Date of Award

Fall 2024

Document Type

Open Access Thesis

Department

Mechanical Engineering

First Advisor

Austin Downey

Abstract

As engineering systems increase in scale and complexity in the era of the Fourth Industrial Revolution, data-driven solutions will become essential in enabling the next generation of these systems. One of the trending tools that can aid in this transition is digital twins. As physical systems degrade throughout their life cycles, their behavior also changes. Digital twins use data assimilation to continuously update virtual models to represent the current state of their physical counterparts. A reliable digital twin can be leveraged by a system operator to perform diagnostics, optimize, and tests without ever needing the physical system. However, implementing effective digital twins involves overcoming challenges such as ensuring model accuracy and minimizing latency between the physical system and its virtual representation. This work proposes an updating scheme that utilizes real-time sensor data and a particle swarm optimization algorithm to update model parameters for continuous virtual model calibration within a digital twin framework. The PSO algorithm iterates through different multi-physics model configurations to reduce the discrepancy between the physical and virtual spaces. All computations are performed on edge devices, aligning with real-time constraints for high-performance applications that require on-site data processing. To evaluate the performance of this methodology, it was implemented on two electro-thermal systems designed to emulate the power and energy systems of a naval ship. Results demonstrate that the updating scheme can effectively update a digital twin in a reasonable amount of time, guarantee a higher level of accuracy, and adapt to external changes in its physical counterpart. This work aims to provide a novel model updating scheme that operates within a digital twin frame to boost system resilience and adaptability. This approach sets the stage for more robust and autonomous applications across various engineering fields as they evolve alongside emerging technological demands.

Rights

© 2025, Braden Robert Priddy

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