Fuzzy identification of transitional flow patterns in core annular multiphase flows using Computational Fluid Dynamics

The complexity of multiphase flow systems poses significant engineering challenges. For Core Annular Flow (CAF) systems, where a viscous core is enveloped by a less viscous fluid, accurate flow pattern identification is crucial for optimizing efficiency. This research pioneers an integrated approach using Computational Fluid Dynamics (CFD) and fuzzy clustering to identify and classify CAF patterns. A novel methodology was developed: numerical simulations provide data on velocity and phase distributions, which are then processed using principal component analysis (PCA) and a fuzzy logic-based clustering algorithm specifically designed to resolve transitional flow states. Our findings confirm the methodology’s capacity to effectively differentiate various flow regimes and, importantly, to characterize the continuous transitions between them. This work delivers a robust and adaptable framework for improved understanding and optimization of CAF in industrial contexts. • Integration of CFD and Fuzzy Logic: We propose a novel methodology combining Computational Fluid Dynamics (CFD) simulations with fuzzy clustering techniques to capture the continuous transitions between flow patterns. This approach overcomes the limitations of traditional Boolean classification by accommodating intermediate states. • Practical Relevance: Our framework successfully reproduces classical flow patterns (e.g., annular, slug, stratified) observed in literature and identifies transitional regimes. The validated 3D flow maps provide actionable insights into industrial applications, enhancing system stability and efficiency. • Methodological Innovation: By leveraging Principal Component Analysis (PCA) and Fuzzy C-Means clustering, we reduce dimensionality while preserving critical flow dynamics, enabling robust pattern recognition even in complex, overlapping regimes.