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VISION-iT: A Framework for Digitizing Bubbles and Droplets
DOI:10.1016/j.egyai.2023.100309.png)
摘要
En 中文
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 twophase 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 largescale, 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 casespecific 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.
Keyword:
Deep learning
Computer vision
Nucleation
Heat transfer
Phase -change phenomena
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期刊
IF:
9.6
论文数:
895
被引数:
3.1K
机构
引用论文
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SCIENTIFIC REPORTS
IF3.9
Flow boiling and critical heat flux in horizontal channel with one-sided and double-sided heating单侧和双侧加热的水平通道中的流动沸腾和临界热通量
Heat Transfer through a Condensate Droplet on Hydrophobic and Nanostructured Superhydrophobic Surfaces
LANGMUIR
IF3.9

