Understanding and predicting flow behaviour of water and air in serpentine pipes using machine learning and CFD modelling

The flow behaviour of two-phase fluid (water and air) in serpentine pipes is complex and poorly understood. This study aims to address this research problem by using machine learning concepts to gain a deeper understanding of the flow behaviour and optimize the geometry of serpentine pipes. Experimental data from the Cranfield process engineering laboratory was used in this work, for a fixed pipe diameter and varying water and gas velocities. Regression models were developed and trained. The study aimed to accurately predict the pressure drop, void fraction and liquid film thickness in serpentine pipes in a timely manner with high accuracy. In addition, Computational Fluid Dynamics (CFD) models were developed to predict the flow behaviour with two different geometries, and machine learning was applied to determine the best model for capturing the intermediate geometry flow behaviour. Results provide valuable insights into the behaviour of two-phase fluid in serpentine pipes. The use of machine learning in this research contributes to the field by offering a new approach for optimizing the geometry of serpentine pipes with improved accuracy and efficiency. The findings demonstrate the potential for machine learning to play a role in improving our understanding of two-phase fluid flow in serpentine pipes. This research is expected to have potential future applications in various sectors, including automotive, electronics cooling systems, and industrial and chemical processing systems.