regimes are observed and analyzed. The slug length and velocity is calculated by analyzing laser patterns. The two‐phase flow regime map is generated based on the identified two‐phase flow patterns and is validated with a flow map for conventional channels available in the literature.
The applications of Bubble column reactors in gas-liquid multiphase reactions are widely observed in process industries. Biochemical reactions such as wet oxidation and algae bio-reactions are carried out in bubble column reactors. In this article, an image processing based comprehensive algorithm is developed to identify the trajectory of bubbles in a bubble column reactor. Photographs of bubbles moving up in a bubble column reactor are recorded for different velocities using a high speed camera. An algorithm is developed to plot the trajectory of the bubble. The developed algorithm can be used with experimental and numerical results to trace the trajectory of bubbles. The algorithm is applied to the results of volume of fluids (VOF) simulation to identify the bubble path in Newtonian and non-Newtonian fluids. Based on the algorithm, numerical results obtained on Newtonian fluids are used to train an Artificial Neural Network (ANN) to find the temporal position of the bubble. Superficial fluid velocities, nozzle diameter and time are the input parameters. The trained Levenberg-Marquardt based neural network can find the position of the bubble at any instant of time. The designed algorithm can study the dynamics and position of a bubble in process applications carried out in a bubble column reactor.
An Electro-Hydraulic System (EHS) that consists of a hydraulic pump driven by brushless DC motor is monitored for pump cavitation using feedforward fully connected artificial neural networks. The effectiveness of monitoring is demonstrated in a laboratory environment using Open Software Architecture (OSA) framework.
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