Computational Engineering Commons^™

Universal Systems Simulation Via Constraint Hypergraphs With Applications To Digital Twins, John Morris

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The characterization of systems encompasses a variety of modeling frameworks designed to capture specific behaviors and components of various system domains. Whatever the framework, the core elements of a system representation are the information of the system and a description of how that information is related. The relations in deterministic systems are functions, which, when composed to form executable processes, can be used to simulate system data. A declarative modeling framework is one that encodes mechanisms for preparing these simulations within the model structure, allowing an external agent to form the execution processes required for a given context. To date, …

Modeling Disaster Resilience Through A Human-Centered Lens: Exposure, Vulnerability And Adaptation, Tong Liu

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The core of reframing and operationalizing disaster resilience with a human-centered lens is to incorporate concepts from socio-ecological resilience into engineering resilience to better understand the humans’ capability for disaster adaptation. Existing studies have drawn practical implications by identifying actionable thresholds for infrastructure systems under disasters, which can be easily applied by policymakers, emergency managers and municipal agencies. However, how individuals interact with, respond to, or adapt under these infrastructure thresholds remain understudied. This hinders the operationalization of disaster resilience at the human scale.

First, I examined exposure by analyzing how configuration and distribution of urban infrastructure systems, such as …

False Information Attack Detection In A Connected Vehicle Environment With Quantum Inspired Long Short-Term Memory, Jean Michel M. Tine

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Wireless communication Systems enabling Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) data exchange, supported by technologies such as Cellular-V2X (C-V2X), has introduced significant cybersecurity challenges, particularly the threat of false information attacks that can compromise traffic safety and efficiency. In this thesis, the author focuses on identifying false information cyber-attack on V2I, in which vehicles are sending Basic Safety Messages (BSMs) to a Roadside Unit (RSU) and RSUs are collecting, processing and communicating data back to Connected Vehicles (CVs) to support different CV applications. Despite advances in anomaly detection using Long Short-Term Memory (LSTM) networks, these models often struggle with computational efficiency …

Mechanistic Insights Into Polymer-Assisted Graphene Exfoliation: The Roles Of Velocity, Adhesion, Cohesion, Temperature, Peeling Mode, And Edge Defect Via Coarse-Grained Molecular Dynamics, Linjiale Dai

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Graphene exfoliation is a critical step in the fabrication of high-quality graphene

layers. However, the underlying fracture mechanisms remain poorly understood. In this

work, I employed coarse-grained (CG) molecular dynamics (MD) simulations to

investigate how factors such as interfacial binding energy, substrate cohesion, temperature,

peeling mode, and edge defects influence the outcome of the exfoliation process. To model

polymer-assisted mechanical exfoliation, I used a finite-size system in which multilayer

graphene (MLG) is sandwiched between two thin polymer films. Leveraging the

spatiotemporal efficiency of the CG model, I performed fifty simulation iterations per

parameter set and analyzed the results from a …

Toward The Application Of Natural Language Processing In Electronic Health Record Analysis For Taxonomy Development, Latoya Mcdonald

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Electronic health records (EHRs) are pivotal resources for nurse practice because they increase the timeliness and reliability of patient information at the point of care and support access by multiple healthcare providers and the individual patients themselves. However, it is widely recognized that data extraction from EHRs is challenging due to the variability in the language used in clinical care notes and the lack of standardized terminology across healthcare systems. The broad objective of this dissertation is to develop taxonomy-based classification models for nursing care by applying feature engineering approaches to EHRs that include nursing care of ostomy patients following …

Collision Hazard Prevention And Notification For Construction Worker Safety Using Audio Surveillance, Kehinde Elelu

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The construction industry faces significant safety challenges, with collision hazards ranking as the second highest cause of annual fatalities and injuries in the United States, as reported by the Occupational Safety and Health Administration (OSHA). Current collision detection methods predominantly rely on proximity technologies, which necessitate costly and complex installations on each construction equipment piece. Furthermore, auditory situational awareness declines among workers due to hearing loss and intricate construction noises, which further heightens collision risks. This research introduces an innovative, low-cost, audio-based collision prevention technology aimed at enhancing auditory situational awareness for construction workers exposed to high noise levels. The …

Sustainable And User-Driven Scope Development Framework For Engineering Design Services Of Transportation Infrastructure Projects, Ahmad Zaki Ghafari

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Rapid economic growth has led to a significant increase in transportation demands, exerting unprecedented pressure on infrastructure systems in the United States. State transportation agencies play a vital role in maintaining and developing transportation infrastructure networks within their regions. State transportation agencies face immense pressure from federal and state governments, elected officials, and the public to deliver projects efficiently and within budget. State transportation agencies rely on professional consultants for engineering design services. Engineering design services for transportation infrastructure projects are delayed due to incomplete inaccurate scope. For state Department of Transportation (DOTs), a streamlined scope development process is essential …

Neural Operator And Physics-Informed Deep Learning Approaches For Inverse Design Of Composites And Manufacturing Processes, Minglei Lu

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In this dissertation, artificial intelligence (AI) models are designed and used to accelerate inverse design of composites and manufacturing processes. The critical bottlenecks in machine learning (ML) including data availability, data quality, model generalization and adaptation, interpretability, physical consistency, and the ’black box’ nature of models for the inverse design are addressed. And the proposed AI models are tested under different engineering scenarios. Firstly, a fast deep neural operator (DNO) structure was developed to significantly reduce training time. This model was tested in the context of additive manufacturing, a transformative industrial technology that allows for the creation of materials with …

An Efficient Computational Frameworks For Design And Analysis Of Metamaterials, Raj Pradip Khawale

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Advancement in additive manufacturing helps in building artificial lattice structures with unique properties that are not available in naturally occurring materials or in continuum structures. Specifically, beam-based lattices are well known for producing lightweight structures with very high strength, auxetic behavior, and energy absorption capabilities. In recent years, numerous research studies have been conducted on generating algorithms and frameworks to obtain unusual properties based on the variation in the cell geometry and material properties. However, the exploration of the full design space is hampered in practice primarily due to restrictions on cell tiling variation. Additionally, the lattices are very intricate, …

Exploiting Physical Side-Channel Information For Offensive And Defensive Ends, Sisheng Liang

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Side-channel information consists of side effects of computation that range from microarchitectural to physical phenomena. Empirical studies have demonstrated the practical exploitability of these side effects in real-world systems for malicious attacks and effective defenses. In this dissertation, we discover, analyze, and exploit certain physical side-channel information for end-to-end attacks and defense across three studies.

In the first study, we demonstrate a new DNN model extraction attack named Clairvoyance that exploits certain far-field electromagnetic signals emitted from a GPU to steal DNN models several meters away from the victim machine, even with some physical obstacles in between. Using Clairvoyance, an …

Large-Scale Hpc-Empowered Power Electronics Modeling And Simulation In Photovoltaic Applications, Liwei Wang

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The rising popularity of renewable energy sources requires advanced, efficient power electronic systems for energy conversion, grid integration, and system management, thereby raising expectations for power electronics in the energy industry. The complexity of modern power electronic systems requires comprehensive simulations and in-depth analysis to predict performance accurately, but this process is impeded by prolonged simulation times. The primary objective of this dissertation is to develop a high-fidelity, high-speed event-driven simulator to tackle challenges related to mass data processing, uncertainty evaluation, as well as modeling and simulation issues in assessing the reliability of power electronics in large-scale Photovoltaic (PV) systems. …

Numerical Simulation Of Laser Induced Elastic Waves In Response To Short And Ultrashort Laser Pulses., Alireza Zarei

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In an era of intensified market competition, the demand for cost-effective, high-quality, high-performance, and reliable products continues to rise. Meeting this demand necessitates the mass production of premium products through the integration of cutting-edge technologies and advanced materials while ensuring their integrity and safety. In this context, Nondestructive Testing (NDT) techniques emerge as indispensable tools for guaranteeing the integrity, reliability, and safety of products across diverse industries.

Various NDT techniques, including ultrasonic testing, computed tomography, thermography, and acoustic emissions, have long served as cornerstones for inspecting materials and structures. Among these, ultrasonic testing stands out as the most prevalent method, …

Quantifying Hurricane Effects On Housing: Evaluating Damage, Loss, And Shelter Demands Using Historical And Simulated Storm Tracks, Adish Deep Shakya

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This research introduces an advanced framework which employs parametric wind field models for peak wind speeds, and building fragility curves, loss functions, and demographic data to estimate for estimating housing damage and loss. The uninhabitable units immediate displaced households, short-term and long-term shelter need households are determined. with a particular focus on those eligible for FEMA assistance. The framework's validity is reinforced by a high correlation in the analysis of recent hurricane events between estimated numbers of displaced households and actual FEMA aid recipients, where FEMA aids about 20-60% of the predicted long-term displaced households. A novel application of the …

Machine Learning-Based Data And Model Driven Bayesian Uncertanity Quantification Of Inverse Problems For Suspended Non-Structural System, Zhiyuan Qin

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Inverse problems involve extracting the internal structure of a physical system from noisy measurement data. In many fields, the Bayesian inference is used to address the ill-conditioned nature of the inverse problem by incorporating prior information through an initial distribution. In the nonparametric Bayesian framework, surrogate models such as Gaussian Processes or Deep Neural Networks are used as flexible and effective probabilistic modeling tools to overcome the high-dimensional curse and reduce computational costs. In practical systems and computer models, uncertainties can be addressed through parameter calibration, sensitivity analysis, and uncertainty quantification, leading to improved reliability and robustness of decision and …

Explainable Physics-Informed Deep Learning For Rainfall-Runoff Modeling And Uncertainty Assessment Across The Continental United States, Sadegh Sadeghi Tabas

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Hydrologic models provide a comprehensive tool to calibrate streamflow response to environmental variables. Various hydrologic modeling approaches, ranging from physically based to conceptual to entirely data-driven models, have been widely used for hydrologic simulation. During the recent years, however, Deep Learning (DL), a new generation of Machine Learning (ML), has transformed hydrologic simulation research to a new direction. DL methods have recently proposed for rainfall-runoff modeling that complement both distributed and conceptual hydrologic models, particularly in a catchment where data to support a process-based model is scared and limited.

This dissertation investigated the applicability of two advanced probabilistic physics-informed DL …

Elucidation Of Active Site And Mechanism Of Metal Catalysts Supported In Nu-1000, Hafeera Shabbir

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Advances in extraction of shale oil and gas has increased the production of geographically stranded natural gas (primarily consisting of methane (C1) and ethane (C2)) that is burned on site. A potential utilization strategy for shale gas is to convert it into fuel range hydrocarbons by catalytic dehydrogenation followed by oligomerization by direct efficient catalysts. This work focuses on understanding metal cation catalysts supported on metal-organic framework (MOF) NU-1000 that will actively and selectively do this transformation under mild reaction conditions, while remaining stable to deactivation (via metal agglomeration or sintering). I built computational models validated by experimental methods to …

Quantum-Mechanical Evaluation Of Defects In Uranium-Bearing Materials, Megan Hoover

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Quantum-mechanical calculations using density functional theory with the generalized gradient approximation were employed to investigate the effects dopants have on the uranium dioxide (UO₂) structure. Uraninite is a common U⁴⁺ mineral in the Earth's crust and an important material used to produce energy and medical isotopes. Though the incorporation mechanism remains unclear, divalent cations are known to incorporate into the uranium dioxide system. Three charge-balancing mechanisms were evaluated to achieve a net neutral system, including the substitution of (1) a divalent cation for a tetravalent uranium atom and oxygen atom; (2) two divalent cations for a tetravalent …

Characterization Of Friction Element Welding Using Finite Element Modeling, Ankit Varma

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Friction element welding (FEW) has been advocated as a solution to weld different materials together, with the ability to join high-strength materials for a range of thicknesses with low input energy and a short processing time. This work develops a coupled thermal-mechanical finite element model to better understand the physical mechanisms involved in the process and to predict temperature and material flow during the process. Furthermore, microstructural analysis is performed for the steel layer using a scanning electron microscope and Vickers microhardness tester to understand the variation in its grain structure and hardness. Results from the finite element model and …

Computational Study Of Dense Gas Dispersion In Urban Areas, Rasna Sharmin

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A series of steady-state simulations have been conducted to investigate removal of dense gas from a simple square canyon formed between two square cross-section obstacles. Due to urbanization and industrialization, there always lies a high risk of exposure to harmful pollutants which can result from accidental release of toxic gasses. Those are often denser than the atmosphere. and can easily get trapped in between buildings in urban canopies. It is important to have full understanding of flushing mechanism of dense fluid inside urban canopies by steady turbulent flow because the exposure to these toxic dense gasses can be catastrophic. There …