Date of Award

Spring 2020

Document Type

Open Access Dissertation


Chemical Engineering

First Advisor

Sirivatch Shimpalee

Second Advisor

John R. Regalbuto


The direct modeling-based Lattice Boltzmann Agglomeration Method (LBAM) is used to explore the electrochemical kinetics and multi-scalar/multi-physics transport inside the detailed structure of the porous and catalyst layers inside polymer electrolyte membrane fuel cells (PEMFCs). The complete structure of the samples is obtained by both micro- and nano- X-ray computed tomography (CT). LBAM is able to predict the electrochemical kinetics in the nanoscale catalyst layer and investigate the electrochemical variables during cell operation. This work shows success in integrating the lattice elements into an agglomerate structure in the catalyst layer (CL). The predictions using LBAM were compared and validated with a macro-kinetics model, ex-situ, and in-situ flow visualization. The studies include prediction of water evolution, water saturation, breakthrough pressure, heat transfer, species transport, and electrochemical kinetics inside the porous and catalyst layers under different conditions that can occur in fuel cells. The overall predictions reveal that the local saturation of liquid water, distributions of electrochemical variables, and mass fraction across the samples can be controlled by the regulation of operating conditions, especially under conditions that cause transport losses. LBAM is a highly effective method of predicting the partial flooding issue, understanding the transport resistance, and investigating transport inside the porous transport layer that affects the overall cell performance in the PEMFC.