VISION-iT:A Framework for Digitizing Bubbles and Droplets  

作　　者：Youngjoon Suh Sanghyeon Chang Peter Simadiris Tiffany B.Inouye Muhammad Jahidul Hoque Siavash Khodakarami Chirag Kharangate Nenad Miljkovic Yoonjin Won 

机构地区：[1]Department of Mechanical and Aerospace Engineering,University of California,Irvine,4200 Engineering Gateway,CA 92617-2700,USA [2]Department of Electrical Engineering and Computer Science,University of California,Irvine,5200 Engineering Hall,CA 92617-2700,USA [3]Department of Mechanical Science and Engineering,University of Illinois at Urbana-Champaign,Urbana,IL 61801,USA [4]Mechanical and Aerospace Engineering Department,Case Western Reserve University,Cleveland,OH,44106,USA [5]Department of Electrical and Computer Engineering,University of Illinois at Urbana-Champaign,Urbana,IL,61801,USA [6]Materials Research Laboratory,University of Illinois at Urbana-Champaign,Urbana,IL,61801,USA [7]International Institute for Carbon Neutral Energy Research(WPI-12CNER),Kyushu University,744 Moto-oka,Nishi-ku,Fukuoka 819-0395,Japan

出　　处：《Energy and AI》2024年第1期26-35,共10页能源与人工智能（英文）

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

摘　　要：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.

关 键 词：Deep learning Computer vision NUCLEATION Heat transfer Phase-change phenomena 

分 类 号：TP39[自动化与计算机技术—计算机应用技术]