Dimensionless Artificial Intelligence-Based Model for Multiphase Flow Pattern Recognition in Horizontal Pipe

Abstract

Summary Multiphase flow analysis attracts a lot of attention from researchers from diverse disciplines. There are several studies including experimental, theoretical modeling, and numerical analysis that were carried out to investigate the multiphase flow. However, many facets of multiphase flow are still unresolved owing to the extremely complex nature of the multiphase flow. The complex interactions of the different phases are leading to different flow regimes that are difficult to predict but essential for developing the computational model. The identification of the flow pattern is still a challenging task. One of the growing fields is the machine learning approach, which can address such complex problems. This study aims to use machine learning to develop models that can identify the flow patterns in multiphase flow. To achieve the objective, a large set of experimental data was collected. The effect of fluid properties, such as density, viscosity, and surface tension, on the flow pattern was introduced by changing the fluid properties. The wide range of data was processed by applying a machine learning technique for predicting the flow regimes. The models were built using dimensionless parameters to extend their validity for various design and operational conditions. This approach enables to capture the main flow pattern as well as subcategories of flow patterns in the horizontal pipe. Comparison and analyses of the different machine learning tools were carried out to investigate classification of multiphase flow patterns. The results showed that different artificial intelligence (AI) methods can predict the flow pattern in horizontal pipes with high accuracy. The results of using Reynold’s number for liquid (ReL) and gas (ReG) as an input to predict the flow patterns are deficient in accuracy for the support vector machine (SVM) and discriminant analysis (DA). However, the prediction capability of the model was improved by introducing Weber’s number for liquid (WeL) and gas (WeG) along with the Reynolds numbers (ReL and ReG). The improvement in the flow pattern prediction owing to the introduction of Weber’s number is speculated because of the capturing hydrodynamic phenomenon (inertia and surface tension) owing to change in fluid properties. It infers that capturing hydrodynamic phenomena affecting the flow pattern and their transition is essential for the prediction of flow patterns in multiphase flow.