Title: Multiphase flow in porous media with applications to the energy system
Time: 10:00 to 11:00, Friday, December.29, 2023
Place: F301, School of Mechanical Engineering
Host: WU Rui, Assist.Professor (Institute of Engineering Thermophysics)

Biography
Benzhong (Robin) Zhao is an Assistant Professor in the Department of Civil Engineering at McMaster University in Ontario, Canada. Dr. Zhao’s background is in civil & environmental engineering, specializing in multiphase flow in porous media with applications to low-carbon energy systems. He earned his BASc from the University of Waterloo, SM and PhD from the Massachusetts Institute of Technology, where he worked on the fluid mechanics of geological carbon dioxide sequestration. He completed a Postdoctoral Fellowship at the University of Toronto, where he worked on developing more efficient electrochemical devices for renewable energy storage.

Abstract
Multiphase flows in porous media are ubiquitous in modern energy systems, whose applications include recovery of oil and gas from hydrocarbon-bearing reservoirs, geological sequestration of carbon dioxide in saline aquifers, and renewable energy generation and storage using electrochemical devices. Micro-scale physical mechanisms such as capillarity and wettability play a key role in these systems, since the narrowly confined pore spaces of the porous media enable intimate contact between the fluid and the solid. Additionally, this strong fluid-solid coupling at the micro-scale can often have an important, complex and often poorly understood impact on macro-scale fluid flows. This talk will discuss our recent work on i) multiphase flow in porous media with heterogeneous wettability conditions (mixed-wet), which is prevalent in subsurface formations; ii) engineered porous transport layers in polymer electrolyte membrane (PEM) electrolyzers, which is a promising technology for renewable energy storage. iii) permeability prediction of digital porous media using coupled convolutional neural network and physics-informed neural network.