Term of Award

Summer 2025

Degree Name

Master of Science, Mechanical Engineering

Document Type and Release Option

Thesis (open access)

Copyright Statement / License for Reuse

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

Department

Department of Mechanical Engineering

Committee Chair

Hayri Sezer

Committee Member 1

Hossain Ahmed

Committee Member 2

Mosfequr Rahman

Committee Member 3

Yinkai Lei

Abstract

Solid oxide fuel cells have significant advantages in renewable energy utilization due to their high efficiency, fuel flexibility, and low emissions. However, despite the numerous efforts of technology, thermal and current density gradients and impedance behavior fluctuations are still causing performance degradation. A combined computational framework that integrates machine learning and three-dimensional Multiphysics modeling is needed to investigate and optimize the performance of solid oxide fuel cells. A machine learning model, trained on synthetic microstructure data by percolation analysis, is used to predict important microstructural parameters like triple phase boundary density and geometric tortuosity. These are then employed in a Fortran-based solver coupling electrochemical, thermal, and transport phenomena across the entire cell design. Simulations are performed with different methods of anode segmentation to analyze the effect of microstructural grading on electrochemical activity and thermal distribution. The investigation uses impedance spectroscopy with a Bessler time-domain current excitation technique for investigating the origins of low-frequency inductive loops and analyzing steady-state performance. Inductive loops are usually found in the cathode and strongly depend on the reaction kinetics that are temperature-dependent and oxygen transport properties. Temperature dependence of governing equations affects inductive behavior significantly and thereby increases the influence of thermal coupling on these dynamic responses. The modeling technique allows for inclusion of accurate microstructural details without complicated image-based reconstructions. The method enables efficient simulation and analysis of spatially graded structures and dynamic processes under diverse operating conditions and may be used to investigate degradation mechanisms and optimize for better cell performance.

OCLC Number

1535301670

Research Data and Supplementary Material

No

Download

Share

COinS