Postdoctoral Associate, Department of Mechanical Engineering, Massachusetts Institute of Technology
Liquid-vapor phase change processes are ubiquitous phenomena in nature and energy industries such as steam-based power plant, thermal management, and water desalination. Developing viable thermal energy strategies through nanoengineered materials and machine learning approaches will have enormous impact on global energy crisis and climate change. In this talk, I will first discuss fundamental studies of wetting and droplet manipulation on nanoengineered surfaces, and the role of surface wettability on enhancing condensation heat transfer. I will address the governing physics behind dropwise and jumping-droplet condensation from smooth hydrophilic surfaces to micro/nanostructured superhydrophobic surfaces. Next, I will discuss the machine learning-assisted models to predict and optimize boiling heat transfer performance on scalable random structured surfaces. Furthermore, the development of image segmentation techniques allows us to extract physical parameters from heat transfer surface profiles and to fundamentally understand bubble nucleation dynamics during boiling. These studies provide fundamental insights into the complex physical processes underlying solid-liquid interactions and offer paths to achieving increased efficiency in next generation energy systems.
Dr. Hyeongyun Cha is a Postdoctoral Associate in the Department of Mechanical Engineering at the Massachusetts Institute of Technology (MIT) under the supervision of Prof. Evelyn Wang. Hyeongyun received his Ph.D. degree in Mechanical Engineering from the University of Illinois Urbana-Champaign (UIUC) under the guidance of Prof. Nenad Miljkovic. His research intersects the multidisciplinary fields of thermo-fluid science, interfacial phenomena, and machine learning. His doctoral work focused on micro/nanoengineered surfaces to enhance condensation heat transfer and fundamental studies probing the origin of hydrophobic coating degradation. At MIT, he is currently developing machine learning-assisted models to understand and optimize boiling heat transfer for emerging energy applications. He is the recipient of the PPG-Materials Research Laboratory Graduate Research Assistantship Award and the Mavis Future Faculty Fellow Award at UIUC.
Event Date: December 19, 2022 at 11:00 AM