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Segmenting the Complex and Irregular in Two-Phase Flows: A Real-World Empirical Study with SAM2

Segmenting the Complex and Irregular in Two-Phase Flows: A Real-World Empirical Study with SAM2

来源:Arxiv_logoArxiv
英文摘要

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.

Semanur Küçük、Cosimo Della Santina、Angeliki Laskari

冶金技术计算技术、计算机技术

Semanur Küçük,Cosimo Della Santina,Angeliki Laskari.Segmenting the Complex and Irregular in Two-Phase Flows: A Real-World Empirical Study with SAM2[EB/OL].(2025-08-07)[2025-08-18].https://arxiv.org/abs/2508.05227.点此复制

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