Unlocking the dynamics of complex instability mechanisms in developing gravity-driven slug flows

Slug flow stability stands as a critical frontier in two-phase flow research, with limited focus on the complex dynamics governing unstable gravity-driven slug flows in developing regions. Despite decades of research, several uncertainties persist, particularly regarding the complex mechanisms driving the flow instabilities. These uncertainties encompass the systematic classification of instabilities, their interdependence or isolation, their persistence or transience, and whether they exhibit chaotic or periodic behavior. Additionally, questions remain about their temporal dynamics—whether they evolve rapidly or gradually—their relative intensity, and their spatiotemporal propagation as they interact with overall flow development. Moreover, it remains unclear whether gravity induces new instability modes, what distinct characteristics these modes exhibit, and how gas density modulate instability dynamics. Furthermore, can a fully stabilized flow state ever be attained, or is it an elusive ideal? Most critically, how can one effectively diagnose instabilities, track their progression, and pinpoint stabilization onset in real time under operational constraints? Addressing these questions has been historically challenging due to the lack of a robust framework capable of simultaneously analyzing the inherent multi-layered complexities of two-phase flow instabilities. To overcome this limitation and provide explanations for the above-mentioned questions, we introduce a novel AI-assisted, data-driven, scale-independent spatiotemporal-spectral analysis framework, integrating advanced signal processing techniques—Recurrence Qualification Analysis, Fast Fourier Transform, and Discrete and Continuous Wavelet Transforms—to analyze void fraction signals captured at four spatial locations in air- and CO₂-water gravity-driven slug flows. High-speed imaging complements the analysis, offering visual insights into transient instability mechanisms. The analysis also charts an instability map, systematically classifying instability mechanisms while depicting their interconnections. A Convolutional Neural Network extracts features, transforming the analysis framework into a fast-response, real-time diagnostic and predictive tool, achieving an accuracy of $\sim$96 %. This represents a breakthrough in diagnosing instabilities, tracking their evolution, and identifying the onset of stabilization within slug flows. By bridging analytical precision with real-time capabilities, this data-driven, scale-independent framework establishes a new benchmark for the analysis and control of complex two-phase flow systems of varying dimensions.