Segmenting the Complex and Irregular in Two-Phase Flows: A Real-World Empirical Study with SAM2
This work addresses a long-standing challenge in industrial settings like metallurgy and maritime systems, where traditional methods fail due to bubble deformation and coalescence, though it is incremental as it applies an existing foundation model to a new domain.
The paper tackled the problem of segmenting irregular gas bubbles in two-phase flows, which is critical for industrial applications, and demonstrated that a fine-tuned Segment Anything Model (SAM2) can accurately segment these complex structures using only 100 annotated images.
Segmenting gas bubbles in multiphase flows is a critical yet unsolved challenge in numerous industrial settings, from metallurgical processing to maritime drag reduction. Traditional approaches-and most recent learning-based methods-assume near-spherical shapes, limiting their effectiveness in regimes where bubbles undergo deformation, coalescence, or breakup. This complexity is particularly evident in air lubrication systems, where coalesced bubbles form amorphous and topologically diverse patches. In this work, we revisit the problem through the lens of modern vision foundation models. We cast the task as a transfer learning problem and demonstrate, for the first time, that a fine-tuned Segment Anything Model SAM v2.1 can accurately segment highly non-convex, irregular bubble structures using as few as 100 annotated images.