Engineering Commons^™

Reconstructing Modis Aerosol Optical Depth And Exploring Dynamic And Influential Factors Of Aod Via Random Forest At The Global Scale, Bin Guo, Zheng Wang, Lin Pei, Xiaowei Zhu, Qiuji Chen, Haojie Wu, Wencai Zhang, Dingming Zhang

Mechanical and Materials Engineering Faculty Publications and Presentations

Aerosols pose a significant impact on the atmospheric environment and public health. Aerosol optical depth (AOD) is one of the essential parameters for quantifying aerosols. Currently, existing AOD products suffer from severe pixel value missing issue due to clouds and other interferences globally, which severely affects the accuracy of fine particulate matter estimation and related epidemics researches. At the global scale, this study first adopted population as one of the influencing factors of AOD, and fully considered the effects of humidity, surface pressure, cloud cover, and evapotranspiration on AOD. Meanwhile, the spatiotemporal resolution of meteorological data was promoted to improve …

Floating Wind Farm Experiments Through Scaling For Wake Characterization, Power Extraction, And Turbine Dynamics, Juliaan Bossuyt, OndˇRej FercˇÁk, Zein Sadek, Charles Meneveau, Dennice Gayme, Raúl Bayoán Cal

Mechanical and Materials Engineering Faculty Publications and Presentations

In this study, wind and water tunnel experiments of turbulent wakes in a scaled floating wind farm are performed. Scaling of a floating wind farm with a scaling ratio of 1:400 is made possible by relaxing geometric scaling of the turbine platform system, such that the dynamic response can be correctly matched, and to allow for relaxing Froude scaling such that the Reynolds number can be kept large enough. Four dimensionless parameters, describing the relative importance of wind and wave loads compared to turbine inertia, are used to guide the scaled floater design. Free decay tests of the pitch and …

Fintech And Entrepreneurship: An Assessment Model To Evaluate Policy Instruments For Fintech Adoption By Small And Medium Enterprises (Smes), Deemah Abdullah Alassaf

Dissertations and Theses

Small and medium enterprises (SMEs) are the engines that drive economic development. They are the backbone of the middle class as they provide social stability, innovation, inclusive growth, and poverty alleviation. SMEs contribute significantly to job creation, employment, tax provision and the Gross Domestic Product (GDP). However, they face inferior conditions and challenges when it comes to their financing compared to that of large enterprises, as well as having a high expectancy of failing. Because of these limitations, SMEs tend to grow slowly since building up higher credit is difficult for them, and in addition to this, they lack access …

Biological Nitrogen And Phosphorus Removal From Wastewater Under Low Dissolved Oxygen (Ldo) Conditions, Bashar Al-Daomi

Dissertations and Theses

This study examines the complexities of the integration of different technologies for nutrient removal, investigating parameters like sludge residence time (SRT), carbon-to-nitrogen (C/N) ratio, Anaerobic/Oxic (A/O), and Anaerobic/Oxic/Anoxic (A/O/A) systems, continuous and intermittent aeration regimes, and air OFF/air ON periods ratios for efficient nitrogen and phosphorus removal.

Two continuous aeration systems were implemented: The first aimed to carry out a simultaneous nitrification and denitrification (SNdN) process for nitrogen and phosphorus removal under low dissolved oxygen (DO) and C/N ratio conditions. This system operated with 4.5-5-day total sludge age in a single-stage anaerobic/oxic (A/O)-SBR, performing stepwise DO reduction at varying DO …

Sea Level Rise And The Drivers Of Daily Water Levels In The Sacramento-San Joaquin Delta, H. Baranes, Steven Dykstra, D. A. Jay, S. A. Talke

Civil and Environmental Engineering Faculty Publications and Presentations

Water levels in deltas and estuaries vary on multiple timescales due to coastal, hydrologic, meteorologic, geologic, and anthropogenic factors. These diverse factors increase the uncertainty of, and may bias, relative sea level rise (RSLR) estimates. Here, we evaluate RSLR in San Francisco Bay and the Sacramento-San Joaquin Delta, USA by applying a physics-based, nonlinear regression to 50 tide gauges that determines the spatially varying controls on daily mean water level for water years 2004–2022. Results show that elevated river flow and pumping (99th percentile) raise water level up to 6 m and lower it up to 0.35 m, respectively, and …

Impact Of Material And Tunnel Barrier Quality On Spin Transport In A Cvd Graphene Non-Local Spin Valve Device Array, Samuel T. Olson, Daniel Still, Kaleb Hood, Otto Zietz, Jun Jiao

Mechanical and Materials Engineering Faculty Publications and Presentations

Wafer-scale graphene films produced via chemical vapor deposition (CVD) are now commercially available, however these films inherently contain randomly distributed defects such as adlayers and grain boundaries. This report discusses the impact of these defects on the signal integrity of an array of graphene-based non-local spin valves (NLSVs). It was found that critical spin parameters fluctuate drastically between adjacent identical devices. Investigation of the channel quality indicated that adlayers do not affect spin signal significantly even when located directly in the spin transport region of the device. In contrast, grain boundary defects within the spin transport region have significant impact …

Restricted Nonlinear Scales Of Turbulent Secondary Flows Over Spanwise Heterogeneous Roughness, Benjamin A. Minnick, Xiaowei Zhu, Dennice F. Gayme

Mechanical and Materials Engineering Faculty Publications and Presentations

Recent studies, e.g., Wangsawijaya et al. (2020) have revealed the prevalence of streak meandering in turbulent flow over spanwise heterogeneous roughness. Here we exploit the scale decomposition inherent in the Restricted Nonlinear (RNL) modeling approach to further investigate this behavior. The RNL decomposition comprises a large-scale streamwise averaged mean with small-scale fluctuations about that mean defined through a dynamical restriction that leads to computational tractability. The simplified setting facilitates the study of secondary flow interacting with the large-scale streak via one-way coupling, thus enabling additional insight into relevant interaction mechanisms. In agreement with the experimental work, our results indicate that …

A Tessellation Of Engineers, Grace Jenkins Welsh

University Honors Theses

A capstone project is the culmination of what a student has learned while pursuing an undergraduate degree. A project of this magnitude requires a lot of time and dedication from every member of the team. This essay looks back at the experience of an electrical engineering student participating in the design of an auxiliary power and protection of a planned substation. The author describes the team's work, how they performed as a team, and their involvement with the professional sponsor and advisor. The author examines the experience with the goal of informing and encouraging prospective engineering students who may be …

Quantum Algorithms For Unate And Binate Covering Problems With Application To Finite State Machine Minimization, Abdirahman Alasow, Marek Perkowski

Electrical and Computer Engineering Faculty Publications and Presentations

Covering problems find applications in many areas of computer science and engineering, such that numerous combinatorial problems can be formulated as covering problems. Combinatorial optimization problems are generally NPhard problems that require an extensive search to find the optimal solution. Exploiting the benefits of quantum computing, we present a quantum oracle design for covering problems, taking advantage of Grover’s search algorithm to achieve quadratic speedup. This paper also discusses applications of the quantum counter in unate covering problems and binate covering problems with some important practical applications, such as finding prime implicants of a Boolean function, implication graphs, and minimization …

Material And Physical Reservoir Computing For Beyond Cmos Electronics: Quo Vadis?, Christof Teuscher

Electrical and Computer Engineering Faculty Publications and Presentations

Traditional computing is based on an engineering approach that imposes logical states and a computational model upon a physical substrate. Physical or material computing, on the other hand, harnesses and exploits the inherent, naturally-occurring properties of a physical substrate to perform a computation. To do so, reservoir computing is often used as a computing paradigm. In this review and position paper, we take stock of where the field currently stands, delineate opportunities and challenges for future research, and outline steps on how to get material reservoir to the next level. The findings are relevant for beyond CMOS and beyond von …

Chemical Strategies To Mitigate Electrostatic Charging During Coffee Grinding, Joshua Méndez Harper, Christopher H. Hendon

Electrical and Computer Engineering Faculty Publications and Presentations

The process of grinding coffee generates particles with high levels of electrostatic charge, causing a number of detrimental effects including clumping, particle dispersal, and spark discharge. At the brewing level, electrostatic aggregation between particles affects liquid-solid accessibility, leading to variable extraction quality. In this study, we quantify the effectiveness of four charge mitigation strategies. Our data suggests that adding small amounts of water to whole beans pre-grinding, or bombarding the grounds with ions produced from a high-voltage ionizer, are capable of de-electrifying the granular flows. While these techniques helped reduce visible mess, only the static reduction through water inclusion was …

Probability Distribution Fitting For Censored Data: A Practical Guide For Survival Analysis Of Nbi Data For Concrete Bridge Decks In Oregon, Asmaa Almeshaileh

Dissertations and Theses

Many sophisticated statistical models and analyses have been proposed to analyze datasets derived from the national bridge inventory (NBI). These objectives range from creating an understanding of the factors that drive the deterioration of bridge members to providing agencies with quantitative information for their maintenance and repair decisions. As these models become increasingly sophisticated and difficult to comprehend and implement, practicing engineers and owners are less likely to use them. Often, however, grouping data intuitively, for example, per a select structure type or condition rating and analyzing them using prescriptive statistics such as distribution fitting, does not only lie in …

An Overview Of Elements And Relations: Aspects Of A Scientific Metaphysics, Martin Zwick

Complex Systems Faculty Publications and Presentations

A talk on my book, Elements and Relations: Aspects of a Scientific Metaphysics. Book description:

This book develops the core proposition that systems theory is an attempt to construct an “exact and scientific metaphysics,” a system of general ideas central to science that can be expressed mathematically. Collectively, these ideas would constitute a non-reductionist “theory of everything” unlike what is being sought in physics. Inherently transdisciplinary, systems theory offers ideas and methods that are relevant to all of the sciences and also to professional fields such as systems engineering, public policy, business, and social work. To demonstrate the generality …

A Bayesian Network-Based Methodology For Improved Bridge Load Rating And Asset Management, Jeffery Mark Roberts

Dissertations and Theses

From the day a new structure is made available for use, to the day that the structure is no longer able to fulfill an intended purpose, structural safety is a vital interest. Managing a portfolio of structures can be a difficult undertaking for an asset manager, particularly if different types of structures are being maintained. The goal is to manage assets in the most efficient manner which can be influenced by, at a minimum, safety and financial concerns.

A potential tool for an asset manager or owner is the use of Bayesian Networks (BNs). When a BN is used to …

Shallow‑Water Habitat In The Lower Columbia River Estuary: A Highly Altered System, Will Templeton, David A. Jay, Heida Diefenderfer, Stefan Talke

Civil and Environmental Engineering Faculty Publications and Presentations

Decreases in shallow-water habitat area (SWHA) in the Lower Columbia River and Estuary (LCRE) have adversely affected salmonid populations. We investigate the causes by hindcasting SWHA from 1928 to 2004, system-wide, based on daily higher high water (HHW) and system hypsometry. Physics-based regression models are used to represent HHW along the system as a function of river inflow, tides, and coastal processes, and hypsometry is used to estimate the associated SWHA. Scenario modeling is employed to attribute SWHA losses to levees, flow regulation, diversion, navigational development, and climate-induced hydrologic change, for subsidence scenarios of up to 2 m, and for …

Multicolor Dye-Based Flow Structure Visualization For Seal-Whisker Geometry Characterized By Computer Vision, Ondřej Ferčák, Kathleen Lyons, Christin T. Murphy, Kristina M. Kamensky, Raul Bayoan Cal

Mechanical and Materials Engineering Faculty Publications and Presentations

Pinniped vibrissae possess a unique and complex three-dimensional topography, which has beneficial fluid flow characteristics such as substantial reductions in drag, lift, and vortex induced vibration. To understand and leverage these effects, the downstream vortex dynamics must be studied. Dye visualization is a traditional qualitative method of capturing these downstream effects, specifically in comparative biological investigations where complex equipment can be prohibitive. High-fidelity numerical simulations or experimental particle image velocimetry (PIV) are commonplace for quantitative high-resolution flow measurements, but are computationally expensive, require costly equipment, and can have limited measurement windows. This study establishes a method for extracting quantitative data …

Quantitative Gait And Balance Outcomes For Ataxia Trials: Consensus Recommendations By The Ataxia Global Initiative Working Group On Digital-Motor Biomarkers, Winfried Ilg, Sarah Milne, Tanja Schmitz-Hübsch, Lisa Alcock, Lukas Beichert, Enrico Bertini, Helen Dawes, Christopher M. Gomez, James Mcnames, Multiple Additional Authors

Electrical and Computer Engineering Faculty Publications and Presentations

With disease-modifying drugs on the horizon for degenerative ataxias, ecologically valid, finely granulated, digital health measures are highly warranted to augment clinical and patient-reported outcome measures. Gait and balance disturbances most often present as the first signs of degenerative cerebellar ataxia and are the most reported disabling features in disease progression. Thus, digital gait and balance measures constitute promising and relevant performance outcomes for clinical trials.

This narrative review with embedded consensus will describe evidence for the sensitivity of digital gait and balance measures for evaluating ataxia severity and progression, propose a consensus protocol for establishing gait and balance metrics …

Depth And Frequency Dependence Of Geoacoustic Properties On The New England Mud Patch From Reflection Coefficient Inversion, Jiang Yong-Min, Charles W. Holland, Stan E. Dosso, Jan Dettmer

Electrical and Computer Engineering Faculty Publications and Presentations

Muddy sediments cover significant portions of continental shelves, but their physical properties remain poorly understood compared to sandy sediments. This paper presents a generally applicable model for sediment-column structure and variability on the New England Mud Patch (NEMP), based on trans-dimensional Bayesian inversion of wide-angle, broadband reflection-coefficient data in this work and in two previously published reflection-coefficient inversions at different sites on the NEMP. The data considered here include higher frequencies and larger bandwidth and cover lower reflection grazing angles than the previous studies, hence, resulting in geoacoustic profiles with significantly better structural resolution and smaller uncertainties. The general sediment-column …

Localization And Shape Determination Of A Hidden Corridor In The Great Pyramid Of Giza Using Non-Destructive Testing, Mohamed Elkarmoty, Johannes Rupfle, Khalid Helal, Mohamed Sholqamy, Mohamed Fath-Elbab, Jochen Kollofrath, Thomas Schumacher, Multiple Additional Authors

Civil and Environmental Engineering Faculty Publications and Presentations

Built over 4500 years ago, the Great Pyramid of Giza, Egypt, is the only remaining structure of the Wonders of the Ancient World as described by the ancient Greek historian Herodotus. Despite this long existence, only recently has modern science and technology been employed to study this massive stone structure. Cosmic-ray muon radiography throughout the ScanPyramids project has detected a large void above the Grand Gallery of the Great Pyramid and a smaller unidentified void behind the so-called Chevron. The Chevron is an assembly of four large limestone blocks arranged in the shape of two inverted downward open angles. Guided …

A One-Dimensional Volcanic Plume Model For Predicting Ash Aggregation, D. W. Hoffman, L. G. Mastin, Alexa R. Van Eaton, Stephen Solovitz, Raul Bayoan Cal, J. K. Eaton

Mechanical and Materials Engineering Faculty Publications and Presentations

During explosive volcanic eruptions, volcanic ash is ejected into the atmosphere, impacting aircraft safety and downwind communities. These volcanic clouds tend to be dominated by fine ash (μm in diameter), permitting transport over hundreds to thousands of kilometers. However, field observations show that much of this fine ash aggregates into clusters or pellets with faster settling velocities than individual particles. Models of ash transport and deposition require an understanding of aggregation processes, which depend on factors like moisture content and local particle collision rates. In this study, we develop a Plume Model for Aggregate Prediction, a one-dimensional (1D) volcanic plume …

Development Of Prediction Method For Dimensional Stability Of 3d-Printed Objects, Kyung-Eun Min, Jae-Won Jang, Jesik Shin, Cheolhee Kim, Sung Yi

Mechanical and Materials Engineering Faculty Publications and Presentations

Fused deposition modeling (FDM), as one of the additive manufacturing processes, is known for strong layer adhesion suitable for prototypes and end-use items. This study used a multiple regression model and statistical analysis to explore the dimensional accuracy of FDM objects. Factors such as inclination angle, layer thickness, support space, and raster angle were examined. Machine learning models (Gaussian process regression (GPR), support vector machines (SVM), and artificial neural network (ANN)) predicted dimensions using 81 datapoints. The mean squared dimensional error (MSDE) between the measured and designed surface profiles was selected as an output for the dimensional accuracy. Support spacing, …

Accelerating Training Of Mlips Through Small-Cell Training, Jason A. Meziere, Yu Luo, Yi Xia, Laurent Karim Béland, Mark R. Daymond

Mechanical and Materials Engineering Faculty Publications and Presentations

While machine-learned interatomic potentials have become a mainstay for modeling materials, designing training sets that lead to robust potentials is challenging. Automated methods, such as active learning and on-the-fly learning, construct reliable training sets, but these processes can be resource-intensive. Current training approaches often use density functional theory (DFT) calculations that have the same cell size as the simulations that the potential is explicitly trained to model. Here, we demonstrate an easy-to-implement small-cell training protocol and use it to model the Zr-H system. This training leads to a potential that accurately predicts known stable Zr-H phases and reproduces the α-β …

Design And Characterization Of 2.4ghz Antennas For Wireless Sensor Applications, Luis Zogby Brennan

Dissertations and Theses

Wireless sensor networks benefit greatly from advances in circuit integration and miniaturization. However, antennas are bound to specific geometries both in terms of fundamental limits and their response to immediate surroundings. As such, various successful techniques for antenna miniaturization must still be carefully tested and characterized as part of the design process of a sensor system. The work presented here fully characterizes a number of miniaturized 2.4GHz antennas and successfully integrates designs into a sensor system using the highly integrated Single Chip Micro Mote (SCµM), which uses a crystal-less radio design resulting in the requirement of only a …

Modeling Leaf-Level Transpiration: Exploring The Consequences Of Assumed Saturated Vapor Pressure In Leaves, Danlyn L. Brennan

Dissertations and Theses

Understanding the dynamics of water transport through leaf intercellular airspaces (IAS) and its impact on transpiration is crucial for accurate predictions of plant water use and ecosystem response to changing climates. This study investigates the implications of assuming undersaturation of water vapor in the IAS for transpiration predictions and explores potential modifications to standard modeling approaches.

A dynamic 1D soil-plant-atmosphere continuum using a stomatal optimization model (SPAC-SOT) framework was used to simulate the response of tree species, P. edulis, to prolonged drought and varying environmental conditions. Comparisons between two model assumptions (saturated vs. undersaturated IAS) reveal notable differences in …

Modeling Yield Strength Of Austenitic Stainless Steel Welds Using Multiple Regression Analysis And Machine Learning, Sukil Park, Myeonghwan Choi, Dongyoon Kim, Cheolhee Kim, Namhyun Kang

Mechanical and Materials Engineering Faculty Publications and Presentations

Designing welding filler metals with low cracking susceptibility and high strength is essential in welding low-temperature base metals, such as austenitic stainless steel, which is widely utilized for various applications. A strength model for weld metals using austenitic stainless steel consumables has not yet been developed. In this study, such a model was successfully developed. Two types of models were developed and analyzed: conventional multiple regression and machinelearning- based models. The input variables for these models were the chemical composition and heat input per unit length. Multiple regression analysis utilized five statistically significant input variables at a significance level of …

Role Of Binder On Yield Strength Of Polycaprolactone/Dimethylsulfone Composites For Bio-Applications, Kyung-Eun Min, Jae-Won Jang, Sung Yi, Cheolhee Kim

Mechanical and Materials Engineering Faculty Publications and Presentations

Polycaprolactone (PCL) and dimethylsulfone (DMSO2) composites can tailor the properties of scaffold materials, allowing their use in bone tissue engineering. With an increase in DMSO2 content, the modulus of the material increases but not the yield strength. In order to increase yield strength, a binder was added. However, the optimization of the content and the mixing process of the binder were not optimized in the previous studies. In this study, gamma-methacryloxypropyltrimethoxysilane (A-174) was used as a binder to increase the strength of a composite. Four different mixing processes were employed based on the binder mixing sequence. The binders with content …

Optimally Distributed Receiver Placements Versus An Environmentally Aware Source: New England Shelf Break Acoustics Signals And Noise Experiment, William K. Stevens, Martin Siderius, Matthew J. Carrier, Drew Wendeborn

Electrical and Computer Engineering Faculty Publications and Presentations

This article describes the results of the Spring of 2021 New England Shelf Break Acoustics (NESBA) Signals and Noise experiment as they pertain to the optimization of a field of passive receivers versus an environmentally aware source with end-state goals. A discrete optimization has been designed and used to demonstrate providing an acoustic system operator with actionable guidance relating to optimally distributed receiver locations and depths and likely mean source detection times and associated uncertainties as a function of source and receiver levels of environmental awareness. The uncertainties considered here are those due to the imperfect spatial and temporal sensing …

Asymmetric Secondary Flows Above Geometrically Symmetric Surface Roughness, Wen Zhang, Xiang Yang, Xiaowei Zhu, Minping Wan, Shiyi Chen

Mechanical and Materials Engineering Faculty Publications and Presentations

The mean flow behaviour of a turbulent boundary layer over rough walls is expected to exhibit symmetries that govern the flow dynamics. In particular, when roughness elements are arranged in a spanwise symmetric manner, the mean flow above them should also exhibit spanwise symmetry. This symmetrical consideration has garnered substantial empirical support. We conduct direct numerical simulations (DNS) of flow over aligned cube arrays to test such symmetry considerations further. We vary the surface coverage density from 0.25 % to 6.25 %, and employ an averaging time of about 100 large-eddy turnover times, which is longer than the typical averaging …

Real-Time Length Sensing Of Braided Pneumatic Actuator Using Ir Time-Of-Flight Sensor, Luon B. Chung

Dissertations and Theses

Braided Pneumatic Actuators (BPAs) are used widely in robotics as artificial muscles because of their similar characteristics to biological muscles. When a BPA is pressurized, it expands in the radial direction and contracts in the longitudinal direction. The contraction generates a pulling force that is dependent on the input pressure and BPA length. Biological muscle also behaves in the same way where a pulling force is generated on a contraction. To control BPAs on a robot, the real-time pressure and length measurements are necessary. In this dissertation, I present a new method to measure length of BPAs in real-time using …

Multi-Agent Deep Reinforcement Learning For Radiation Localization, Benjamin Scott Totten

Dissertations and Theses

For the safety of both equipment and human life, it is important to identify the location of orphaned radioactive material as quickly and accurately as possible. There are many factors that make radiation localization a challenging task, such as low gamma radiation signal strength and the need to search in unknown environments without prior information. The inverse-square relationship between the intensity of radiation and the source location, the probabilistic nature of nuclear decay and gamma ray detection, and the pervasive presence of naturally occurring environmental radiation complicates localization tasks. The presence of obstructions in complex environments can further attenuate the …