Doctoral Dissertations

Abstract

The growing demand for high-efficiency energy systems and advanced manufacturing technologies requires predictive frameworks that link atomic-scale physics with engineering-scale performance. This dissertation develops a unified multiscale modeling approach that integrates molecular dynamics, density functional theory, finite-element analysis, and machine learning to connect structure, transport, and performance across materials and processes. Depending on the interactions involved, the framework employs loose coupling for parameter transfer, tight coupling for two-way feedback, and hybrid coupling where machine-learning surrogates accelerate high-fidelity simulations while retaining physical interpretability. In electrochemical systems, an XGBoost-enhanced single-particle model reproduces P2D-level electrolyte potential dynamics at roughly one-hundredth the computational cost, enabling real-time diagnostics for lithium-ion batteries. Multiscale studies further show that ionic transport in Ba-doped Li₃OCl solid-state electrolytes is dominated by amorphous regions and limited at crystalline and interfacial domains by electrostatic potential mismatch and Coulombic blocking. For nonlinear electrical contacts, a coupled FEA-MD approach demonstrates that oxide fracture at asperities drives the transition from metal–insulator–metal tunneling to metallic conduction, explaining contact-resistance instability in aluminum connectors. In wire-arc additive manufacturing, continuum-scale thermal histories inform atomistic solidification, while a graph-neural-network-assisted MD model cuts atomic simulation cost by over 50 % without loss of structural fidelity.

Advisor(s)

Park, Jonghyun

Committee Member(s)

Hwang, Chulsoon
Chandrashekhara, K.
Du, Xiaosong
Liang, Zhi

Department(s)

Mechanical and Aerospace Engineering

Degree Name

Ph. D. in Mechanical Engineering

Publisher

Missouri University of Science and Technology

Publication Date

Fall 2025

Journal article titles appearing in thesis/dissertation

Paper I, found on pages 14-32, has been published in Applied Physics Letters.

Paper II, found on pages 33-67, is intended for submission to the Journal of Power Sources.

Paper III, found on pages 68–88, is intended for submission to ACS Applied Energy Materials.

Paper IV, found on pages 89-130, is intended for submission to Acta Materialia.

Paper V, found on pages 131-167, is intended for submission to IEEE Transactions on Components, Packaging, and Manufacturing Technology (TCPMT).

Paper VI, found on pages 168-201, is prepared for submission to Additive Manufacturing.

Pagination

xx, 206 pages

Note about bibliography

Includes_bibliographical_references_(pages 30, 62, 86, 123, 164, 199)

Rights

© 2025 Emmanuel Olugbade , All Rights Reserved

Document Type

Dissertation - Open Access

File Type

text

Language

English

Thesis Number

T 12575

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