Engineering Commons^™

Advancing Solar Farm Resilience: Cfd-Driven Wind Load Optimization, Aly Mousaad Aly

Faculty Publications

This study investigates wind load design methods for ground-mounted solar panels and arrays by comparing Computational Fluid Dynamics (CFD) simulations with design standards. A case study of a solar farm impacted by Hurricane Maria examines the effects of elevation height, tilt angle, and variations in the American Society of Civil Engineers (ASCE) standards on wind loads and structural failure. Turbulence models, including Reynolds Stress Model, k–ε Model, and Large Eddy Simulation (LES), are used to analyze wind pressures, lift, drag, and peak pressures. Results show Phase 1 experienced higher wind loads than Phase 2 due to design differences. A cost-benefit …

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.

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 …

Distribution Comparisons Of Eac Cost Growth For Aircraft Work Breakdown Structure Elements, Kyle P. Marquis, Edward D. White, Brandon M. Lucas, Robert D. Fass, Jonathan D. Ritschel, Shawn M. Valentine

Faculty Publications

This article analyzes and investigates the distribution of cost growth of the Estimate at Completion (EAC) for the Work Breakdown Structure (WBS) elements of approximately 60 historical United States Acquisition Category I Research, Development, Test and Evaluation aircraft programs. Using the method of maximum likelihood in conjunction with the Akaike Information Criterion, the authors suggest that both the lognormal and Weibull distributions provide relatively good fit to EAC cost growth, with the lognormal slightly edging out the Weibull. As a summarized finding, the authors present their empirical results for the mean, coefficient of variation (CV), the 15th and 85th percentiles …

The Effects Of Snow Cover On The Dynamic Pressure Of Nuclear Detonation Blast Waves, Adam Card, Andrew W. Decker

Faculty Publications

Blast pressure is the primary military targeting metric for nuclear weapons. Any local conditions that affect blast pressure have the potential for altering nuclear plans, both from defensive and offensive standpoints. Understanding the impact of snow to the blast wave, therefore, provides a benefit both to military planners and to warfighters on the ground, for any operation occurring in arctic environments. No existing data provides a quantitative description of how snow on the ground affects a nuclear detonation blast wave passing over it. Similar blast waves passing over dust have experimentally proven to enhance blast pressure in a localized region.1 …

Machine Learning For Reactor Power Monitoring With Limited Labeled Data, C. L. Stewart, B. L. Goldblum, R. G. Abbott, L. Appleby, Brett J. Borghetti, V. Hollingshead, J. H. Whetzel

Faculty Publications

Real-time reactor power monitoring is critical for a variety of nuclear applications, spanning safety, security, operations, and maintenance. While machine learning methods have shown promise in monitoring reactor power levels, there is limited research on their efficacy in label-starved environments. The goal of this work is to assess the feasibility of classifying nuclear reactor power level using multisource data in scenarios with limited labels. Data were collected using low-resolution multisensors at four nuclear reactor facilities: two large research reactors and two TRIGA reactors. Within each pair, one reactor dataset served as the source and the other as the target in …

The Impact Of Solar Angle And Cloud Shadows On 3d Reconstruction Of Rolling Stock Cargo, Carlina M. Ostrand, Adam D. Reiman, Frank W. Ciarallo, Scott L. Nykl, Clark N. Taylor, Joshua F. Krutz

Faculty Publications

Meeting the relentless demand for more efficient air cargo transportation is of paramount importance for commercial needs and military missions. This study describes an experiment to test an innovative approach that harnesses cutting-edge stereoscopic vision technology to create 3D point clouds of rolling stock cargo across varying solar angles and cloud shadow conditions. Virtual cargo point clouds are generated by calibrating and systematically organizing the depth and location points from an RGB-D camera and then reprojecting them in a virtual environment. Measurement accuracy was rigorously tested across six camera positions in various combinations of weather conditions against physical ground truth …

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 …

Enhancing Mucus Flow And Clearance By Grafting Zwitterionic Hydrogel Films To Luminal Surfaces, Ryan Horne, Caleb Escudero, Morgan Ellerman, C. Allan Guymon, Marlan Hansen

Faculty Publications

Objective

To determine the effects of zwitterionic hydrogel films on mucus contact angles, flow, and stasis with respect to medical polymer surfaces, both flat and tubular.

Methods

A zwitterionic hydrogel thin film was photografted onto medical rubber surfaces and compared against non-zwitterionic hydrogel thin films and untreated surfaces to determine its impact on mucus contact angles, mucus flow on sheets and tubes, and mucus plugging.

Results

Zwitterionic and conventional hydrogel films significantly reduce the mucus contact angles and the tilt required to initiate mucus flow on sheets and in tubular systems. Preliminary experiments show that these films may also shorten …

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 …

Uncertainty Propagation And Sensitivity Analysis For Constrained Optimization Of Nuclear Waste Vitrification, Lagrande Gunnell, Xiaonan Lu, John D. Vienna, Dong-Sang Kim, Brian J. Riley, John Hedengren

Faculty Publications

The vitrification of high-level waste (HLW) by heating a mixture of glass-forming chemicals (GFCs) with the waste can be improved using a constrained optimization problem. This study explores how different uncertainty propagation (UP) methods implemented with the optimization process can affect the glass formulation of nuclear waste glasses. UP is the effort of propagating uncertain inputs through a system to understand and quantify output distributions. Uncertainty intervals are crafted from output distributions to inform the optimization algorithm. UP is often implemented with Monte Carlo (MC) sampling for large nonlinear systems, which can be difficult to implement within a constrained optimization …

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 …

Increasing A-Type Co32− Substitution Decreases The Modulus Of Apatite Nanocrystals, Stephanie Wong, Abigail Eaton, Christina Krywka, Arun Nair, Christophe Drouet, Alix Deymier

Faculty Publications

Biological apatite mineral is highly substituted with carbonate (CO₃²⁻). CO₃²⁻ can exchange for either phosphate, known as B-type, or hydroxyl groups, known as A-type. Although the former has been extensively studied, A-type CO₃²⁻ substituted apatites are poorly understood. Therefore, A-type CO₃²⁻ apatites with biologically relevant levels of CO32− (1.7–5.8 wt%) were prepared and characterized. The addition of A-type CO₃²⁻ into the apatite structure caused the predicted expansion of the a-axis and contraction of the c-axis in the unit cell. This was accompanied by a significant modification in the atomic …

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 …

Potential Of Individual Upper-Limb Muscles To Contribute To Postural Tremor: Simulations From Neural Drive To Joint Rotation, Spencer A. Baker, Landon J. Beutler, Daniel B. Free, Dario Farina, Steven Knight Charles

Faculty Publications

Background: It is unclear which muscles contribute most to tremor and should therefore be targeted by tremor suppression methods. Previous studies used mathematical models to investigate how upper-limb biomechanics affect muscles’ potential to generate tremor. These investigations yielded principles, but the models included at most only 15 muscles. Here we expand previous models to include 50 upper-limb muscles, simulate tremor propagation, and test the validity of the previously postulated principles. Methods: Tremor propagation was characterized using the gains between tremorogenic neural drive to the 50 muscles (inputs) and tremulous joint rotations in the 7 joint degreesof- freedom (DOF) from shoulder …

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 …

Toward Quantifying Interpolation Uncertainty In Set-Line Spacing Hydrographic Surveys, Elias Adediran, Christos Kastrisios, Kim Lowell, Glen Rice, Qi Zhang

Faculty Publications

The oceans remain one of Earth’s last great unknowns, with about 74% still unmapped to modern standards. Consequently, interpolation is employed to create seamless digital bathymetric models (DBMs) from incomplete hydrographic datasets, but this introduces unquantified depth uncertainties. This study aims to estimate and characterize uncertainties arising from set-line spacing hydrographic surveys, which are important for nautical charting, navigational safety, and many other applications. By sampling at different line spacings four complete coverage testbeds that vary in slope and roughness, the study interpolates across entire testbed areas using Spline, Inverse Distance Weighting, and Linear interpolation. The resulting interpolation uncertainties are …

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.

Streamlined Production, Protection, And Purification Of Enzyme Biocatalysts Using Virus-Like Particles And A Cell-Free Protein Synthesis System, Seung O. Yang, Joseph P. Talley, Gregory H. Nielsen, Kristen M. Wilding, Bradley Charles Bundy

Faculty Publications

Enzymes play an essential role in many different industries; however, their operating conditions are limited due to the loss of enzyme activity in the presence of proteases and at temperatures significantly above physiological conditions. One way to improve the stability of these enzymes against high temperatures and proteases is to encapsulate them in protective shells or virus-like particles. This work presents a streamlined, three-step, cell-free protein synthesis (CFPS) procedure that enables rapid in vitro enzyme production, targeted encapsulation in protective virus-like particles (VLPs), and facile purification using a 6× His-tag fused to the VLP coat protein. This process is performed …

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 …

The Design And Cell-Free Protein Synthesis Of A Pembrolizumab Single-Chain Variable Fragment, Landon E. Ebbert, Tyler J. Free, Mehran Soltani, Bradley Charles Bundy

Faculty Publications

Background/Objectives: Cancer is a leading cause of death. However, recently developed immunotherapies have shown significant promise to improve cancer treatment outcomes and survival rates. Pembrolizumab, a cancer immunotherapy drug, enables a strong T-cell response specifically targeting cancer cells to improve patient outcomes in more than 16 types of cancer. The increasing demand for pembrolizumab, the highest selling drug in 2023, increases global dependence on drug production, which can be vulnerable to supply chain disruptions. Methods: Cell-free protein synthesis (CFPS) is a rapid in vitro protein production method that could provide the production of an immunotherapy drug in an emergency and …