VISION-iT: A Framework for Digitizing Bubbles and Droplets
Youngjoon Suh,
Sanghyeon Chang,
Peter Simadiris,
Tiffany B. Inouye,
Muhammad Jahidul Hoque,
Siavash Khodakarami,
Chirag Kharangate,
Nenad Miljkovic,
Yoonjin Won
Affiliations
Youngjoon Suh
Department of Mechanical and Aerospace Engineering, University of California, Irvine, 4200 Engineering Gateway, CA 92617-2700, USA
Sanghyeon Chang
Department of Mechanical and Aerospace Engineering, University of California, Irvine, 4200 Engineering Gateway, CA 92617-2700, USA
Peter Simadiris
Department of Mechanical and Aerospace Engineering, University of California, Irvine, 4200 Engineering Gateway, CA 92617-2700, USA
Tiffany B. Inouye
Department of Mechanical and Aerospace Engineering, University of California, Irvine, 4200 Engineering Gateway, CA 92617-2700, USA
Muhammad Jahidul Hoque
Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
Siavash Khodakarami
Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
Chirag Kharangate
Mechanical and Aerospace Engineering Department, Case Western Reserve University, Cleveland, OH, 44106, USA
Nenad Miljkovic
Department 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, Japan
Yoonjin Won
Corresponding 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, USA
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.
