Thermal-hydraulic modeling and simulation are heavily dependent on the knowledge of flow regimes, which sort multiphase flow patterns into classifications with static boundaries that display characteristic features of flow patterns and phase distributions. Flow regime maps heretofore have been primarily developed mainly for vertical or horizontal pipe orientations by capturing images of flows at varying conditions and assigning them to different flow regimes based on visualization studies.

As such, this method is inherently subjective. Furthermore, classifying two-phase flows into regimes with static boundaries is not physical, as two-phase flows undergo gradual changes as they transition from one regime to another. Therefore, such a static approach is not only unphysical but also inaccurate, resulting in the erroneous analysis of two-phase flows. Additionally, subjective observations at such flow conditions are prone to disagreement and inconsistencies in labeling. These difficulties are exacerbated in inclined two-phase flows.

In this study, a novel two-phase flow pattern map employing the dynamic red, green, and blue (RGB) color model is presented. The model contains the embedded information of three normalized time-averaged statistics from signals obtained by a dual-ring impedance meter. A database of impedance meter signals from a 25.4-mm-inner-diameter adiabatic air-water inclinable test facility is used to compute statistics for each experimental flow condition. Multiple statistical measures are evaluated, and a principal component analysis is performed for feature selection.

By mapping each parameter to intensities of red, green, and blue, a dynamic RGB flow pattern map can be produced in which groups of similar colors are representative of various flow regimes and the dynamic transition characteristics between the flow regimes are characterized physically. Therefore, this new method makes viable the observation of how flow patterns gradually change with respect to angle, as well as the fuzzification or quantification of the uncertainty surrounding experimental transition boundaries.