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 ...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.展开更多
基金funding support from the Office of Naval Research(ONR)under Grant No.N00014-22-1-2063from the National Science Foundation(NSF)under CBET-TTP 2045322+2 种基金funding support from the under grant No.N00014-21-1-2089funding support from the International Institute for Carbon Neutral Energy Research(WPI-I2CNER)sponsored by the Japanese Ministry of Education,Culture,Sports,Science and Technology.
文摘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.
