Engineering Commons^™

Pulse Width Modulation-Based Voltage Balancing And Circulating Current Control For Modular Multilevel Converters, Md Multan Biswas

Theses and Dissertations

In the last decade, because of some key features and advantages of Modular Multilevel Converter (MMC), it is widely applied for various Medium Voltage (MV) and High Voltage (HV) power electronic interfacing applications. The structure of MMCs varies by application but in general, they are composed of multiple Sub-Modules (SMs) which each contains a floating capacitor. For the operation of MMCs, capacitor voltages are required to remain equally set. This is referred to as the balanced condition and requires a method for SM Capacitor Voltage Balancing (CVB). Connections of SMs for most of the common MMC configurations result in additional …

Liquid Phase Modeling In Metal Catalysis And In Zeolites, Subrata Kumar Kundu

Theses and Dissertations

Liquid phase processing is often desired for selective product formation, such as during catalytic conversion of biomass, and for purification of waste-water streams. Since the liquid phase environment influences the adsorption properties and reactivity of solid catalysts and porous materials, significant performance changes have been observed as a function of solvent properties for various industrial catalytic and separation processes. There is a need to better understand these solvation effects on processes at solid-liquid interfaces and to develop novel multiscale models that permit one to explore solvation effects on surface catalyzed reactions and during adsorption in porous media such as zeolites. …

Thermodynamic Assessment Of Chromium Corrosion In The Na-K-Mg-U(Iii, Iv) Chloride Salt, Jacob Allen Yingling

Theses and Dissertations

Thermodynamic descriptions of high-order molten chloride salt systems are essential for the development of fast molten salt reactor (MSR) technologies. However, a complete thermodynamic assessment of the essential Na-K-Mg-U(III)-U(IV) molten chloride salt with the most prevalent CrCl2 corrosion product has yet to be provided. This is remedied in the present work through application of the CALculation of PHAse Diagrams (CALPHAD) approach to the available thermodynamic data and new measurements that include differential scanning calorimetry (DSC) observations for previously unexplored two and three component chloride salt systems. Through these efforts, a unique approach was developed for the quantification of uncertainty in …

Use Of Dic Measurements With Finite Element Models For Direct Heterogeneous Material Property Determination And Wrinkle Formation During Automated Fiber Placement, Sreehari Rajan Kattil

Theses and Dissertations

In this work, application of DIC in combination with finite element modeling is demonstrated for efficient material characterization for complex heterogeneous materials and tow wrinkle formation during automated fiber placement.

The first part of the work describes the integration of DIC measurements with finite element models for direct property identification in heterogeneous material systems. The material property identification is based on solving partial differential equations (PDE) of equilibrium for unknown elastic material properties with known boundary conditions and full field strain data (Eg. DIC). The PDEs are solved numerically using Petrov-Galerkin finite element procedure. The classical Bubnov-Galerkin method is shown …

Closed Form Implicitly Integrated Models For Computationally Efficient Simulation Of Power Electronics, Andrew Wunderlich

Theses and Dissertations

This work describes novel closed-form, implicitly integrated (CF-implicit) models of switched-mode power converters which feature implicit integration but require no iterative numerical solving algorithm for evaluation because they are explicitly solved prior to model execution. The derived models capture the large-signal dynamic behavior of the power converters, so their use and accuracy are not limited to any one set of operating conditions. These models can be implemented in any computational environment, including directly on an existing embedded controller as a digital twin. Since no iterative solver is required, the models are highly computationally efficient and have a very predictable worst-case …

Cyclic Response And Liquidfaction Behavior Of Gravelly Soils, Pitak Ruttithivaphanich

Theses and Dissertations

Cyclic response and liquefaction behavior are soil mechanisms under earthquake conditions. The study of dynamic soil response is important to evaluate soil behaviors under dynamic loading for a wide range of strains. Soil liquefaction phenomena involving a significant reduction in the strength and stiffness of saturated cohesionless soils has caused catastrophic ground failure in numerous earthquakes. Liquefaction mostly occurs in sand, but liquefaction of gravelly soil has been observed. To date, only a few studies have focused on the liquefaction of gravelly soils; hence, research data is limited. This research aims to improve the understanding of cyclic response and liquefaction …

Empirical Studies On Automated Software Testing Practices, Alireza Salahirad

Theses and Dissertations

Software testing is notoriously difficult and expensive, and improper testing carries economic, legal, and even environmental or medical risks. Research in software testing is critical to enabling the development of the robust software that our society relies upon. This dissertation aims to lower the cost of software testing without decreasing the quality by focusing on the use of automation. The dissertation consists of three empirical studies on aspects of software testing. Specifically, these three projects focus on (1) mapping the connections between research topics and the evolution of research topics in the field of software testing, (2) an assessment of …

Learning To Automate Follow-Up Question Generation Using Process Knowledge For Depression Triage On Reddit Posts, Shrey Gupta, Anmol Agarwal, Manas Gaur, Kaushik Roy, Vignesh Narayanan, Ponnurangam Kumaraguru, Amit Sheth

Publications

Conversational Agents (CAs) powered with deep language models (DLMs) have shown tremendous promise in the domain of mental health. Prominently, the CAs have been used to provide informational or therapeutic services (e.g., cognitive behavioral therapy) to patients. However, the utility of CAs to assist in mental health triaging has not been explored in the existing work as it requires a controlled generation of follow-up questions (FQs), which are often initiated and guided by the mental health professionals (MHPs) in clinical settings. In the context of `depression', our experiments show that DLMs coupled with process knowledge in a mental health questionnaire …

A Novel Self-Assembled Cobalt-Free Perovskite Composite Cathode With Triple-Conduction For Intermediate Proton-Conducting Solid Oxide Fuel Cells, Hua Tong, Min Fu, Yang Yang, Fanglin Chen, Zetian Tao

Faculty Publications

A traditional composite cathode for proton-conducting solid oxide fuel cells (H-SOFCs) is typically obtained by mixing cathode materials and proton conducting electrolyte of BaCe_0.7Y_0.2Z_r0.1O_3–δ (BZCY), providing chemical and thermal compatibility with the electrolyte. Here, a series of triple-conducing and cobalt-free iron-based perovskites as cathodes for H-SOFCs is reported. Specifically, BaCe_xFe_1–xO_3–δ (x = 0.36, 0.43, and 0.50) shows various contents of two single phase perovskites with an in situ heterojunction structure as well as triple conductivity by tailoring the Ce/Fe ratios. The cell performance with the optimized BaCe_{0.36 …}

Towards Efficient Scoring Of Student-Generated Long-Form Analogies In Stem, Thilini Wijesiriwardene, Ruwan Wickramarachchi, Valerie L. Shalin, Amit P. Sheth

Publications

Switching from an analogy pedagogy based on comprehension to analogy pedagogy based on production raises an impractical manual analogy scoring problem. Conventional symbol-matching approaches to computational analogy evaluation focus on positive cases, and challenge computational feasibility. This work presents the Discriminative Analogy Features (DAF) pipeline to identify the discriminative features of strong and weak long-form text analogies. We introduce four feature categories (semantic, syntactic, sentiment, and statistical) used with supervised vector-based learning methods to discriminate between strong and weak analogies. Using a modestly sized vector of engineered features with SVM attains a 0.67 macro F1 score. While a semantic feature …

Mathematical Model For Sei Growth Under Open-Circuit Conditions, Shiv Krishna Madi Reddy, Wei Shang, Ralph E. White

Faculty Publications

A solid electrolyte interphase (SEI) growth model is developed in a mixed mode that contains solvent diffusion through the SEI layer and corresponding solvent reduction kinetics at the SEI/electrode interface. The governing equations are solved by the Landau transformation, which makes the moving layer fixed to predict the open circuit potential, SEI layer thickness, and capacity loss. The estimated parameters fitted with experimental data from the literature are computed using COMSOL and MATLAB. Results show that the mixed mode model predicts lower capacity loss and thinner SEI layer due to its growth under open circuit conditions than previously reported by …

Overview Of Arbitrarily High-Order Adjoint Sensitivity And Uncertainty Quantification Methodology For Large-Scale Systems, Dan Gabriel Cacuci

Faculty Publications

This work reviews from a unified viewpoint the concepts underlying the “nth-Order Comprehensive Adjoint Sensitivity Analysis Methodology for Response-Coupled Forward/Adjoint Linear Systems” (nth-CASAM-L) and the “nth-Order Comprehensive Adjoint Sensitivity Analysis Methodology for Nonlinear Systems” (nth-CASAM-N) methodologies. The practical application of the nth-CASAM-L methodology is illustrated for an OECD/NEA reactor physics benchmark, while the practical application of the nth-CASAM-N methodology is illustrated for a nonlinear model of reactor dynamics that exhibits periodic and chaotic oscillations. As illustrated both by the general theory and by the examples reviewed in this work, both the nth-CASAM-L and nth-CASAM-N methodologies overcome the curse of dimensionality …

Advances In High-Order Sensitivity Analysis For Uncertainty Quantification And Reduction In Nuclear Energy Systems, Dan Gabriel Cacuci

Faculty Publications

No abstract provided.

Sensitivity Analysis, Uncertainty Quantification And Predictive Modeling Of Nuclear Energy Systems, Dan Gabriel Cacuci

Faculty Publications

No abstract provided.

Strain And Strain Rate In Friction Extrusion, Xiao Li, Md Reza-E- Rabby, Anthony Guzman, Glenn Grant, Suveen Mathaudhu, Michah Hinton, Anthony P. Reynolds

Faculty Publications

Friction extrusion is a metal forming process that generates large plastic strains and deformation-induced heating through friction between a rotating die and the material to be extruded. The final deformation state in friction extruded wire has been visualized in previous studies but the distribution and evolution of strain and strain rate inside the solid material have yet to be elucidated. This paper develops an approach that reveals the deformation during the process by visualizing the shape change of the pre-embedded markers in the remnant billet. For the first time, the distribution of steady-state strain and strain rate in friction extrusion …

Analysis Of The Temperature Characteristics Of High-Speed Train Bearings Based On A Dynamics Model And Thermal Network Method, Baosen Wang, Yongqiang Liu, Bin Zhang, Wenqing Huai

Faculty Publications

High-speed trains often use temperature sensors to monitor the motion state of bearings. However, the temperature of bearings can be affected by factors such as weather and faults. Therefore, it is necessary to analyze in detail the relationship between the bearing temperature and influencing factors. In this study, a dynamics model of the axle box bearing of high-speed trains is established. The model can obtain the contact force between the rollers and raceway and its change law when the bearing contains outer-ring, inner-ring, and rolling-element faults. Based on the model, a thermal network method is introduced to study the temperature …

Liquid-Phase Effects On Adsorption Processes In Heterogeneous Catalysis, Mehdi Zare, Mohammad S. Saleheen, Nirala Singh, Mark J. Uline, Muhammad Faheem, Andreas Heyden

Faculty Publications

Aqueous solvation free energies of adsorption have recently been measured for phenol adsorption on Pt(111). Endergonic solvent effects of ∼1 eV suggest solvents dramatically influence a metal catalyst's activity with significant implications for the catalyst design. However, measurements are indirect and involve adsorption isotherm models, which potentially reduces the reliability of the extracted energy values. Computational, implicit solvation models predict exergonic solvation effects for phenol adsorption, failing to agree with measurements even qualitatively. In this study, an explicit, hybrid quantum mechanical/molecular mechanical approach for computing solvation free energies of adsorption is developed, solvation free energies of phenol adsorption are computed, …

Generative Design Of Stable Semiconductor Materials Using Deep Learning And Density Functional Theory, Edirisuriya M. Dilanga Siriwardane, Yong Zhao, Indika Perera, Jianjun Hu

Faculty Publications

Semiconductor device technology has greatly developed in complexity since discovering the bipolar transistor. In this work, we developed a computational pipeline to discover stable semiconductors by combining generative adversarial networks (GAN), classifiers, and high-throughput first-principles calculations. We used CubicGAN, a GAN-based algorithm for generating cubic materials and developed a classifier to screen the semiconductors and studied their stability using first principles. We found 12 stable AA’ MH₆ semiconductors in the F-43m space group including BaNaRhH₆, BaSrZnH₆, BaCsAlH₆, SrTlIrH₆, KNaNiH₆, NaYRuH₆, CsKSiH₆, CaScMnH₆, YZnMnH …

A Primer On The Factories Of The Future, Noble Anumbe, Clint Saidy, Ramy Harik

Faculty Publications

In a dynamic and rapidly changing world, customers’ often conflicting demands have continued to evolve, outstripping the ability of the traditional factory to address modern-day production challenges. To fix these challenges, several manufacturing paradigms have been proposed. Some of these have monikers such as the smart factory, intelligent factory, digital factory, and cloud-based factory. Due to a lack of consensus on general nomenclature, the term Factory of the Future (or Future Factory) has been used in this paper as a collective euphemism for these paradigms. The Factory of the Future constitutes a creative convergence of multiple technologies, techniques, and capabilities …

Piezoelectric Point-Of-Care Biosensor For The Detection Of Sars-Cov-2 (Covid-19) Antibodies, Debdyuti Mandal, Mustahseen M. Indaleeb, Alexandra Younan, Sourav Banerjee

Faculty Publications

It is always challenging to diagnose a disease using a biosensor reliably, and quickly with high sensitivity and selectivity, simultaneosuly. Recently the world experienced a global pandemic caused by a novel coronavirus (COVID-19). Although the vaccines are available, COVID-19 resulted a huge threat to the entire world with high mortality rates. Irrespective of a specific disease, there is a constant need for a cheaper and faster in-vitro, lab-on-a-chip sensor with high sensitivity and selectivity. Such sensors will not only facilitate the disease detection but will expedite and vaccine development process through detection of its corresponding antibodies when developed. In this …

Securing Information On A Web Application System To Facilitate Online Blood Donation Booking, Hrishitva Patel

Faculty Publications

Blood donation has saved many lives in the past. According to statistics presented by the American Red Cross, a patient is in need of a blood transfusion every two seconds. There are many benefits that arise from blood donation to both the donor and the blood recipients. With blood donation, cancer patients, people involved in accidents, or those battling diseases that require blood donation have access to enough blood to sustain their survival. There is a need to digitize the blood donation booking to facilitate blood donation across the United States, and ensure patients in need of blood, receive their …

Predicting Lattice Vibrational Frequencies Using Deep Graph Neural Networks, Nghia Nguyen, Steph-Yves V. Louis, Lai Wei, Kamal Choudhary, Ming Hu, Jianjun Hu

Faculty Publications

Lattice vibrational frequencies are related to many important materials properties such as thermal and electrical conductivity as well as superconductivity. However, computational calculation of vibrational frequencies using density functional theory methods is computationally too demanding for large number of samples in materials screening. Here we propose a deep graph neural network based algorithm for predicting crystal vibrational frequencies from crystal structures. Our algorithm addresses the variable dimension of vibrational frequency spectrum using the zero padding scheme. Benchmark studies on two data sets with 15,000 mixed-structure and 35,552 rhombohedra samples show that the aggregated R2 scores of the prediction reach …

High-Throughput Computational Evaluation Of Lattice Thermal Conductivity Using An Optimized Slack Model, Guangzhao Qin, An Huang, Yinqiao Liu, Huimin Wang, Zhenzhen Qin, Xue Jiang, Jijun Zhao, Jianjun Hu, Ming Hu

Faculty Publications

High-throughput computational screening of materials with targeted thermal conductivity (κ) plays an important role in promoting the advancement of material design and enormous applications. The Slack model has been widely applied for the fast evaluation of κ with minimal time and resources, showing the potential capability of high-throughput screening of κ. However, after examining the Slack model on a large set of 353 materials, a huge discrepancy is found between the predicted κ and the correspondingly measured κ in experiments for some materials in addition to the generally overestimated κ by the Slack model. Thus, it is …

Optimised Adjoint Sensitivity Analysis Using Adjoint Guided Mesh Adaptivity Applied To Neutron Detector Response Calculations, Andrew G. Buchan, Dan Gabriel Cacuci, Steven Dargaville, Christopher C. Pain

Faculty Publications

This article presents a new approach for the efficient calculation of sensitivities in radiation dose estimates, subject to imprecisely known nuclear material cross-section data. The method is a combined application of adjoint-based models to perform, simultaneously, both the sensitivity calculation together with optimal adaptive mesh refinement. Adjoint-based sensitivity methods are known for their efficiency since they enable sensitivities of all parameters to be formed through only two solutions to the problem. However, the efficient solutions can also be obtained by their computation on optimal meshes, here guided by goal-based adjoint approaches. It is shown that both mesh adaptivity and sensitivity …

Integrated Socio-Environmental Vulnerability Assessment Of Coastal Hazards Using Data-Driven And Multi-Criteria Analysis Approaches, Ahad Hasan Tanim, Erfan Goharian, Hamid Moradkhani

Faculty Publications

Coastal hazard vulnerability assessment has been centered around the multi-variate analysis of geo-physical and hydroclimate data. The representation of coupled socio-environmental factors has often been ignored in vulnerability assessment. This study develops an integrated socio-environmental Coastal Vulnerability Index (CVI), which simultaneously combines information from five vulnerability groups: biophysical, hydroclimate, socio-economic, ecological, and shoreline. Using the Multi-Criteria Decision Making (MCDM) approach, two CVI (CVI-50 and CVI-90) have been developed based on average and extreme conditions of the factors. Each CVI is then compared to a data-driven CVI, which is formed based on Probabilistic Principal Component Analysis (PPCA). Both MCDM and PPCA …

Structural Health Monitoring Of Fatigue Cracks For Steel Bridges With Wireless Large-Area Strain Sensors, Sdiq Anwar Taher, Jian Li, Jong-Hyun Jeong, Simon Laflamme, Hongki Jo, Caroline Bennett, William N. Collins, Austin Downey

Faculty Publications

This paper presents a field implementation of the structural health monitoring (SHM) of fatigue cracks for steel bridge structures. Steel bridges experience fatigue cracks under repetitive traffic loading, which pose great threats to their structural integrity and can lead to catastrophic failures. Currently, accurate and reliable fatigue crack monitoring for the safety assessment of bridges is still a difficult task. On the other hand, wireless smart sensors have achieved great success in global SHM by enabling long-term modal identifications of civil structures. However, long-term field monitoring of localized damage such as fatigue cracks has been limited due to the lack …

Circadiomics: Circadian Omic Web Portal, Muntaha Samad, Forest Agostinelli, Tomoki Sato, Kohei Shimaji, Pierre Baldi

Faculty Publications

Circadian rhythms are a foundational aspect of biology. These rhythms are found at the molecular level in every cell of every living organism and they play a fundamental role in homeostasis and a variety of physiological processes. As a result, biomedical research of circadian rhythms continues to expand at a rapid pace. To support this research, CircadiOmics (http://circadiomics.igb.uci.edu/) is the largest annotated repository and analytic web server for high-throughput omic (e.g. transcriptomic, metabolomic, proteomic) circadian time series experimental data. CircadiOmics contains over 290 experiments and over 100 million individual measurements, across >20 unique tissues/organs, and 11 different species. Users are …

Efficiently Searching Extreme Mechanical Properties Via Boundless Objective-Free Exploration And Minimal First-Principles Calculations, Joshua Ojih, Mohammed Al-Fahdi, Alejandro David Rodriguez, Kamal Choudhary, Ming Hu

Faculty Publications

Despite the machine learning (ML) methods have been largely used recently, the predicted materials properties usually cannot exceed the range of original training data. We deployed a boundless objective-free exploration approach to combine traditional ML and density functional theory (DFT) in searching extreme material properties. This combination not only improves the efficiency for screening large-scale materials with minimal DFT inquiry, but also yields properties beyond original training range. We use Stein novelty to recommend outliers and then verify using DFT. Validated data are then added into the training dataset for next round iteration. We test the loop of training-recommendation-validation in …

Neurochemical, Molecular, And Behavioral Effects Of Intranasal Insulin, Jennifer Marie Erichsen

Theses and Dissertations

Age-related cognitive decline (ARCD) is one of the most dreaded aspects of growing old and a public health concern. Unfortunately, there is currently no treatment that effectively ameliorates ARCD. In recent decades, intranasal insulin (INI) has demonstrated promising memory enhancements for rodents and individuals with Alzheimer’s disease, both in basic science research laboratories and multi-center clinical trials. Other studies have shown INI improves memory and cognition in healthy rodents and humans, indicating that INI may hold promise as a successful ARCD therapeutic. However, there is a glaring lack of evidence regarding how INI produces these effects. The goal of these …

Role Of Ahr In The Epigenetic Regulation Of Immune Cells In Lungs During Acute Respiratory Distress Syndrome, Bryan Latrell Holloman

Theses and Dissertations

Acute lung injury and acute respiratory distress syndrome (ALI/ARDS) arises from pulmonary inflammatory diseases that stem from direct and indirect activation of immune cells. Severe complications from ALI/ARDS arise when inflammatory mediators initiate an overzealous immune response leading to the destruction of lung epithelial cell barriers, which causes systematic oxygen deprivation. Infiltrating innate immune cells drive the immune response during the acute phase of a lung injury. However, I3C, a naturally occurring AhR ligand found in cruciferous vegetables such as broccoli, collard greens, and kale, plays a crucial role in suppressing inflammatory mediators through its immunomodulatory functionality, which regulates both …