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Feasibility of Blast Furnace Slag for Stabilizing Sulfate-Bearing Soil
Get PDFCalcium-based stabilizers are frequently used by The Oklahoma Department of
Transportation (ODOT) to enhance the strength and reduce the swelling potential of fine-grained
soils. However, these stabilizers can lead to adverse reactions in high sulfate-bearing soils which
are very common in Oklahoma. This study aimed to explore the efficacy of Ground Granulated
Blast Furnace Slag (GGBFS) as an additive for high sulfate-bearing soils, by comparing the
performance of GGBFS with other stabilizers like lime and Portland cement. In this research, the
primary evaluation tests were the unconfined compression test (UCT), to evaluate the strength
gained from the addition of the stabilizer, and the response to wetting test to study swelling
behavior. Two test soils were manufactured that contained approximately 20,000 ppm of sulfate
in the form of ground gypsum. In addition to the gypsum, Test Soil 1 was made with equal amounts
of fine sand and kaolinite and Test Soil 2 with equal amounts of fine sand and montmorillonite.
The results indicate that adding between 6% and 12% GGBFS by dry weight of soil
significantly increased the unconfined compressive strength (UCS), and the optimum amount of
GGBFS was 8% by dry weight of soil for both test soils. With the addition of the 8% GGBFS, the
UCS of Test Soil 1 and Test Soil 2 increased on average by 40 psi and 50 psi, respectively. These
values are close to the 50 psi increase desired for chemical stabilizers according to ODOT
requirements in OHD L-50. Additionally, when GGBFS was combined with a small amount of
either lime (0.5% or 1%) or PC (1% or 3%), a significant increase in the UCS was observed
compared to untreated samples or the ones only treated with one additive. The introduction of 1%
lime and 7% GGBFS increased the UCS around 200 psi for Test Soil 1 and 94 psi for Test Soil 2.
The combination of 3% PC and 7% exhibited the highest UCS increase, reaching 300 psi for Test
Soil 1 and 140 psi for Test Soil 2.
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The incorporation of GGBFS was observed to decrease the swelling behavior in both test
soils during a response-to-wetting test lasting approximately 15 days. The addition of 8% GGBFS
decreased the vertical swell from 3.3% to 1.9% for Test Soil 1 and from 5.2% to 0.3% for Test
Soil 2. Most notably, both soils exhibited no apparent swelling after the first day of the test, which
is in contrast to all other tests containing lime, PC or GGBFS mixed with lime or PC. When lime
or PC was used alone or in combination with GGBFS, swelling continued throughout the testing
period. In some cases for Test Soil 1, the final amount of swelling exceeded that of the test soil
alone, which is a clear indication of adverse reactions occurring due to addition of lime or PC.
That the GGBFS completely halted the swelling behavior while all the mixes containing lime or
PC continued to swell at the end of the nearly 15-day tests is an important finding. It suggests that
adverse reactions may not be avoidable even with small amounts of PC or lime mixed with
GGBFS.
The addition of GGBFS to the test soils had a minimal impact on the Liquid Limit, Plastic
Limit, and Plasticity Index of the test soils. However, for both test soils the Shrinkage Limit was
significantly reduced with the addition of 8% GGBFS. For Test Soil 1 and Test Soil 2, respectively,
the SL reduced from about 9.5% and 18% for the untreated soil to about 1.5% and 6% for the
treated soil
High-resolution Satellite-based Modeling and Assessment of Gross Primary Production over Agroecosystems for Precision Agriculture
No full textSustainable agriculture stands at the forefront of the global sustainable development strategic goals (SDGs), targeting the needs of increasing production to meet the growing food demands and to ensure profitability for producers, while maintaining affordability for consumers. Central to this endeavor is the precise monitoring of crop health, growth, vegetation productivity, water usage, and yield forecasting—each vital for enhancing productivity and optimizing resource allocation. Yet, addressing this strategic SDG is challenging given the big gap between industry applications and academic research: commercial methods often lack scientific rigor, while academic models are marred by complex, impractical parametrizations.
This dissertation bridges this gap by providing a potential solution with strong scientific rigor and high scalability potential for easy commercial applicability. Initially, this work examined the biophysical components and links that explain the changes in Gross Primary Production (GPP) and Transpiration(T)—key indicators of vegetation growth, vegetation carbon capture, crop productivity, and water use in crop ecosystems. The analysis of these variables highlighted their critical role in agroecosystems and the potential benefits of using the Vegetation Photosynthesis Model (VPM) and Vegetation Transpiration Model (VTM) to estimate GPP and T respectively. The study evaluated the efficacy of high spatial resolution (HSR) satellite imagery compared to moderate spatial resolution (MSR) data as input in estimating GPP and T across various high-value crops, underscoring the potential of VPM and VTM in precision agriculture. Then, the research investigated the VPM_GPP estimates as a viable proxy for yield forecasting, addressing the challenges inherent in crop models and the limitations of remote sensing-based approaches. Through extensive testing across multiple agroecosystems, the findings revealed the VPM model's capacity to deliver accurate, scalable, and actionable insights into crop monitoring, field productivity variability, and yield predictions. Finally, the dissertation extended beyond theoretical models to practical application, engaging with over 170 industry stakeholders to align the technology evaluated in this document with their needs. This interaction ensures that the proposed solution is not only scientifically sound but also of tangible value to the agricultural sector, facilitating the transition of academic advancements into commercial viability. In essence, this work not only contributes to the academic discourse on sustainable agriculture, reducing the gap in knowledge about a robust method to estimate GPP_VPM-HSR, T_VTM, and a simpler accurate approach for yield forecasting, but also paves the way for its real-world implementation, fostering agricultural resilience and food security
Unveiling Melodies in Shadows: An Analysis of Swedish Female Composer Amanda Maier’s Sonata for Violin and Piano in B Minor
Get PDFAmanda Maier (1853−1894), a pioneering Swedish violinist and composer of the late nineteenth century, holds a unique place in music history as the first-ever female music director in Sweden. Despite her significant achievements, her compositions have remained relatively unknown. Therefore, the document aims to illuminate Amanda Maier's violin works, focusing on investigating her violin sonata in terms of violin performance and pedagogy. Specifically, the study offers insights into the performance techniques employed and provides other pertinent pedagogical suggestions for each movement. The document features an introductory chapter and a review of the historical context of Maier's life and the violin sonata. Subsequent chapters shift the focus to performance practice and pedagogical suggestions with theoretical analysis. One distinctive feature of the study is the inclusion of practice exercises composed originally by the author, tailored specifically to the techniques found in the sonata. These exercises aid practitioners in incorporating Maier's violin sonata into their program. The study assists violinists in diversifying their performance and teaching literature. It seeks to inspire renewed appreciation for Amanda Maier's artistic legacy because it is important to recognize the remarkable contributions of women in the classical music industry, and Amanda Maier, an underrepresented composer, exemplifies this. The document not only contributes to music research but also enhances pedagogical practices, fostering a more inclusive and equitable environment for female composers in the classical music world
Application of seismic attributes and unsupervised machine learning methods for identification of hidden faults in basement and carbonate rocks
Get PDFSeismic fault interpretation is a critical task for any type of energy industry and correct fault mapping can be crucial for the success of a project. Common geometric seismic attributes such as coherence and curvature are routinely employed to enhance fault visualization in seismic data, but they can show limitations for sub-seismic faulting. Two projects are presented here showing how recently introduced geometric seismic attributes, such as total aberrancy, and unsupervised machine learning methods, such as self-organizing maps (SOM) and generative topographic mapping (GTM), can be applied for enhancing fault visualization.
The first project focuses on an area with potential for CO2 storage in the carbonates of the Duperow Formation, northern Montana. In this study, we compared broadband and multispectral coherence, curvature, and aberrancy, and we compared SOM and GTM techniques when including and excluding aberrancy attributes. Our results showed that integrating aberrancy attributes during the multiattribute analysis and the machine learning steps considerably enhance the visualization of lineaments with strikes similar to those of fracture sets seen only with well log data and missed by the conventional geometric seismic attributes and the ML scenarios excluding aberrancy attributes.
The second project is related to wastewater injection and induced seismicity in basement-rooted faults in northcentral Oklahoma. Here, different geometric seismic attributes were analyzed and integrated using unsupervised machine learning to identify potential basement-rooted faults and strike-slip-related structures. The machine learning results not only confirmed the existence of NE-SW faults that extend from the basement upward into the sedimentary section and that correlated with earthquake data but also the potential existence of other NE-SW structurally controlled features of anticlinorium shape
Development and assessment of constrained reinforcement learning-based controller for building demand response
Get PDFRecent advancements in model-free control strategies, such as reinforcement learning (RL) have led to more practical and scalable solutions for building energy system controls These strategies do not require complex models of building dynamics and rely exclusively on data to learn the control policy. Applications of these techniques in heating, ventilation and air-conditioning (HVAC) systems are being studied under different operational scenarios, including demand response programs. Conventional (unconstrained) reinforcement learning controllers often address indoor comfort constraints by incorporating a comfort violation
penalty in the reward function. While this approach can result in good performance in terms of energy cost, it often leads to significant constraint violations when a small penalty factor is used. On the other hand, effective enforcement of constraints can be achieved, but at the cost of economic performance degradation. Hence, a clear trade-off between economic
performance and constraint satisfaction poses a challenge to overcome. Motivated by this challenge, this thesis presents a constrained RL-based control strategy for building demand response. The proposed strategy handles the constraints explicitly, avoiding the use of arbitrarily set penalty factors that can significantly impact control performance. To demonstrate
its efficacy, simulation tests of the proposed strategy, as well as baseline model predictive controllers (MPC) and conventional (unconstrained) policy optimization methods, were conducted. The simulation tests showed that the constrained RL strategy achieved utility cost savings up to 16.1%, similar to the MPC baselines, without requiring any model of the building
and with minimum constraint violation. In contrast, the unconstrained RL controllers led to either high utility costs or constraint violations, depending on the penalty factor setting
Dwarf galaxies in N-body+SPH simulations
Get PDFI present a study of various dwarf galaxies from N-body+SPH simulations in Lambda Cold Dark Matter cosmology. While the science results of this thesis pertain to dwarf galaxies of varying classifications and environments, I maintain a focus on how variations in definition and 3D-orientation affect these results. In a study of ultra-diffuse galaxies (UDGs), I show that those found in isolation are morphologically distinct from their non-UDG counterparts, a tracer of their unique formation channel through early, high-spin mergers. Randomly orienting and projecting our galaxies onto the 2D plane, however, removes any distinction between isolated UDGs and non-UDGs, suggesting that this difference will be difficult to detect with observations. Additionally, comparing various UDG definitions used in current literature shows that the number of UDGs identified in our simulations can vary drastically based on one's choice of definition, a result that is further complicated when considering various 3D orientations. More permissive definitions, that result in a large number of UDGs, tend to homogenize the UDG and non-UDG populations, erasing any observable distinction that may have existed.
I also present a study of study of Milky Way like galaxies and their satellite distributions, with a focus on comparing our results to the Satellites Around Galactic Analogs (SAGA) and Exploration of Local VolumE Satellites (ELVES) surveys. I show that host mass is a driving factor in both satellite accumulation and satellite quenched fraction, while host environment may have significant impact in extreme isolation. This impact can vary in strength depending on the criteria one uses to identify both the Milky Way analogs and their satellites. While the SAGA and ELVES surveys show conflicting results in regards to quenched fraction, I show that this discrepancy originates in low-mass satellites, as restricting quenching analysis to high mass satellites shows good agreement between our simulation and both observational surveys.
Finally, I present a study of morphology measurements for dwarfs in high-resolution zoom-in simulations. In comparing the observation-based method of isophote fitting to the simulation-based method of shape tensor calculation, I show that isophote fitting tends to imply more elongated shapes than shape tensor calculations, and this discrepancy is stronger at more edge-on orientations. I also implement observation-based methods of 3D shape inference presented in current literature to see how they compare to shape tensor calculation. I show that while these inference methods well recover our sample space and our in good qualitative agreement, there is some decent scatter on a galaxy-to-galaxy basis. All methods, however, independently imply that our simulations contain an oblate, high-mass population of satellite galaxies
Transformed Path Integral Based Approaches for Stochastic Dynamical Systems: Prediction, Filtering, and Optimal Control
Get PDFThe study of stochastic systems, their characterization, prediction, and control are of great importance to many fields in science and engineering. This often involves obtaining accurate estimates of quantities of interest such as the system state distribution and/or the expected cost in nonlinear dynamical systems subjected to random forces. The computational prediction and control of such systems are often challenging (and involve large computational costs) due to the presence of nonlinearities, model and measurement uncertainties. Novel path integral–based frameworks for efficient solutions to problems in prediction, nonlinear filtering, and optimal control of stochastic dynamical systems are presented to address several key challenges. The presented frameworks are as follows: (1) the transformed path integral (TPI) approach for solution of the Fokker-Planck equation in stochastic dynamical systems with a full rank diffusion coefficient matrix, (2) the generalized transformed path integral (GTPI) approach—a non-trivial extension of the TPI to stochastic dynamical systems with rank deficient diffusion coefficient matrices, (3) the generalized transformed path integral filter (GTPIF) for solution of nonlinear filtering problems, and (4) the generalized transformed path integral control (GTPIC) for solution of a large class of stochastic optimal control problems are presented. The proposed frameworks are based on the underlying short-time propagators and dynamic transformations of the state variables that ensure the appropriate distributions in the transformed space (state distributions in TPI and GTPI; and corresponding conditional distributions in GTPIF and GTPIC) always have zero mean and identity covariance. In systems where the dynamics are linear with respect to the state variables and initial distribution is Gaussian, the appropriate distributions in the transformed space remain invariant with a standard normal distribution as expected. The frameworks thus allow for the underlying distributions necessary for evaluating the quantities of interest to be accurately represented and evolved in a transformed computational domain. Compared to conventional fixed grid approaches and Monte-Carlo simulations, the challenges in dynamical systems with large drift, diffusion, and concentration of PDF can be addressed more efficiently using the proposed frameworks. In addition, straightforward error bounds for the underlying distributions in the transformed space can be established via Chebyshev's inequality
GPS disciplined RFSoC synchronization, timing, and performance characterization in bistatic radar systems
Get PDFDistributed radar geometries offer multiple advantages over monostatic pulse-Doppler radar, but synchronizing frequency and timing for transmitting and receiving nodes in a distributed system is required to more accurately detect range and Doppler frequency. A GPS-disciplined bistatic radar synchronization system design running on an RF System-on-Chip (RFSoC) transceiver and GPS-disciplined precision timing reference is detailed, and the features and limitations of these two individual systems are examined. To better understand error tolerances of relevant signals produced by the timing reference used in the synchronization system, in-depth analyses of frequency drift, timing drift, and jitter are conducted and described both with and without GPS disciplining. Custom-designed FPGA IP designed to implement transmit and receive pulse Doppler radar functionality in the RFSoC-based system is introduced
Teaching Research: Mind Mapping & Pathfinding Techniques
No full textToday's students need advanced information/data seeking and sorting skills to support their personal and professional writing and research. Mind mapping and pathfinding techniques help students conduct balanced analysis and reduce the tendency to cherry pick sources. This presentation will focus on teaching best practices and provide a resource list of free and low-cost tools for students and librarians.N
Goldfish in a bowl: Teaching privacy literacy to undergraduates
Get PDFFree coffee in exchange for your personal data. Wifi that tracks your movements across campus. Apps that mine data across your device. For college students today, giving up personal data is simply the cost of being online, both for personal and educational purposes. And while students may care about maintaining their online privacy, many do not have the tools to practice good data privacy habits, because they simply have not been taught them. In this presentation, we will explore what privacy literacy is, why librarians are perfectly poised to offer data privacy instruction, and look at examples of data privacy lessons that the presenter has used in their own credit-bearing information literacy course.N