Engineering Commons^™

Experimental And Numerical Analysis Of Fluidization Of Pelletized Activated Carbon Columns: Effects Of Glass Bead Retention Layer, Amin Nemati Tamar

Theses and Dissertations

In pressure swing adsorption (PSA) processes there is always a desire to process as much gas as possible in the smallest beds possible. This necessarily leads to velocities that, especially during pressure-changing steps, may exceed the adsorbent particle fluidization velocity within the bed. To prevent the possibility of bed expansion and fluidization bed retention systems are usually employed at the top of the bed in the form of dense beads, bags of the same or springs, otherwise the mechanical integrity of the adsorbent might become compromised. PSA process simulation, based on Archimedes’ force balance involving weight, buoyancy and the drag …

Multiscale Modeling Techniques For Electrodynamic Systems, Hunter Teel

Theses and Dissertations

In order to rapidly develop better electromagnetic and electrochemical systems, approaching these complicated topics from multiple perspectives is necessary. The use of mathematical models and numerical simulation offers the ability to evaluate and predict the performance of these systems based off the principles of electrodynamics in an ideal setting. Through the use of Computational Fluid Dynamics (CFD), representative geometries of scales can be created and evaluated to provide insight into the behavior of these systems. Simulation offers a modifiable environment that can approach the relevant physics from both the microscale and macroscale. Conclusions, results, boundary conditions, or other information from …

A Novel Geometric Modeling Kernel For Automated Fiber Placement, Rowen Burney

Theses and Dissertations

Automated Fiber Placement (AFP) is a key manufacturing technique for producing high-performance composite structures, yet existing geometric modeling frameworks lack the specialized functionality required for efficient toolpath planning and laminate optimization. This work presents Knots, a Geometric Modeling Kernel (GMK) developed specifically to address these limitations by providing a unified geometric foundation tailored for AFP. Unlike general-purpose Computer Aided Design (CAD) libraries, Knots is optimized for AFP, offering specialized geometric operations that streamline the transition from design to manufacturing. It directly manages the loading and computation of geometric data, ensuring structured laminate generation while supporting automation and scripting for improved …

New Method Of Impact Localization On Plate-Like Structures Using Deep Learning And Wavelet Transform, Asaad Migot, Ahmed Saaudi, Victor Giurgiutiu

Faculty Publications

This paper presents a new methodology for localizing impact events on plate-like structures using a proposed two-dimensional convolutional neural network (CNN) and received impact signals. A network of four piezoelectric wafer active sensors (PWAS) was installed on the tested plate to acquire impact signals. These signals consisted of reflection waves that provided valuable information about impact events. In this methodology, each of the received signals was divided into several equal segments. Then, a wavelet transform (WT)-based time-frequency analysis was used for processing each segment signal. The generated WT diagrams of these segments’ signals were cropped and resized using MATLAB code …

Neurosymbolic Knowledge-Grounded Planning And Reasoning In Ai Systems, Amit Sheth, Vedant Khandelwal, Kaushik Roy, Vishal Pallagani, Megha Chakraborty

Faculty Publications

To build AI systems capable of decision-support assistance, such as AI-assisted healthcare, it is essential to develop user-centric decision-making processes that are robust, interpretable, and capable of effectively processing and acting on natural language interactions. Instruction-based prompting of large language models has demonstrated considerable success in supporting humans with information assistance tasks, including creative writing and content generation. However, recent studies reveal that language models exhibit limitations in performing complex reasoning and planning tasks, such as constructing compositional or hierarchical plans involving multiple reasoning steps. To address these challenges, we propose a neurosymbolic framework that integrates large language models with …

Msbzip55 Regulates Salinity Tolerance By Modulating Melatonin Biosynthesis In Alfalfa, Tingting Wang, Jiaqi Yang, Jiamin Cao, Qi Zhang, Huayue Liu, Peng Li, Yizhi Huang, Wenwu Qian, Xiaojing Bi, Hui Wang, Yunwei Zhang

Faculty Publications

No abstract provided.

Real-Time Defect Detection And Classification In Robotic Assembly Lines: A Machine Learning Framework, Fadi El Kalach, Mojtaba Farahani, Thorsten Wuest, Ramy Harik

Faculty Publications

Manufacturing systems have witnessed a significant transformation with the introduction of Industry 4.0, introducing new capabilities with the emergence of new technologies. One such instance is the proliferation of sensors enabling the generation and acquisition of vast amounts of data, leading to advancements in Artificial Intelligence (AI) for manufacturing. One field profiting from this is that of Time Series Analytics (TSC) which includes forecasting and classification. TSC can be crucial for fault detection and diagnosis in manufacturing systems. However, there are still challenges in utilizing manufacturing datasets to train and deploy classification algorithms for real time classification. As such this …

Time-Series Forecasting In Smart Manufacturing Systems: An Experimental Evaluation Of The State-Of-The-Art Algorithms, Mojaba A. Farahani, Fadi El Kalach, Austin Harper, M.R. Mccormick, Ramy Harik, Thorsten Wuest

Faculty Publications

Time-Series Forecasting (TSF) is a growing research area across various domains including manufacturing. Manufacturing can benefit from Artificial Intelligence (AI) and Machine Learning (ML) innovations for TSF tasks. Although numerous TSF algorithms have been developed and proposed over the past decades, the critical validation and experimental evaluation of the algorithms hold substantial value for researchers and practitioners and are missing to date. This study aims to fill this research gap by providing a rigorous experimental evaluation of the state-of-the-art TSF algorithms on thirteen manufacturing-related datasets with a focus on their applicability in smart manufacturing environments. Each algorithm was selected based …

Friction-Based Recycling: An Evaluation Of Friction Extrusion For Fabricating Ti-6al-4 V Wire Fabricated From Machining Chip Feedstock, Devesh Kumar Chouhan, Mageshwari Komarasamy, Scott B. Taysom, Nicole R. Overman, Nathan L. Canfield, Timothy J. Roosendaal, Anthony P. Reynolds, Scott A. Whalen

Faculty Publications

Titanium and its alloys are used in aviation and automobile industries due to their remarkable strength to weight ratio, but machining loss commonly is high with ~ 80 wt% of the material being converted to scrap. Recycling post-consumer Ti scrap directly into solid bulk products is a potential solution for repurposing valuable material. Further, eliminating fresh Ti sponge during recycling might lead to lower energy and greenhouse gas emissions. In this study, a solid-phase process known as friction extrusion was utilized to recycle Ti-6Al-4 V machining chips into solid wires which could be used as feedstock in additive manufacturing. The …

Understanding Ionic Transport In Perovskite Lithium-Ion Conductor Li3/8Sr7/16Ta3/4Hf1/4O3: A Neutron Diffraction And Molecular Dynamics Simulation Study †, Danyi Sun, Nan Wu, Yeting Wen, Shichen Sun, Yufang He, Ke Huang, Cheng Li, Bin Ouyang, Ralph E. White, Kevin Huang

Faculty Publications

Solid-state Li-ion electrolytes (SSEs) are essential for the development of next-generation solid-state Li-metal batteries and new Li-extraction electrochemical cells. Among these, the perovskite-type SSE Li3/8Sr7/16Ta3/4Hf1/4O3 (LSTH) has garnered attention for Li-extraction applications, owing to its outstanding chemical and thermal stability and high ionic conductivity. However, its precise crystal structure and Li-ion transport mechanisms remain insufficiently understood. This study addresses these gaps by employing neutron diffraction to resolve LSTH's crystallography and machine learning force field (MLFF) based MD simulations to elucidate ionic transport mechanisms. A single-phase LSTH, synthesized via the sol–gel method, exhibits a room-temperature bulk conductivity of 0.418 mS cm−1 …

Processing Parameter-Performance Nexus In 3d Printing Of Nanostructured Chiral Photonics, Kyle George, Nader Taheri-Qazvini, Peter D. Olmsted, Monirosadat Sadati

Faculty Publications

Precisely crafted hierarchical architectures found in naturally derived biomaterials underpin the exceptional performance and functionality showcased by the host organism. In particular, layered helical assemblies composed of cellulose, chitin, or collagen serve as the foundation for some of the most mechanically robust and visually striking natural materials. By utilizing structured materials in additive manufacturing techniques such as extrusion-based 3D printing, the intrinsic deformation process can be used to implement bottom-up design of printed constructs, offering the potential to create intricate macroscale geometries with embedded nanoscale functionality. In this study, comprehensive rheological and rheo-optical characterization of structurally colored, photocurable liquid crystalline …

Lab: Developing Explainable Multimodal Ai Models With Hands-On Lab On The Life-Cycle Of Rare Event Prediction In Manufacturing, Chathurangi Shyalika, Ruwan Wickramarachchi, Revathy Venkataramanan, Dhaval Patel, Amit Sheth

Faculty Publications

In the age of Industry 4.0 and smart automation, unplanned downtime is costing industries over $50 billion annually. Even with preventive maintenance, industries like automotive lose more than $2 million per hour due to downtime caused by unexpected or "rare'' events. The extreme rarity of these events makes their detection and prediction a significant challenge for AI practitioners. Factors such as the lack of high-quality data, methodological gaps in the literature, and limited practical experience with multimodal data exacerbate the difficulty of rare event detection and prediction. This lab will provide hands-on experience to learn how to address these challenges …

A Digital Twin Based Forecasting Framework For Power Flow Management In Dc Microgrids, Kerry Sado, Jarrett Peskar, Austin Downey, Jamil A. Khan, Kristen Booth

Faculty Publications

The ability to forecast system conditions is integral to the definition and functionality of digital twins. While forecasting methods have been explored for use in digital twin systems, the integration of feedback mechanisms for real-time forecasting and in-situ decision-making in DC microgrids has not been extensively investigated. This research develops a modular forecasting framework tailored for digital twins in DC microgrids to enable real-time monitoring, online forecasting, and decision-making. DC microgrids, characterized by dynamic load variations, benefit from advanced predictive capabilities to maintain stability and operational efficiency. The proposed digital twin-based forecasting framework addresses these challenges by providing real-time predictive …

Probing Ion-Blocking Electrode Rigs For Ionic Conductivity In Hybrid Solid Polymer Electrolytes, Kyra Glassey, Gabriela Roman-Martinez, Liliana Delatte, Thomas Burns, Monirosadat Sadati, Paul T. Coman, Ralph E. White

Faculty Publications

Solid electrolytes are critical for structural batteries, combining energy storage with structural strength for applications like electric vehicles and aerospace. However, achieving high ionic conductivity remains challenging, compounded by a lack of standardized testing methodologies. This study examines the impact of experimental setups and data interpretation methods on the measured ionic conductivities of solid polymer electrolytes (SPEs). SPEs were prepared using a polymer-induced phase separation process, resulting in a bi-continuous microstructure for improved ionic transport. Eight experimental rigs were evaluated, including two- and four-electrode setups with materials like stainless steel, copper, and aluminum. Ionic conductivity was assessed using electrochemical impedance …

Si-Doped Ain Using Pulsed Metalorganic Chemical Vapor Deposition And Doping, Tariq Jamil, Abdullah Al Mamun Mazumder, Mohammod Ali, Jingyu Lin, Hongxing Jiang, Grigory Simin, M. Asif Khan

Faculty Publications

In this paper we describe a pulsed metalorganic chemical vapor deposition (MOCVD) Si-doping approach for AlN epilayers over bulk AlN. The Al-rich growth/doping conditions in the pulsed MOCVD process resulted in n-AlN layers with transmission line model currents that were an order higher than for structures on layers that were grown/doped at identical temperatures using the conventional MOCVD process. Our work demonstrated that like the other reported approaches such as UV exposure during growth, the pulsed MOCVD process is also very effective in reducing point defects by the defect quasi-Fermi level-chemical potential control.

Pilot Study: Initial Investigation Suggests Differences In Emt-Associated Gene Expression In Breast Tumor Regions, Kylie L. King, Hamed Abdollahi, Zoe Dinkel, Alannah Akins, Homayoun Valafar, Heather Dunn

Faculty Publications

Triple negative breast cancer (TNBC) is the most aggressive subtype and disproportionately affects African American women. The development of breast cancer is highly associated with interactions between tumor cells and the extracellular matrix (ECM), and recent research suggests that cellular components of the ECM vary between racial groups. This pilot study aimed to evaluate gene expression in TNBC samples from patients who identified as African American and Caucasian using traditional statistical methods and emerging Machine Learning (ML) approaches. ML enables the analysis of complex datasets and the extraction of useful information from small datasets. We selected four regions of interest …

Co2 Electrolysis Using Metal-Supported Solid Oxide Cells With Infiltrated Pr0.5Sr0.4Mn0.2Fe0.8O3−Δ Catalyst, Boxun Hu, Ka-Young Park, Asia Sarycheva, Robert Kostecki, Fanglin Chen, Michael C. Tucker

Faculty Publications

Electrochemical conversion of CO₂ to CO is demonstrated with symmetric-structured metal supported solid oxide cells (MS-SOC). Perovskite Pr_0.5Sr_0.4Mn_0.2Fe0.8O_3−δ (PSMF) and Pr₆O₁₁ catalysts were infiltrated into the MS-SOC cathode and anode, using 3 cycles with firing at 850 °C and 8 cycles with firing at 800 °C, respectively. Upon reduction during operation, the perovskite PSMF was transformed to Ruddlesden–Popper structure with a highly efficient electrocatalytic activity. The impact of operating temperature (600–800 °C) and overpotential (0–1.8 V) on the CO₂ conversion was investigated. The highest CO₂ conversion …

Chemical Damping Of The Motion Of A Piston: Irreversibilities Arising From Nonequilibrium Conditions On The Chemical Potential, David S. Corti, Mark J. Uline

Faculty Publications

We revisit the thermodynamic analysis of an isothermal ideal gas mixture enclosed within a cylinder and separated from the surrounding atmosphere by a movable and frictionless piston. When equilibrium conditions based on the chemical potentials of one or more species in the mixture are not satisfied at all times, which occurs for example for a chemical reaction with finite and non-zero reaction rates in the forward and reverse directions and for mass transfer of one species across a permeable membrane occurring at a finite and non-zero rate, an irreversibility is necessarily introduced into the system with a resulting increase in …

Associations Between Social Determinants Of Health And Mental Health Disorders Among U.S. Population: A Cross-Sectional Study, S. Tanarsuwongkul

Faculty Publications

Aims

The impact of social determinants of health (SDOH) on mental health is increasingly realized. A comprehensive study examining the associations of SDOH with mental health disorders has yet to be accomplished. This study evaluated the associations between five domains of SDOH and the SDOH summary score and mental health disorders in the United States.

Methods

We analyzed data from a diverse group of participants enrolled in the All of Us research programme, a research programme to gather data from one million people living in the United States, in a cross-sectional design. The primary exposure was SDOH based on Healthy …

Online Energy Consumption Forecast For Battery Electric Buses Using A Learning-Free Algebraic Method, Zejiang Wang, Guanhao Xu, Ruixiao Sun, Anye Zhou, Adian Cook, Yuche Chen

Faculty Publications

Accurately predicting the energy consumption plays a vital role in battery electric buses (BEBs) route planning and deployment. Based on the algebraic derivative estimation, we present a novel method to forecast the energy consumption in real time. In contrast to the mainstream machine-learning-based methods, the proposed method does not require access to the historical energy consumption data. It eliminates the time-consuming and computationally expensive offline training. Consequently, its prediction performance is not constrained by the quantity and quality of the training data. Moreover, the method can swiftly adapt to new situations not included in the previous driving cycles, which makes …

2 Kv Al0.64Ga0.36N-Channel High Electron Mobility Transistors With Passivation And Field Plates, Md Tahmidul Alam, Jiahao Chen, Kenneth Stephenson, Md Abdullah-Al Mamun, Abdullah Al Mamun Mazumder, Shubhra S. Pasayat, Asif Khan, Chirag Gupta

Faculty Publications

High voltage (∼2 kV) Al_0.64Ga_0.36N-channel high electron mobility transistors were fabricated with an on-resistance of ∼75 Ω. mm (∼21 mΩ. cm²). Two field plates of variable dimensions were utilized to optimize the breakdown voltage. The breakdown voltage reached >3 kV (tool limit) before passivation however it reduced to ∼2 kV after Si₃N₄ surface passivation and field plate deposition. The breakdown voltage and on-resistance demonstrated a strong linear correlation in a scattered plot of ∼50 measured transistors. The fabricated transistors were electrically characterized and benchmarked against the state-of-the-art high-voltage (> 1 kV) …

Sno2 Modified Csh2Po4 (Cdp) Protonic Electrolyte For An Electrochemical Hydrogen Pump, Minal Gupta, Kangkang Zhang, Kevin Huang

Faculty Publications

CsH₂PO₄ (CDP) is a well-known super-protonic conductor. However, it must operate under high humidity conditions to prevent dehydration and fast conductivity decay. Herein, we report that adding hydrophilic SnO₂ into CDP can suppress the rate of dehydration of CDP, thus stabilizing protonic conductivity over a broader range of water partial pressures (pH₂O). A total of seven compositions of (1 − x)CDP/(x)SnO₂ were prepared, where 5 ≤ x ≤ 40 (wt%), and examined for their phasal, microstructural, and vibrational properties using X-ray diffraction, field emission scanning electron microscopy, and …

Sno2 Modified Csh2Po4 (Cdp) Protonic Electrolyte For An Electrochemical Hydrogen Pump, Minal Gupta, Kangkang Zhang, Kevin Huang

Faculty Publications

CsH₂PO₄ (CDP) is a well-known super-protonic conductor. However, it must operate under high humidity conditions to prevent dehydration and fast conductivity decay. Herein, we report that adding hydrophilic SnO2 into CDP can suppress the rate of dehydration of CDP, thus stabilizing protonic conductivity over a broader range of water partial pressures (pH2O). A total of seven compositions of (1 - x)CDP/(x)SnO₂ were prepared, where 5 ≤ x ≤ 40 (wt%), and examined for their phasal, microstructural, and vibrational properties using X-ray diffraction, field emission scanning electron microscopy, and Raman spectroscopy. The signature of H₂O …

A Survey On Food Ingredient Substitutions, Hyunwook Kim, Revathy Venkataramanan, Amit P. Sheth

Publications

Diet plays a crucial role in managing chronic conditions and overall well-being. As people become more selective about their food choices, finding recipes that meet dietary needs is important. Ingredient substitution is key to adapting recipes for dietary restrictions, allergies, and availability constraints. However, identifying suitable substitutions is challenging as it requires analyzing the flavor, functionality, and health suitability of ingredients. With the advancement of AI, researchers have explored computational approaches to address ingredient substitution. This survey paper provides a comprehensive overview of the research in this area, focusing on five key aspects: (i) datasets and data sources used to …

Exploring The Potential Of Large Language Models For Assisting With Mental Health Diagnostic Assessments: The Depression And Anxiety Case, Kaushik Roy, Harshul Surana, Darssan Eswaramoorthi, Yuxin Zi, Vedant Palit, Ritvik Garimella, Amit Sheth

Publications

Large language models (LLMs) are increasingly attracting the attention of healthcare professionals for their potential to assist in diagnostic assessments, which could alleviate the strain on the healthcare system caused by a high patient load and a shortage of providers. For LLMs to be effective in supporting diagnostic assessments, it is essential that they closely replicate the standard diagnostic procedures used by clinicians. In this paper, we specifically examine the diagnostic assessment processes described in the Patient Health Questionnaire-9 (PHQ-9) for major depressive disorder (MDD) and the Generalized Anxiety Disorder-7 (GAD-7) questionnaire for generalized anxiety disorder (GAD). We investigate various …

C 3 An: Custom, Compact And Composite Ai Systems - A Neurosymbolic Approach: 4Th-Generation Evolution Of Intelligent Systems, Amit P. Sheth, Kaushik Roy, Revathy Venkataramanan, Venkatesan Nadimuthu

Publications

Artificial Intelligence (AI) systems continue to evolve rapidly. From the architecture perspective, it is evolving from large, monolithic models trained on massive internet data to complex, multi-component “compound” systems and “agentic” frameworks capable of semi-autonomous decision-making. These systems show immense promise yet face numerous challenges in reliability, consistency, transparency, and alignment with user goals. In this article, we propose Custom, Compact and Composite AI with Neurosymbolic (C3AN) approach, a framework that paves way to 4th-generation of AI that integrates data, knowledge, and human expertise to build robust, intelligent and trustworthy AI systems defined by 14 foundation elements.

Custom emphasizes …

Digital Twins For Flexible Manufacturing, Drew Brandon Sander

Theses and Dissertations

With the establishment of Digital Twins as an enabling technology for Industry 4.0, it is time to transition from the previous foundations to works that implement and highlight the capabilities Digital Twins present. The Industry 4.0 paradigm has recentered the focus on customizability, leading to reemerging goals of flexibility within manufacturing. Empowered through new cyber-physical systems, new enabling technologies are driving the revolution. To identify how Digital Twins as an enabling technology contribute to flexibility within manufacturing, a comprehensive investigation is conducted along with a detailed case study. A systematic literature review is conducted to analyze quality Digital Twin implementations …

Novel Redox Oxygen Active Materials For Solid Oxide Cells And Thermochemical Hydrogen Production, Jiaxin Lu

Theses and Dissertations

This dissertation investigates novel materials designed to significantly enhance the performance and durability of oxygen electrodes for solid oxide cells and redox active materials for thermochemical hydrogen production. The first part of this work evaluates Ta-doped BaCoO₃₋δ (BCT) and its structural analog SrCoO₃₋δ (SCT), focusing on their catalytic performance in oxygen reduction and evolution reactions. Among BCT compositions (x = 0.1, 0.3, 0.5), the BCT10 (x = 0.1) composition exhibited the highest oxygen vacancy concentration, electronic conductivity, and lowest polarization resistance. However, SCT10 surpassed BCT10 in these critical performance parameters and also demonstrated greater stability over extended operational periods. Notably, …

Safe Data-Enabled Control Of Human-In-The-Loop Robotic Manipulator Systems, Ritirupa Dey, Avimanyu Sahoo, Vignesh Narayanan

Publications

Safe control of human-in-the-loop (HIL) robotic manipulators is critical for applications such as assistive robotics, teleoperation in hazardous environments, and collaborative manufacturing. However, this remains challenging due to the lack of a unified framework that simultaneously addresses safety constraints, external disturbances, unmodeled dynamics, and dynamic role switching in the HIL setting. In this paper, we propose a novel NN-driven HIL control framework in which human–robot dyadic interaction occurs through the haptic channel. Using Lyapunov stability analysis, we theoretically show that the proposed NN-based controller ensures accurate joint trajectory tracking, compensates for system uncertainties, and adapts to human inputs modeled as …

Evaluation Of Impact Energy In Composites Using Acoustic Emission Sensing Technique, Li Ai, Tanner Mesaric, Sydney Flowers, Sydney Houck, Joshua Widawsky, Paul Ziehl

Faculty Publications

A major challenge faced by composite materials is impact, which can result in unexpected damage and degradation. Impact events can cause significant structural damage that may not be immediately visible, leading to a reduction in the material’s mechanical properties and overall performance. This paper presents an impact assessment method using acoustic emission (AE) sensing technology. The primary goal of this approach is to determine the extent of impact damage on composite components by analyzing AE signals produced under operating stress conditions. An advanced algorithm is proposed to predict the probability that the damage falls into various damage categories, providing a …