Engineering Commons^™

Nebraska Balanced Mix Design - Phase Ii, Farzad Yazdipanah, Mahdieh Khedmati, Jamilla Teixeira, Hamzeh Haghshenas

Nebraska Department of Transportation: Research Reports

This study aimed to propose a performance-based framework to evaluate highly recycled asphalt mixtures containing polymer-modified binders for potential use in a balanced mix design (BMD) specification in the state of Nebraska. For that, loose and compacted mixtures were directly collected from plant and field projects within a benchmarking study, subjected to an extensive experimental program. The laboratory investigation employed different monotonic tests recommended in BMD Phase 1, as well as the dynamic modulus test. Three long-term aging protocols were investigated in terms of their impact on the mechanical, rheological, and chemical characteristics of asphalt mixtures and binders. Moreover, field …

Blockchain-Based Ai-Assisted Cyber-Physical Systems For Robust And Reliable Machining Processes, Prithbey Raj Dey, David Lee Enke

Engineering Management and Systems Engineering Faculty Research & Creative Works

This study demonstrates the prospects of Blockchain-based Cyber-Physical Systems (CPS) to establish a scalable framework for designing a secured, automated, and traceable modeling in machining processes. Machining operations like turning, being inherently complex, rely on different types of explanatory parameters such as feed rate, depth of cut, cutting speed, tools, and environmental factors. All of these variables significantly influence key response variables like surface quality, tool wear, cutting forces, and energy consumption. The proposed Blockchain-based framework, designed using the Object-Process Methodology (OPM) systems modeling language, enables the reliable exchange of comparative data streams within a unified data analytics platform. By …

Linear Mixed Model For The Surface Roughness Prediction In Hard Turning Operation, Prithbey Raj Dey, David Lee Enke

Engineering Management and Systems Engineering Faculty Research & Creative Works

Surface machining using hard turning is an intricate operation due to the influence of multiple machining parameters, their non-linear interactions, and the inherent variability introduced by different experimental trials. This study proposes a Linear Mixed Model (LMM) for predicting surface roughness, effectively addressing the challenges in traditional linear models, posed by the influencing factors, non-linearity, and interactions. The LMM incorporates variability from both fixed effects, such as cutting parameters (feed rate, depth of cut, and cutting speed), and random effects arising from tool wear across experimental runs. As a result, it provides a more comprehensive understanding of how these factors …

Burst Pressure Performance Of Multilayer Co-Extruded Polystyrene/Poly (Methyl Methacrylate) Pipes, Negar Shaghaghi, Erik Steinmetz, João Maia

Faculty Scholarship

This study investigates advancements in multilayer co-extrusion of annular structures, focusing on the mechanical properties of a 129-layer polystyrene (PS)/poly(methyl methacrylate) (PMMA) pipe with 50% PS as a skin layer for proof-of-concept validation. We explore the impact of angular rotation on burst pressure, layer structure, and failure mechanisms, emphasizing the potential of this technique for enhanced mechanical performance through biaxial orientation and the elimination of weld lines. Utilizing high aspect ratio (HAR) multipliers and angular rotation, our findings demonstrate improved layer structure and burst pressure performance at higher velocity ratios, which is the ratio of the linear component of angular …

Axiomatic Aggregation Data, Carl D. Sorensen, Christopher A. Mattson, Michael L. Anderson, Thomas J. Ashworth

ScholarsArchive Data

This data set includes the ideation results of an experiment in ideation effectiveness, along with tools for analyzing the quality of the ideation.

All ideas generated by 15 teams of BYU students are included in the database. The ideas have been placed into an OPED genealogy tree format. The quality of all the ideas has been evaluated. The novelty of one team's ideas has been evaluated.

Instructions for performing the OPED organization, evaluating quality, and evaluating novelty are included in the data workbook.

Software tools to aggregate the individual evaluations into team scores and to display the results of the …

Comprehensive Risk Assessment Of Power Grids Using Fuzzy Bayesian Networks Through Expert Elicitation: A Technical Analysis, Yasir Mahmood, Nof Yasir, Nita Yodo, Ying Huang, Di Wu, Roy A. Mccann

Electrical Engineering and Computer Science Faculty Publications and Presentations

Power grid infrastructures, essential to modern societies for electricity distribution, are prone to vulnerabilities due to their numerous sensitive components, necessitating a comprehensive risk assessment. Uncertainty in historical failure data often compromises accurate risk quantification, leading to the integration of expert elicitation as a solution. This study develops a Bayesian network (BN) risk assessment model integrated with fuzzy set theory (FST), referred to as the fuzzy Bayesian network (FBN). By incorporating expert insights, this model quantifies internal and external risk variables more comprehensively. Crisp probabilities (CPr), derived from regional transmission operator (RTO) failure incident data, are complemented by fuzzy probabilities …

Attitude Determination & Control Algorithms, Utah State University Space Dynamics Laboratory

Space Dynamics Laboratory Publications

Purpose

• Quick-look overview of all elements of ADCS (e.g. this control loop)
• Give references and terminology but not the complete "how-to"

Optimizing Concrete Strength: How Nanomaterials And Ai Redefine Mix Design, Dan Huang, Guangshuai Han, Ziyang Tang

Physics and Engineering Science

Nanomaterials and supplementary cementitious materials (SCMs) are typically used together in efforts to enhance the performance of concrete and mitigate the environmental impact of concrete construction. However, the complex interactions between nanomaterials, SCMs, and cement make concrete mix design a challenging, iterative, and labor-intensive process, often relying on trial-and-error experimentation. Machine learning (ML) offers an opportunity to better understand the influence of input parameters and to accelerate the optimization of mix designs through data-driven insights. This study proposes an open-source and easy-to-access framework, Canopy, to support the concrete research community in optimizing mix design. Using a dataset collected from the …

Restricted Nonlinear Simulations Of Flow Over Riblets: Characterizing Drag Reduction And Its Breakdown, Xiaowei Zhu, Bianca Fontanin Viggiano, Benjamin A. Minnick, Dennice Gayme

Mechanical and Materials Engineering Faculty Publications and Presentations

The restricted nonlinear (RNL) model is employed as low-order representation of turbulent flow over riblets at 𝑅𝑒𝜏 ≈ 395. Comparisons with direct numerical simulations (DNS) verify the ability of the model to accurately capture low-order statistics, as well as trends in drag-alteration and secondary motion as a function of riblet geometry and spacing. We demonstrate the ability of the RNL model to reproduce additional flow features by decomposing the roughness function to isolate contributions from the total stress and comparing its predictions to DNS data. An analysis of the spectra of Reynolds shear stress shows that the RNL model captures …

Surface Resistivity Correlation To Nano-Defects In Laser Powder Bed Fused Molybdenum (Mo)-Silicon Carbide (Sic) Alloys, Andrew Mason, Larry W. Burggraf, Ryan A. Kemnitz, Nate Ellsworth

Faculty Publications

The integration of Silicon Carbide (SiC) nanoparticles into Laser Powder Bed Fusion (LB-PBF) Molybdenum (Mo) printing represents a significant advancement in refractory metal additive manufacturing. Our investigation examined how varying SiC nanoparticle sizes affect the microstructural and electrical properties of LB-PBF-printed molybdenum components while maintaining a 0.01 mass fraction of Mo. At an Linear Energy Densities (LED) of 1.8 J/mm, the addition of 80 nm SiC particles achieved a 46% reduction in porosity, while sheet resistance decreased by 6% at LED of 2.0 J/mm with 80 nm SiC particles. These performance improvements stem from several mechanisms: SiC particles serve as …

Modeling Rice Leaf Area Index And Canopy Height In The Us Mid-South Region, Ellie J. Kuhn, Beatriz Moreno-Garcia, Michele Reba, Kusum Naithani, Benjamin R. Runkle

Biological and Agricultural Engineering Faculty Publications and Presentations

Crop growth modeling plays a critical role in addressing the global challenges of food scarcity, carbon cycling, and water management. By simulating crop development from environmental factors, these models help predict harvest yield and carbon or water cycle terms and thus can inform policy and investment decisions. However, for some agricultural regions, such as the US Mid-South, there is a lack of comprehensive data specific to rice (Oryza sativa) cultivars and their growing conditions. Here, we use 30 field seasons of observational data to predict leaf area index (LAI) and canopy height (Hcan), key inputs for crop growth models, for …

Levels Of The Nicotine Analog 6-Methyl Nicotine As A Naturally Formed Tobacco Alkaloid In Tobacco And Tobacco Products, James F. Pankow, Wentai Luo, Kevin J. Mcwhirter, Mohana Sengupta, Robert M. Strongin

Civil and Environmental Engineering Faculty Publications and Presentations

S-6-methyl nicotine (S-6MN) has appeared as a nicotine substitute in commercial electronic e-cigarette products and pouches, including with the claim that such use is not regulated under current U.S. law. This work describes an analytical chemistry based search for the natural S/R presence of 6MN and three other MN compounds in additive-free cured leaf tobaccos and in multiple commercial tobacco products. The samples were extracted using 5 N NaOH, then methyl t-butyl ether. The extracts were analyzed using gas chromatography (GC) with mass spectrometric (MS) detection, and liquid chromatography (LC) with high resolution MS/MS detection. GC peaks with the correct …

True-Bsg: A True Random Bit-Stream Generator For Fast And Efficient Stochastic Computing, Mehran Shoushtari Moghadam, M. Hassan Najafi

Faculty Scholarship

Stochastic computing (SC) leverages random bitstreams to perform arithmetic operations, offering ultra-lowcost, fault-tolerant, and highly parallelizable computations. The quality of these bit-streams is crucial for the accuracy and reliability of SC. This paper introduces TRUE-BSG, a novel true random bit-stream generator designed for fast and energyefficient SC. Unlike state-of-the-art (SoTA) pseudo-random and quasi-random bit-stream generators, TRUE-BSG utilizes a highquality true random number generator (TRNG), capable of producing random bits at a rate of 1 Gigabit per second. Our TRNG ensures high entropy and minimal correlation. TRUE-BSG shows comparable accuracy to software-based generators and better energy efficiency than SoTA bit-stream generators, …

Ams-Hd: Acute Mountain Sickness Detection With Hyperdimensional Computing, M. Hassan Najafi, Mehran Shoushtari Moghadam

Faculty Scholarship

Acute mountain sickness (AMS) is a potentially life-threatening condition that affects many individuals traveling to high altitudes. Early diagnosis is crucial, especially for travelers who may not have immediate access to medical resources. While traditional machine learning (ML) methods have been used to detect AMS using biomedical data (e.g., heart rate, blood oxygen saturation, respiration rate, blood pressure, and body temperature), hyperdimensional computing (HDC) has yet to be explored for this purpose using the few of biomedical data. Previous classification methods fall short of balancing accuracy with low hardware complexity, but HDC offers a promising solution. HDC provides a hardware-efficient …

Exploring Bim Capabilities For Life Cycle Assessment In Structural Projects: A Valencian Barraca Case Study For Sustainable Material Choices, Oscar Selfa, Barry Mcauley, Kieran Lakhera O'Shea

Conference Papers

The construction sector significantly contributes to greenhouse gas (GHG) emissions, resource consumption, and waste generation, with structural materials accounting for up to 40% of embedded carbon. This study investigates the integration of Building Information Modelling (BIM), Finite Element Analysis (FEA), and Life Cycle Assessment (LCA) tools to promote sustainable structural design. Using a BIM model of a Valencian barraca, three scenarios were evaluated: traditional materials (adobe and wood), concrete with steel, and concrete with Cross- Laminated Timber (CLT). Results show that natural materials generate lower impacts, while steel and concrete exhibit higher emissions. This work proposes a workflow integrating BIM, …

A Joint Geometric Topological Analysis Network (Jgta-Net) For Detecting And Segmenting Intracranial Aneurysms, Xinyue Zhang, Zonghan Lyu, Yang Wang, Bo Peng, Jingfeng Jiang

Michigan Tech Publications

Objective: The rupture of intracranial aneurysms leads to subarachnoid hemorrhage. Detecting intracranial aneurysms before rupture and stratifying their risk is critical in guiding preventive measures. Point-based aneurysm segmentation provides a plausible pathway for automatic aneurysm detection. However, challenges in existing segmentation methods motivate the proposed work. Methods: We propose a dual-branch network model (JGTANet) for accurately detecting aneurysms. JGTA-Net employs a hierarchical geometric feature learning framework to extract local contextual geometric information from the point cloud representing intracranial vessels. Building on this, we integrated a topological analysis module that leverages persistent homology to capture complex structural details of 3D objects, …

Emerging Investigator Series: Are We Undervaluing Septage? Rethinking Septage Management For Nutrient Recovery And Environmental Protection, Kevin Orner, Stetson Rowles, Sara F. Heger, Ben Howard

Civil Engineering & Construction: Faculty Publications

An estimated 20–25% percent of households in the US rely on on-site sanitation via septic tanks to manage their wastewater. Septage management strategies such as land application, treatment at wastewater treatment plants, and treatment at independent septage treatment plants are common regulated and protective processes for managing septage. There can, however, be potentially negative environmental impacts such as groundwater contamination if septic systems are failing or improperly designed. In this perspective, we reimagine septage management at each step of the septage value chain, identify barriers to change, and propose solutions to overcome these existing barriers. Reimagined septage management can take …

The Electrostatic Charge On Exuded Liquid Drops, Schuyler Arn, Pablo Illing, Joshua Mendéz Harper, Justin C. Burton

Electrical and Computer Engineering Faculty Publications and Presentations

Fluid triboelectrification, also known as flow electrification, remains an under-explored yet ubiquitous phenomenon with potential applications from material science to planetary evolution. Building upon previous efforts to position water within the triboelectric series, we investigate the charge on individual, millimetric water drops falling through air. Our experiments measured the charge and mass of each drop using a Faraday cup mounted on a mass balance, and connected to an electrometer. For pure water in a glass syringe with a grounded metal tip, we find the charge per drop (Δ/Δ) was approximately -5 pC g to -1 pC g. This was independent …

Statistical Approach To Turbulent Dispersal Of Aerosols For Accurate Prediction Of Concentration And Associated Uncertainties, K. A. Krishnaprasad, ‪Nadim Zgheib, S. Balachandar

Mechanical Engineering Faculty Publications

In the context of turbulent dispersal of aerosol pollutants from a source within a ventilated indoor space, the present work addresses the importance of going beyond accurate prediction of the mean ensemble-averaged exposure, by evaluating the expected level of variability in individual realizations. This uncertainty quantification requires a statistical description of the inherent stochastic turbulent dispersal process and the inhomogeneous nature of the indoor flow. We leverage large datasets from turbulence-resolving EulerLagrange simulations of aerosol dispersal in varying indoor geometries. The datasets provide time-resolved concentrations of pollutants emitted by a source located anywhere in a room and reaching a sink …

A Causal-Comparative Study Between The Aeronautical Decision-Making Skills Of Collegiately And Non-Collegiately Trained Private Pilots, Karl P. Winters

Doctoral Dissertations and Projects

The purpose of this quantitative causal-comparative design study was to examine the relationship between private pilots’ flight training background (collegiate or non-collegiate) and aeronautical decision-making (ADM) skills among recently certificated private pilots enrolled in a commercial pilot flight training course at a Part 141 flight training program in a large, private, mid-Atlantic university. The problem is that although deficient ADM is a known major contributor to fatal general aviation (GA) accidents, it is unknown if there is a difference in the ADM skills of collegiately and non-collegiately trained private pilots. This study, built on the framework of dual-process theory and …

Causation Analysis Of Crane-Related Accident Reports By Utilizing Chatgpt And Complex Networks, Yifan Wang, Junyu Chen, Bo Xiao, Shane T. Mueller, Jingjing Guo

Michigan Tech Publications

This study integrates ChatGPT and complex network (CN) techniques into an accident analysis framework designed to reduce manual effort in accident causation analysis. The proposed framework supports construction stakeholders in extracting causal factors (CFs) from accident reports and identifying both critical CFs and key causal paths. A multistep research design was adopted to develop and validate this novel framework for analyzing crane-related construction accident reports using ChatGPT and CN techniques. First, ChatGPT was prompted to extract CFs from a database of crane-related accident reports. Second, evaluation metrics and an expert questionnaire survey were developed to assess ChatGPT’s performance in CF …

Engineering The Immune Response To Biomaterials, Abolfazl Salehi Moghaddam, Mehran Bahrami, Einollah Sarikhani, Rumeysa Tutar, Yavuz Nuri Ertas, Faleh Tamimi, Ali Hedayatnia, Clotilde Jugie, Houman Savoji, Asma Talib Qureshi, Muhammad Rizwan, Chima V. Maduka, Nureddin Ashammakhi

Michigan Tech Publications

Biomaterials are increasingly used as implants in the body, but they often elicit tissue reactions due to the immune system recognizing them as foreign bodies. These reactions typically involve the activation of innate immunity and the initiation of an inflammatory response, which can persist as chronic inflammation, causing implant failure. To reduce these risks, various strategies have been developed to modify the material composition, surface characteristics, or mechanical properties of biomaterials. Moreover, bioactive materials have emerged as a new class of biomaterials that can induce desirable tissue responses and form a strong bond between the implant and the host tissue. …

Predicting Seizure Onset Zones From Interictal Intracranial Eeg Using Functional Connectivity And Machine Learning, Jared Pilet, Scott A. Beardsley, Chad Carlson, Christopher T. Anderson, Candida Ustine, Sean Lew, Wade Mueller, Manoj Raghavan

Biomedical Engineering Faculty Research and Publications

Functional connectivity (FC) analyses of intracranial EEG (iEEG) signals can potentially improve the mapping of epileptic networks in drug-resistant focal epilepsy. However, it remains unclear whether FC-based metrics provide additional value beyond established epilepsy biomarkers such as epileptic spikes and high-frequency oscillations (HFOs). Using interictal iEEG data from 26 patients, we estimated FC across eight frequency bands (4–290 Hz) using amplitude envelope correlation (AEC) and phase locking value (PLV). From the resulting FC-matrices, we estimated two graph metrics each to derive 32 FC-based features. We also extracted features related to spikes, HFOs, and power spectral densities (PSD). A trained support …

Probabilistic Cash Flow Analysis Considering Risk Impacts By Integrating 5d-Building Information Modeling And Bayesian Belief Network, Mohammad Hosein Madihi, Mohammadsoroush Tafazzoli, Ali Akbar Shirzadi Javid, Farnad Nasirzadeh

Civil Engineering & Construction: Faculty Publications

Unrealistic cash flow forecasts negatively affect project stakeholders and are a common issue for construction practitioners. This study proposes a new method for predicting the probabilistic cash flow of a project that can automate the calculation process while considering the impact of risks and their inter-related structure. This research integrates a Bayesian Belief Network (BBN) and 5D-BIM to provide a new probabilistic cash flow analysis approach. Here, 5D-BIM is used to facilitate cash flow calculations and automate the process. The BBN has also been implemented to assess the impact of risk factors on project cash flow, considering their complex inter-related …

Selected Artificial Intelligence Provisions In U.S. Fiscal Year 2025 National Defense Authorization Act, Bert Chapman

Libraries Faculty and Staff Presentations

The 2025 Fiscal Year National Defense Authorization Act contains multiple provisions relating to artificial intelligence (AI). These congressionally mandated provisions direct various sections of the Department of Defense (DOD) and individual U.S. armed service branches to execute congressional intent for AI policymaking. Examples of such intent include identifying and planning DOD's AI workforce, demonstrating AI biotechnology applications for national security, improving the human usability of AI systems, and establishing an AI security center. This presentation will note that reports on these initiatives must be prepared for relevant congressional oversight committees, and, in many cases, are in many cases, publicly released …

The Influence Of Extrusion Geometry And Ratio On Extrudate Mechanical Properties For A 6005a Alloy Containing Either Sc And Zr Or Cr And Mn Dispersoid Formers, Eli A. Harma, Paul Sanders, Thomas Wood, Timothy Langan

Michigan Tech Publications

There is a demand for a 6005A series extrusion alloy with improved strength that maintains good extrudability. Replacing Mn and Cr dispersoid formers with Sc and Zr is expected to increase the room temperature mechanical properties while not affecting extrudability. Al3X dispersoids with a Sc core surrounded by a Zr shell are stable at higher temperatures and enhance recrystallization resistance and precipitation strengthening. However, there is little information on how the Sc and Zr additions affect the properties of an extrudate as a function of extrusion geometry and ratio. A 6005A series alloy with Cr and Mn additions is compared …

Sharc: Simulator For Hardware Architecture And Real-Time Control, Paul K. Wintz, Yasin Sonmez, Paul Griffioen, Mingsheng Xu, Surim Oh, Heiner Litz, Ricardo G. Sanfelice, Murat Arcak

Faculty Work Comprehensive List

Tight coupling between computation, communication, and control pervades the design and application of cyber-physical systems (CPSs). Due to the complexity of these systems, advanced design procedures that account for these tight interconnections are paramount to ensure the safe and reliable operation of control algorithms under computational constraints. This paper presents the Simulator for Hardware Architecture and Real-time Control (Sharc) to assist in the co-design of control algorithms and the computational hardware on which they are run. Sharc simulates the execution of a user-specified control algorithm on a given processor microarchitecture configuration, evaluating how computational constraints affect the dynamical properties of …

Thermaltrack Dataset - Training Labels - Sequence 1-11, Yiming Yang, Jeremy P. Bos

ThermalTrack

We present a wheel track detection system that leverages RGB-Thermal (RGB-T) imaging, where thermal channels reveal critical temperature differentials between compacted tracks and loose snow - tracks exhibit higher thermal inertia and lower reflectivity, emitting stronger radiation signatures even in visually homogeneous conditions. By fusing these distinctive thermal patterns with RGB spatial information, our method reliably identifies navigable tracks, enabling robust path-following in complete white-out conditions where snow textures and terrain features become indistinguishable.

Experimentally Driven Numerical Model Of Carbon/Polyaniline-Based Glucose Monitoring Sensors: An Evaluation Using A New Figure Of Merit, Kyrillos Selim, Ziad Khalifa, Amira Ali, Sameh O. Abdellatif

Electrical Engineering

This study presents an experimentally driven numerical model for evaluating carbon/polyaniline (PANI)-based glucose monitoring sensors (GMSs), focusing on innovative configurations using graphene-PANI and carbon nanotube (CNT)-PANI composites. We performed a thorough analysis of the morphological, electrophysical, and electrical properties of these materials, ultimately leading to the extraction of key electrical parameters for integration into a finite element model (FEM). This model simulates the entire sensor, enabling the estimation of critical performance metrics such as sensitivity, limit of detection (LOD), linearity, and power consumption. Our findings demonstrate that the CNT-PANI configuration significantly outperforms the laser-induced graphene (LIG)-PANI electrode, achieving a figure …

Reliability Of Cazro3 And Mgo Capacitors At High Temperatures, Alan Devoe, Hung Trinh, Fatih Dogan

Materials Science and Engineering Faculty Research & Creative Works

Calcium zirconate and magnesium oxide have both been found to have useful dielectric properties at high temperatures. Multilayer ceramic capacitors (MLCCs) were made with these dielectrics and platinum electrodes. The relationship between the lifespan (mean-time to failure) and applied voltage was determined using Weibull statistics. CaZrO₃ capacitors were tested between 550◦C and600◦C while MgO capacitors were tested between 600◦C and 650◦C. The temperature coefficient Ea and the voltage coefficient n of the Prokopowicz Vaskas equation were determined under various test conditions. Activation energiesforcalciumzirconateandmagnesiumoxidewere3.25eVand3.48eV, respectively. Resistance degradation of CaZrO₃ and MgO capacitors, tested at 600◦Cand250V, were compared.