Work

Multi-modality fusion: Illuminating the complexity of two-phase flow

Year: 2026

Type: article

Abstract: Bubble dynamic analysis is crucial for modeling and optimizing phase change heat transfer. Recent advances in computer vision models have been widely applied to automatically extract bubble behavior parameters related to their performances. However, current segmentation models heavily rely on large datasets to achieve high accuracy, imposing a significant human workload. To overcome this challenge, we propose a novel data-fusion approach that enriches input information with dynamic features of bubble motion. Our methodology incorporates multi-modal generation techniques to transform conventional high-speed images into complementary modalities, and then fuse them for enhanced representation. This framework enables precise segmentation prediction using small datasets and without any pre-trained models. The resulting bubble statistics provide a quantitative foundation for advancing boiling heat transfer analysis and elucidating interfacial transport mechanisms. Overall, our multi-modal segmentation framework offers an automated, data-efficient, accurate, and versatile method for bubble behavior quantification in diverse two-phase flow systems.

Source: AI Thermal Fluids

DOI: 10.1016/j.aitf.2026.100028

Authors: Xiaojing Zhang, Sanghyeon Chang, Youngjoon Suh, Yoonjin Won

Institutions: University of California, Irvine, University of Illinois Chicago

Language: English

Topic: Heat Transfer and Boiling Studies

Subfield: Mechanical Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,500

Funders: University of California, Irvine, Multidisciplinary University Research Initiative, Office of Naval Research