Abstract

Multiphase phenomena are observed in our everyday life in nature and many industrial applications, ranging from dew condensation on insects, water harvesting, electronics cooling, climatology prediction, hydrogen generations, and manufacturing. While the fundamentals of multiphase processes have been studied for over a century, key scientific questions remain regarding the fundamental mechanisms governing complex phenomena. The intricate interplay between the evolution of phase boundaries and mass transport results in nonlinear behavior, where subtle changes in one parameter can have profound and unexpected effects on others. The multimodal, multidimensional, and transient nature of these processes presents challenges for investigation and comprehension. Additionally, interpreting experimental data and predicting multiphase phenomena remain significant challenges. Central to unraveling these complexities is the extraction of interpretable and rich datasets from dynamic visual information around phase boundaries, such as bubbles, droplets, or interfaces. To address these challenges, our research group seeks to integrate cutting-edge computer vision and machine learning strategies. In this talk, I will showcase key approaches developed by my group that reveal previously undefined features and hidden mechanisms. Furthermore, I will introduce examples illustrating how AI technologies enable learning, understanding, and prediction of the dynamic nature of multiphase phenomena. In conclusion, this talk will briefly discuss potential game-changing innovations for energy and water applications, exploring the exciting opportunities that arise from the intersection of multiphase physics and advanced technologies.

Bio

Yoonjin Won received a B.S. degree in Mechanical and Aerospace Engineering from Seoul National University, and M.S. and Ph.D. degrees in Mechanical Engineering from Stanford University. She is currently an Associate Professor of Mechanical and Aerospace Engineering at the University of California, Irvine. She has courtesy appointments in Electrical Engineering and Computer Science and Materials Science Engineering. Dr. Won's overarching research goal is to gain fundamental insights into multiphase thermal science, centering on keywords—AI for science, graphic-driven physics, data-driven approach, and materials design. She is a recipient of the National Science Foundation CAREER Award, the ASME Electronic & Photonic Packaging Division Early Career Award, the ASME Electronic & Photonic Packaging Division Women Engineer Award, the ASME ICNMM Outstanding Leadership Award, the Emerging Innovation/Early Career Innovator from UCI Beall Innovation Center, Faculty Excellence in Research Awards, Mid-Career from UCI, and numerous best paper and poster awards. The key papers are published in high impact journals including Advanced Science, Advanced Functional Materials, Small, Proceedings of National Academy of Science (PNAS), and American Chemical Society (ACS) journals. Additional details for Dr. Won’s qualifications and research group are available online (won.eng.uci.edu).

References

Suh, Y., Lee, J., Simadiris, P., Yan, X., Sett, S., Li, L., Rabbi, K., Miljkovic, N.,* Won, Y.* (2021) A Deep Learning Perspective on Dropwise Condensation, Advanced Science, DOI: 10.1002/advs.202101794. Selected for Cover Art, Highlighted in Hot Topic in ML

Suh, Y., Bostanabad, R., Won, Y.* (2021) Deep learning predicts boiling heat transfer, Scientific Reports 11, 5622. DOI: 10.1038/s41598-021-85150-4