Research Expertise and Interest

mechanical engineering, statistical thermodynamics, near-interface micro- and nanoscale thermophysics and transport in liquid-vapor systems, computational modeling and simulation of energy conversion and transport processes at scales ranging from the molecular level to the device and system level, use of machine learning to enhance energy technology research and the efficiency of energy conversion and transport in applications, and use of machine learning to create next-generation energy technologies that autonomously adapt to maximize their performance and reduce their environmental impact .

Research Description

Since joining the Berkeley faculty in 1982, Professor Carey’s research has spanned a variety of applications areas, including high performance solar thermal power systems, building and vehicle air conditioning, smelting and casting of aluminum, phase change thermal energy storage, heat pipes for aerospace applications, high heat flux cooling of electronics, data center thermal management, and energy efficiency of digital information systems.  His research has also contributed to developing advanced heat rejection technologies for electronics cooling, building AC systems, and power plants, and developed performance models for Tesla turbine expanders for green energy conversion technologies, and thermionic power generation technologies for space applications.

Carey’s current research emphasizes development of strategies to use machine learning tools to better understand and model flame spread processes in electronic systems and the physics of boiling heat transfer at surfaces covered with hydrophilic nanostructured coatings.  This includes exploring innovative ways to combine advanced instrumentation data and machine learning image analysis to understand the physics of boiling processes.   He is also using machine learning tools to enhance performance modeling of energy conversion devices, and developing machine-learning-based adaptive energy conversion systems that can autonomously adjust their operation to simultaneously maximize energy efficiency and meet operating requirements for the application of interest.