Yoonjin Won, associate professor of mechanical & aerospace engineering, has won a USD 6M multidisciplinary university research initiative from the U.S. Department of Defence for her project to improve the efficiency of thermal management systems for Navy power and energy applications. The UC Irvine-led project, “Fundamentals of Machine Learning for Phase Change Heat Transfer,” aims to develop an intelligent framework for liquid-vapour phase change physics that integrates advanced metrology with computer vision and machine learning.
Phase change heat transfer, involving boiling or condensation, has been used for over 100 years in energy and power systems. It is an essential element of modern building systems, transportation, refrigeration and power generation. The physics of phase change heat transfer are incredibly complicated due to their chaotic nature and the multiple physical processes present in these systems. The extreme complexity of phase change heat transfer makes it nearly impossible to perform modelling and simulation that can predict phase change flows and be used to design phase change systems.