Identificação de padrões de escoamentos multifásicos em sistemas core annular flow baseados em fluidodinâmica computacional utilizando agrupamentos fuzzy

Abstract

Petroleum, which is composed of a variety of chemical components, exhibits distinct physical and chemical characteristics, which requires the application of specific techniques to ensure adequate flow and avoid operational problems. Among these techniques, Core Annular Flow (CAF) stands out, as it reduces shear stresses by peripherally introducing a low-viscosity fluid. However, the injection of immiscible fluids can result in different multiphase flow patterns, which has been a challenge for the industry, which seeks to identify the flow pattern present in the system. To address this issue, a methodology was developed that combines Computational Fluid Dynamics (CFD) and artificial intelligence with fuzzy groupings. This approach allowed us to effectively identify different flow patterns. The simulations carried out were compared with experimental data already existing in the literature, demonstrating the validity of the proposed methodology. Furthermore, the application of fuzzy clustering enabled the detection of transition regions, providing a more detailed and continuous characterization of multiphase flow patterns, which expands the understanding of these phenomena in the industry.