Engineering Commons^™

Multiagent Copilot In Industrial Ai Applications, Chathurangi Shyalika, Renjith Prasad, Utkarshani Jaimini, Cory Henson, Fadi El Kalach, Amit Sheth

Publications

In the era of smart automation and digital transformation, achieving efficiency, precision, and adaptability is essential for industries to remain competitive. Sectors, including manufacturing, supply chain and logistics, healthcare, finance, and retail, face significant challenges in deploying Artificial Intelligence (AI) solutions tailored to their unique needs, particularly in critical, resource-constrained applications. According to Gartner’s 2024 Hype Cycle for Artificial Intelligence, composite AI, which integrates techniques like machine learning, knowledge graphs, and rule-based systems, is becoming foundational for industries, enhancing predictions, decisions, and scalability across complex environments.

The complexity of real-world systems requires Industrial AI solutions to be customizable to business …

Review Of Tethered Unmanned Aerial Vehicles: Building Versatile And Robust Tethered Multirotor Uav System, Dario Handrick, Mattie Eckenrode, Junsoo Lee

Faculty Publications

This paper presents a comprehensive review of tethered unmanned aerial vehicles (UAVs), focusing on their challenges and potential applications across various domains. We analyze the dynamic characteristics of tethered UAV systems and address the unique challenges they present, including complex tether dynamics, impulsive forces, and entanglement risks. Additionally, we explore application-specific challenges in areas such as payload transportation and ground-connected systems. The review also examines existing tethered UAV testbed designs, highlighting their strengths and limitations in both simulation and experimental settings. We discuss advancements in multi-UAV cooperation, ground–air collaboration through tethers, and the integration of retractable tether systems. Moreover, we …

How Extreme Rainfall And Failing Dams Unleashed The Derna Flood Disaster, Ayman Mokhtar Nemnem, Ahad Hasan Tanim, Audrika Nahian, Sadik Khan, Erfan Goharian, Jasim Imran

Faculty Publications

On September 11, 2023, Storm Daniel unleashed unprecedented rainfall over the Wadi Derna watershed, triggering one of the most devastating floods in modern history, striking Derna, a coastal city in Libya. This study reconstructs the disaster using an integrated modeling approach that combines satellite imagery, hydrologic, hydraulic, and geotechnical simulations, machine learning, eyewitness accounts, and digital elevation data to assess the impact of cascading dam failures. Our findings reveal that the region’s dams, even if structurally sound, would have provided minimal protection against the extreme runoff. However, their failure unleashed a destructive surge wave, amplifying the disaster’s magnitude and devastation. …

How Extreme Rainfall And Failing Dams Unleashed The Derna Flood Disaster, Ayman Mokhtar Nemnem, Ahad Hasan Tanim, Audrika Nahian, Sadik Khan, Erfan Goharian, Jasim Imran

Faculty Publications

On September 11, 2023, Storm Daniel unleashed unprecedented rainfall over the Wadi Derna watershed, triggering one of the most devastating floods in modern history, striking Derna, a coastal city in Libya. This study reconstructs the disaster using an integrated modeling approach that combines satellite imagery, hydrologic, hydraulic, and geotechnical simulations, machine learning, eyewitness accounts, and digital elevation data to assess the impact of cascading dam failures. Our findings reveal that the region’s dams, even if structurally sound, would have provided minimal protection against the extreme runoff. However, their failure unleashed a destructive surge wave, amplifying the disaster’s magnitude and devastation. …

Graphics Processing Unit-Enabled Path Planning Based On Global Evolutionary Dynamic Programming And Local Genetic Algorithm Optimization, Junlin Ou, Ge Song, Yi Wang

Faculty Publications

This paper presents a novel path planning method for real-time robotic path planning in a dynamic environment involving moving obstacles. It combines on a holistic platform a global approach to rapidly generate initial paths of prominent diversity and a heuristic approach to enable local path refinement for enhanced computational efficiency, exploration, and robustness. The global approach innovates a formulation that treats a path planning problem with a visibility graph as a Markov decision process and decomposes the process into many subproblems. A new evolutionary dynamic programming approach (EDP) is proposed to solve these subproblems in an iterative manner using …

Towards Human Modeling For Human-Robot Collaboration And Digital Twins In Industrial Environments: Research Status, Prospects, And Challenges, Guoyi Xia, Zied Gharairi, Thorsten Wuest, Karl Hribernik, Aaron Heuermann, Furui Liu, Hui Liu, Klaus-Dieter Thoben

Faculty Publications

Human-Robot Collaboration (HRC) and Digital Twins (DT) have significantly advanced industrial development and digital transformation. Human representations and models are essential in Industry 5.0, where human-centric is one of the key features. Despite the growing interest in human models for HRC and DT, a comprehensive overview of these models and enabling technologies currently needs to be provided. This paper aims to present the research status, prospects, applications, and challenges of human modeling for HRC and DT in industrial environments. This paper adopts a Systematic Literature Review (SLR) approach. Moreover, a framework is proposed to systematize human modeling aspects, the technologies …

Mechanism Of Property Enhancement Of Cu−Ti Alloys Via Microalloying With Cr And Mg Elements, Huan Wei, Hong Wei, Hua-Yun Du, Qian Wang, Cai-Zhi Zhou, Ying-Hui Weu, Li-Feng Hou

Faculty Publications

The effect of adding Cr and Mg on the microstructure and properties of Cu−Ti alloys was examined. Cu−Ti−Cr−Mg alloys were fabricated using vacuum induction melting. The microstructure and phase composition of Cu−Ti−Cr−Mg alloys in different aging states were characterized. Additionally, the hardness and electrical conductivity of the materials were investigated. Results show that the precipitation pattern in Cu−Ti−Cr−Mg alloys resembled that of binary Cu−Ti alloys, with Cr and Ti forming the intermetallic compound of Cr2Ti during casting. The introduction of Cr and Mg increased the hardness of the alloy. Increasing the Mg content in the Cu−Ti−Cr−Mg alloy led to grain …

Scalable Cyber-Physical Testbed For Cybersecurity Evaluation Of Synchrophasors In Power Systems, Shuvangkar Chandra Das, Tuyen Vu, Hebert L. Ginn Iii

Faculty Publications

This paper presents a synchrophasor-based real-time cyber-physical power system testbed with a novel security evaluation tool, pySynphasor, that can emulate different real attack scenarios on the phasor measurement unit (PMU). The testbed focuses on real-time cyber-security emulation using different components, including a real-time digital simulator, virtual machines (VM), a communication network emulator, and a packet manipulation tool. The script-based VM deployment and software-defined network emulation facilitate a highly scalable cyber-physical testbed, which enables emulations of a real power system under different attack scenarios such as address resolution protocol (ARP) poisoning attack, man-in-the-middle (MITM) attack, false data injection attack (FDIA), and …

Effect Of Precursors On Trimetallic Ruthenium-Based Catalysts Supported On Γ‑Al2O3 Pellets For Low-Temperature Ammonia Decomposition, Christopher J. Koch, Jennifer Naglic, Logan Kearney, Daniel Clairmonte, Binod Rai, Jochen A. Lauterbach, Lucas M. Angelette, Tyler Guin

Faculty Publications

Ammonia is a promising candidate as a liquid hydrogen energy storage medium, but it requires catalytic decomposition (ammonia cracking) to regenerate hydrogen. Recently developed trimetallic ruthenium−potassium-promoter (RuKM) ammonia decomposition catalysts have exceptionally low ammonia decomposition temperatures, able to perform the decomposition as low as 250 °C, which is significantly lower than other known catalysts that require temperatures above 500 °C. However, the effects of the RuKM precursor on the catalytic activity have not been investigated. We report the observed differences of 3% ruthenium/12% potassium/1% yttrium (RuKY) catalysts on γ-alumina synthesized from chloride-, nitrate-, and acetate-based precursors. Catalysts synthesized from chloride-based …

Two-Layer Formulation For Long-Runout Turbidity Currents: Theory And Bypass Flow Case, Hongbo Ma, Gary Parker, Matthieu Cartigny, Enrica Viparelli, S. Balachandar, Xudong Fu, Rossella Luchi

Faculty Publications

Turbidity currents, which are stratified, sediment-laden bottom flows in the ocean or lakes, can run out for hundreds or thousands of kilometres in submarine channels without losing their stratified structure. Here, we derive a layer-averaged, two-layer model for turbidity currents, specifically designed to capture long-runout. A number of previous models have captured runout of only tens of kilometres, beyond which thickening of the flows becomes excessive, and the models without a lateral overspill mechanism fail. In our framework, a lower layer containing nearly all the sediment is a faster, gravity-driven flow that propels an upper layer, where sediment concentration is …

Average Biomechanical Responses Of The Human Brain Grouped By Age And Sex, Ahmed A. Alshareef, Aaron Carass, Yaun-Chiao Lu, Joy Mojumder, Alexa M. Diano, Olivia M. Bailey, Ruth J. Okamoto, Dzung L. Pham, Jerry L. Prince, Philip V. Bayly, Curtis L. Johnson

Faculty Publications

Traumatic brain injuries (TBIs) occur from rapid head motion that results in brain deformation. Computational models are typically used to estimate brain deformation to predict risk of injury and evaluate the effectiveness of safety countermeasures. The accuracy of these models relies on validation to experimental brain deformation data. In this study, we create the first group-average biomechanical responses of the brain, including structure, material properties, and deformation response, by age and sex from 157 subjects. Subjects were sorted intro three age groups—young, mid-age, and older—and by sex to create group-average neuroanatomy, material properties, and brain deformation response to non-injurious loading …

Examining Physiological Responses To Misophonic Triggers, Christian O'Reilly, Xuan Yang, Sewon Oh, Doug Wedell, Svetlana Shinkareva

Faculty Publications

We collected and analyzed an array of biosignals (face electromyogram, skin electrodermal activity, peripheral temperature, and electrocardiogram) in 60 participants with and without misophonia, a condition characterized by decreased tolerance to innocuous sounds. Our goal was to objectively characterize the physiological response to misophonia triggering sounds. We found that misophonic responses can be objectively identified in some cases through atypical physiological reactions to triggering stimuli, though not all participants exhibited this response. Our analyses suggest a large interindividual variability in response to misophonic triggers and highlights the need for methodological adjustments in future experiments to increase the detectability of misophonic …

Removing Eog Artifacts From Eeg Recordings Using Deep Learning, Christian O'Reilly, Scott Huberty

Faculty Publications

The electroencephalogram (EEG) directly measures the electrical activity generated by the brain. Unfortunately, it is often contaminated by various artifacts, notably those caused by eye movements and blinks (EOG artifacts). Such artifacts are usually removed using an independent component analysis (ICA) or other blind source separation techniques. However, it is difficult to assess whether subtracting EOG components estimated through ICA removes some neurogenic activity. It is crucial to address this question to avoid biasing EEG analyses. Toward that objective, we developed a deep learning model for EOG artifact removal that exploits information about eye movements available through eye-tracking (ET). Using …

A Reliable And Efficient Detection Pipeline For Rodent Ultrasonic Vocalizations, Sabah Shahnoor Anis, Devin Mark Kellis, Kris Ford Kaigler, Marlene A. Wilson, Christian O'Reilly

Faculty Publications

Analyzing ultrasonic vocalizations (USVs) is crucial for understanding rodents' affective states and social behaviors, but the manual analysis is time-consuming and prone to errors. Automated USV detection systems have been developed to address these challenges. Yet, these systems often rely on machine learning and fail to generalize effectively to new datasets. To tackle these shortcomings, we introduce ContourUSV, an efficient automated system for detecting USVs from audio recordings. Our pipeline includes spectrogram generation, cleaning, pre-processing, contour detection, post-processing, and evaluation against manual annotations. To ensure robustness and reliability, we compared ContourUSV with three state-of-the-art systems using an existing open-access USV …

Open Accessarticle Crystal Plasticity Modeling Of Dislocation Density Evolution In Cellular Dislocation Structures, Md Mahabubur Rohoman, Caizhi Zhou

Faculty Publications

The complex thermal cycles during the solidification process in metal additive manufacturing (AM) lead to the formation of high-density dislocation networks, organizing into submicron-scale cellular structures. These ultrafine structures are recognized as crucial for enhancing the mechanical properties of AM metals. In this study, we investigate the evolution of dislocation density within these cellular structures under plastic deformation and its impact on mechanical response using dislocation density-based crystal plasticity finite element (CPFE) modeling. The model incorporates the evolution of both statistically stored dislocation (SSD) and geometrically necessary dislocation (GND). Our simulations reveal that the yield and flow stresses of dislocation …

Endothelial Dysfunction Promotes Age-Related Reorganization Of Collagen Fibers And Alters Aortic Biomechanics In Mice, Liya Du, Jeffrey Rodgers, Nazli Gharraee, Olivia Gary, Tarek Shazly, John F. Eberth, Susan M. Lessner

Faculty Publications

Endothelial dysfunction, defined as a reduction in the bioavailability of nitric oxide (NO), is a risk factor for the occurrence and progression of various vascular diseases. This study investigates the effect of endothelial dysfunction on age-related changes in aortic extracellular matrix (ECM) microstructure and the relationship between microstructural adaptation and the mechanical response. Here, we used groups of NOS3 knockout (KO), NOS3 heterozygotes (Het), and wild-type (WT) B6 mice (controls) to study changes in hemodynamic parameters, collagen fiber organization, and both active and passive aortic mechanics using biaxial pressure myography over a time course from 1.5 to 12 mo. Our …

Endothelial Dysfunction Promotes Age-Related Reorganization Of Collagen Fibers And Alters Aortic Biomechanics In Mice, Liya Du, Jeffrey Rodgers, Nazli Gharraee, Olivia Gary, Tarek Shazly, John F. Eberth, Susan M. Lessner

Faculty Publications

Endothelial dysfunction, defined as a reduction in the bioavailability of nitric oxide (NO), is a risk factor for the occurrence and progression of various vascular diseases. This study investigates the effect of endothelial dysfunction on age-related changes in aortic extracellular matrix (ECM) microstructure and the relationship between microstructural adaptation and the mechanical response. Here, we used groups of NOS3 knockout (KO), NOS3 heterozygotes (Het), and wild-type (WT) B6 mice (controls) to study changes in hemodynamic parameters, collagen fiber organization, and both active and passive aortic mechanics using biaxial pressure myography over a time course from 1.5 to 12 mo. Our …

Shear-Thinning Hydrogel For Delayed Delivery Of A Small Molecule Metalloproteinase Inhibitor Attenuates Myocardial Infarction Remodeling, Joshua E. Mealy, William M. Torres, Lisa A. Freeburg, Shayne C. Barlow, Alison A. Whalen, Chima V. Maduka, Tarek Shazly, Jason A. Burdick, Francis G. Spinale

Faculty Publications

No abstract provided.

Assessing The Feasibility Of Transportable Nuclear Reactors: A Radiological Risk Case Study, Robert John Demuth

Theses and Dissertations

The feasibility of a truly transportable nuclear reactor presents significant radiological challenges that must be addressed to ensure compliance with regulatory limits and operational safety. This study evaluates the radiological constraints associated with the transportability of the Transportable Helium-cooled One-megawatt Reactor (THOR), focusing on neutron activation, shielding performance, and external radiation exposure. Using advanced radiation transport and activation modeling within the SCALE framework, a comprehensive analysis was conducted to quantify dose rates during operation, post-shutdown, and transport. A key aspect of this work involved assessing activation products in core materials, auxiliary components, and surrounding environments to determine their contribution to …

Automated Fiber Placement Laminate Level Optimization: A Physics-Inspired Method To Analyze Through-Thickness Defect Interactions, Nishan Patel

Theses and Dissertations

With the growing adoption of composite materials in industries such as aerospace, automotive, naval, wind energy, and even sectors like sports and consumer goods, there has been a strong push towards improving manufacturing reliability and productivity. One key method that has gained significant traction is Automated Fiber Placement (AFP), an additive manufacturing technique valued for its precision, efficiency, and adaptability in producing complex composite parts. As industries continue to shift from traditional metal-based structures to more advanced composite materials, the need for efficient and high- quality manufacturing solutions has become even more critical, prompting a focus on automation and optimization …

Explainable And Reduced-Feature Machine Learning Models For Shape And Drag Prediction Of A Freely Moving Drop In The Sub-Critical Weber Number Regime, Md Amanullah Kabir Tonmoy

Theses and Dissertations

Accurately predicting the shape and drag of a moving drop is crucial in many spray applications. However, due to the complex interaction between the drag force and drop shape deformation, accurate prediction of drop shape and drag from Computational Fluid Dynamics (CFD) simulation requires large computational resources and time. A novel data-driven approach using NARXNN (Non-linear Auto Regressive eXogenous input Neural Network) is proposed in this study, which recurrently predicts the drop shape and drag for a given Weber number (We) and Reynolds number (Re), in the sub-critical We regime where there is no drop breakup. The average error in …

Ultrasonic Spray Coating Of Carbon Fibers For Composite Cathodes In Structural Batteries, Thomas Burns, Liliana Delatte, Gabriela Roman-Martinez, Kyra Glassey, Paul Ziehl, Monirosadat Sadati, Ralph E. White, Paul T. Coman

Faculty Publications

Structural batteries, also known as “massless batteries”, integrate energy storage directly into load-bearing materials, offering a transformative alternative to traditional Li-ion batteries. Unlike conventional systems that serve only as energy storage devices, structural batteries replace passive structural components, reducing overall weight while providing mechanical reinforcement. However, achieving uniform and efficient coatings of active materials on carbon fibers remains a major challenge, limiting their scalability and electrochemical performance. This study investigates ultrasonic spray coating as a precise and scalable technique for fabricating composite cathodes in structural batteries. Using a computer-controlled ultrasonic nozzle, this method ensures uniform deposition with minimal material waste …

Understanding Water Washing Needs Of Hydrochar Generated From The Hydrothermal Carbonization Of Food Wastes To Establish Minimum Post-Processing Needs For Soil Amendment Application, Neve Renee Steger

Theses and Dissertations

Hydrothermal carbonization (HTC) is an advantageous and potentially environmentally beneficial approach for the conversion of wet biomass and waste streams into a value-added solid product referred to as hydrochar. Although hydrochar composition and properties generated from the carbonization of a variety of feedstocks have been well studied, there is a need for understanding whether hydrochar requires further refinement and/or treatment prior to being used in environmental applications, particularly when using the hydrochar as a soil amendment. Understanding the need for and extent of hydrochar post-treatment is critical in identifying environmentally beneficial and economically attractive strategies for hydrochar use. In this …

Frequency-Based Rapid Structural Damage Detection Using Embedded Edge Computing On Resource-Constrained Devices, Ryan Yount

Theses and Dissertations

Structural Health Monitoring (SHM) is essential for ensuring reliability and longevity of useful structures. Traditional SHM approaches rely on manual or remote data transmission and external processing, which introduce latency and can depend on stable communication links. This work aims to advance SHM by integrating edge-computing techniques for rapid damage detection to enable faster and more autonomous structural assessments in resource-constrained environments. The research spans three key contributions including 1) frequency-based damage detection of civil structures using a sensor package with the addition of an edge processor, 2) additions to the computational efficiency of the edge-computing sensor package in a …

Swvl: A Custom Ai-Powered Face Tracking Camera Gimbal, Alexander J. Anderson-Mcleod, Jakub Jerzmanowski, Michael Laitarovsky, Trevor Allison, Jagger Tanner

Senior Theses

In response to the growing demand for smarter, more responsive face tracking cameras in the post-pandemic world, our team designed SWVL, a custom AI-powered face tracking gimbal meant to address the limitations commonly encountered by the commercial models currently on the market. These commercially available gimbals come with several issues, such as frequently losing track of the person in the frame and requiring manual resets, which we sought to fix with our implementation. We designed a system with fully custom hardware and software including a 3D printed dual-axis camera gimbal driven by stepper motors, a control PCB based around an …

Cross-Layer Design And Optimization Of Analog In-Memory Computing Systems, Md Hasibul Amin

Theses and Dissertations

There has been a rapid growth in the computational demands of machine learning (ML) workloads in recent days. Conventional von Neumann architectures are not capable of keeping up with the high cost of data movement between the processor and memory, well-known as memory wall problem. In-memory computing (IMC) has been focused as a solution by the researchers, where the computation is performed inside the memory devices such as SRAM, MRAM, RRAM etc. Most commonly, the memory devices are arranged in a crossbar setting where the matrixvector multiplication (MVM) operation is performed through intrinsic parallelism of analog computations. The conventional IMC …

Hybrid Machine Learning And Comparison Error Minimization For Frequency Domain-Based Rapid State Estimation In Structures Subjected To High-Rate Boundary Change, James Scheppegrell

Theses and Dissertations

Dynamic forces and evolving structural boundary conditions pose challenges for various structural systems such as aircraft, orbital infrastructure, and energy harvesting devices. The design, evaluation, and functionality, of such systems can be aided through the collection and analysis of data. However, real-time decision-making for systems experiencing high-rate changes can pose unique challenges, if assessments are to be made accurately and rapidly enough to be relevant. In cases where the systems are well-defined and thoroughly understood, monitoring the frequency response can be instrumental in determining the state of structures subjected to high-rate structural boundary condition changes. This study focuses on investigating …

Investigation Of Shear Modulus And Material Damping Of A Lightly Cemented Silty Sand Representing Offshore Soil, Joshua Jeanjaquet

Theses and Dissertations

Offshore structures remain highly relevant, making the dynamic characterization of offshore soils critical. Since many offshore soils are cemented, understanding their response to dynamic loads is essential for developing accurate models that improve the reliability and cost-effectiveness of offshore structure designs. This study examines the impact of cement content on shear modulus, shear modulus reduction, shear wave velocity, and material damping of lightly cemented fine silty sand. A series of Resonant Column and Torsional Shear tests were conducted. The results were analyzed and compared to existing literature. The modified hyperbolic model was proposed to characterize the shear modulus and damping …

Investigating The Influence Of Volumetric Water Content In The Upper Layer Of Soil On Embankment Factor Of Safety, Kiera Rose Hughes

Theses and Dissertations

Geohazards, including landslides and embankment failures, pose significant risks to railway infrastructure, necessitating early recognition methods for effective risk mitigation. This research explores the relationship between the volumetric water content in the upper soil layer and the embankment's factor of safety (FOS) to improve early detection and stability assessment. A parametric analysis was conducted to evaluate how variations in the soil type (shear strength and hydraulic conductivity), slope geometry (angle and height) and recharge conditions (rainfall intensity and duration) affect the FOS. Using the GeoStudio software, seepage and slope stability analyses were performed for two baseline soils, as well as …

Supercritical Water Oxidation For Removing Pfas Contaminants: A Numerical Study, Ashley Joy Poyner

Theses and Dissertations

Per – and polyfluoroalkyl substances (PFAS) are a group of stable pollutants known for their long lifetimes and resistance to treatment methods used traditionally to remove other contaminants. PFAS and their ability to accumulate in both aquatic and terrestrial environments present threats such as detrimental effects to people and ecosystems due to their ability to penetrate entire systems. Though many methods are being explored to treat PFAS contamination in different systems, Supercritical Water Oxidation (SCWO) is a promising method for both removing and decomposing PFAS, with the ability to completely oxidize these persistent contaminants. This study is a numerical investigation …