VISION-iT: A Framework for Digitizing Bubbles and Droplets
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 quantifyi...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-01-01
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| Series: | Energy and AI |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666546823000812 |
| _version_ | 1797352651155832832 |
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| author | Youngjoon Suh Sanghyeon Chang Peter Simadiris Tiffany B. Inouye Muhammad Jahidul Hoque Siavash Khodakarami Chirag Kharangate Nenad Miljkovic Yoonjin Won |
| author_facet | Youngjoon Suh Sanghyeon Chang Peter Simadiris Tiffany B. Inouye Muhammad Jahidul Hoque Siavash Khodakarami Chirag Kharangate Nenad Miljkovic Yoonjin Won |
| author_sort | Youngjoon Suh |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T13:20:00Z |
| format | Article |
| id | doaj.art-bc06792bedad4b799fa6953b23fa6c22 |
| institution | Directory Open Access Journal |
| issn | 2666-5468 |
| language | English |
| last_indexed | 2024-03-08T13:20:00Z |
| publishDate | 2024-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy and AI |
| spelling | doaj.art-bc06792bedad4b799fa6953b23fa6c222024-01-18T04:18:33ZengElsevierEnergy and AI2666-54682024-01-0115100309VISION-iT: A Framework for Digitizing Bubbles and DropletsYoungjoon Suh0Sanghyeon Chang1Peter Simadiris2Tiffany B. Inouye3Muhammad Jahidul Hoque4Siavash Khodakarami5Chirag Kharangate6Nenad Miljkovic7Yoonjin Won8Department of Mechanical and Aerospace Engineering, University of California, Irvine, 4200 Engineering Gateway, CA 92617-2700, USADepartment of Mechanical and Aerospace Engineering, University of California, Irvine, 4200 Engineering Gateway, CA 92617-2700, USADepartment of Mechanical and Aerospace Engineering, University of California, Irvine, 4200 Engineering Gateway, CA 92617-2700, USADepartment of Mechanical and Aerospace Engineering, University of California, Irvine, 4200 Engineering Gateway, CA 92617-2700, USADepartment of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USADepartment of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USAMechanical and Aerospace Engineering Department, Case Western Reserve University, Cleveland, OH, 44106, USADepartment of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA; Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA; Materials Research Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA; International Institute for Carbon Neutral Energy Research (WPI-12CNER), Kyushu University, 744 Moto-oka, Nishi-ku, Fukuoka 819-0395, JapanCorresponding author.; Department of Mechanical and Aerospace Engineering, University of California, Irvine, 4200 Engineering Gateway, CA 92617-2700, USA; Department of Electrical Engineering and Computer Science, University of California, Irvine, 5200 Engineering Hall, CA 92617-2700, USAQuantifying 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.http://www.sciencedirect.com/science/article/pii/S2666546823000812Deep learningComputer visionNucleationHeat transferPhase-change phenomena |
| spellingShingle | Youngjoon Suh Sanghyeon Chang Peter Simadiris Tiffany B. Inouye Muhammad Jahidul Hoque Siavash Khodakarami Chirag Kharangate Nenad Miljkovic Yoonjin Won VISION-iT: A Framework for Digitizing Bubbles and Droplets Energy and AI Deep learning Computer vision Nucleation Heat transfer Phase-change phenomena |
| title | VISION-iT: A Framework for Digitizing Bubbles and Droplets |
| title_full | VISION-iT: A Framework for Digitizing Bubbles and Droplets |
| title_fullStr | VISION-iT: A Framework for Digitizing Bubbles and Droplets |
| title_full_unstemmed | VISION-iT: A Framework for Digitizing Bubbles and Droplets |
| title_short | VISION-iT: A Framework for Digitizing Bubbles and Droplets |
| title_sort | vision it a framework for digitizing bubbles and droplets |
| topic | Deep learning Computer vision Nucleation Heat transfer Phase-change phenomena |
| url | http://www.sciencedirect.com/science/article/pii/S2666546823000812 |
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