Flow Regime Identification using Machine Learning and Local Conductivity Measurements

The accurate identification of flow regimes in multiphase flow systems is of paramount importance in many engineering applications. This thesis explores the significance of flow regime identification using neural networks, specifically employing a self-organizing map (SOM) algorithm. The focus of this research is on the determination of bubble void fraction probability density function (PDF) using local conductivity probe measurements. The thesis begins by providing an overview of the importance of flow regime identification in understanding and predicting the behavior of multiphase flows. Various flow regimes such as bubbly flow, slug flow, annular flow, and others, are discussed highlighting their distinct characteristics and implications for system performance. The self-organizing map is introduced as a powerful neural network technique capable of identifying and classifying different flow regimes based on input parameters obtained from local conductivity probe measurements. The SOM algorithm is explained in detail, emphasizing its ability to learn and adapt to complex patterns in the data. To validate the effectiveness of the proposed approach, experimental measurements of local conductivity probe signals were conducted in a multiphase flow system. The obtained data was used to train and optimize a self-organizing map for flow regime identification. The bubble void fraction probability density function was calculated based on the local time-averaged void fraction measurements from the droplet-capable conductivity probe (DCCP-4). The analysis of the PDF provides valuable insights into the distribution and characteristics of bubbles within the multiphase flow system. These insights can enhance the understanding of bubble behavior, droplet behavior, interfacial phenomena and overall system performance. The thesis concludes with the classification results along with an error analysis conducted to highlight potential discrepancies in the tested results. Additionally, future research directions and potential improvements in the flow regime identification methodology are outlined.