University of Notre Dame
Browse
KIMS42024D.pdf (8.29 MB)

Large Parallel Adaptive Multiscale Modeling of Interfaces

Download (8.29 MB)
dataset
posted on 2024-05-09, 16:50 authored by Sion Kim
The computationally demanding nature of multiscale modeling (e.g., Computational Homogenization (CH)) necessitates the development of parallel and adaptive strategies for industrial-scale applications. Accordingly, this dissertation introduces an adaptive and parallel multiscale strategy for interface modeling. It adopts an adaptive approach, selecting between two microscale models through an offline database. This database utilizes nonlinear classifiers based on Support Vector Regression (SVR) constructed from microscale sampling data to serve as a preprocessing step for multiscale simulations. A co-designed parallel network library, facilitating seamless model selection, integrates tailored communication layers to ensure scalability, which is essential in parallel computing environments. This work presents a novel multiscale solver capable of executing high-fidelity, large-scale engineering simulations. The implementation of the solver is verified and validated through the application, demonstrating its ability to capture the physics observed in experimental data at both macro and micro scales. This is illustrated through the analysis of the failure of a large wind turbine blade.

History

Date Created

2024-04-15

Date Modified

2024-05-08

Defense Date

2024-04-01

CIP Code

  • 14.1901

Research Director(s)

Karel Matous

Committee Members

Matthew Zahr|Kapil Khandelwal|Ed Kinzel

Degree

  • Doctor of Philosophy

Degree Level

  • Doctoral Dissertation

Language

  • English

Library Record

006584506

OCLC Number

1433045124

Publisher

University of Notre Dame

Program Name

  • Aerospace and Mechanical Engineering

Usage metrics

    Dissertations

    Categories

    No categories selected

    Keywords

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC