VISION-iT: A Framework for Digitizing Bubbles and Droplets

Abstract

Quantifying the nucleation processes involved in liquid-vapor phase-change phenomena, while dauntingly challenging, is central in designing energy conversion and thermal management systems. Recent technological advances in the deep learning and computer vision field offer the potential for quantifying such complex two-phase nucleation processes at unprecedented levels. By leveraging these new technologies, a multiple object tracking framework called “vision inspired online nuclei tracker (VISION-iT)” has been proposed to extract large-scale, physical features residing within boiling and condensation videos. However, extracting high-quality features that can be integrated with domain knowledge requires detailed discussions that may be field- or case-specific problems. In this regard, we present a demonstration and discussion of the detailed construction, algorithms, and optimization of individual modules to enable adaptation of the framework to custom datasets. The concepts and procedures outlined in this study are transferable and can benefit broader audiences dealing with similar problems.

Keywords

• Deep learning
• Computer vision
• Nucleation
• Heat transfer
• Phase-change phenomena