RSS 1.0Theses & Dissertations
Browse by
Collections in this community
Recent Submissions
-
Failure conditions and triggers of the Achoma landslide, central Andes region, Arequipa PeruThe Colca Valley has historically been subject to a variety of geological hazards such as landslides, rock falls, and debris flows. On June 18, 2020, a rotational landslide occurred near the community of Achoma in the Colca Valley. The event destroyed agricultural land, impacting the finances of many families, and the displaced material crossed the Colca River and created a dam that increased the risk of flooding for the towns upstream. To estimate the critical groundwater conditions that caused the Achoma landslide, limit equilibrium and finite element analyses were completed. Also, forward modeling was completed to analyze the hazard posed by further movement of the scarp. The research relied on two-dimensional models that required several kinds of information to provide valid results. At the Achoma site, the information on the strength of in situ soil and rock materials and the depth and distribution of groundwater is extremely limited and the access to the zone is restricted and dangerous. Therefore, remote sensing data was used to inform these parameters, accompanied by probabilistic analysis. The use of satellite images, drone images, and three-dimensional models was also vital to complete the research. The slope stability was dependent on lacustrine sediments composed of clayey silts interbedded with sandstones and conglomerates of the Colca formation. The trigger of the landslide was a combination of natural and anthropogenic factors which produced changes in the groundwater levels. The principal causes identified are rainfall, extensive irrigation activity on the landslide area, and potential leakage from an irrigation project conformed by a canal and tunnel uphill from the landslide. Considering the results, we observed differences in the influence of the different triggering mechanisms. The impact of the rainfall was low to moderate because almost no rain occurred two months before the landslide. The agricultural activity in the area has a moderate influence because the irrigation could result in excess water percolation to the perched water table. And finally, the effect of the Majes-Siguas canal and tunnel observed was moderate-high. Models results show that the leakage from the tunnel is more likely than leakage from the canal.
-
Elucidating protein-protein interactions that regulate the structure of bacterial protein assemblies using multiscale modeling methodsSelf-assembling protein structures have a wide range of functions in many bacterial species. In the spore-forming bacterial pathogen Bacillus anthracis, a protective paracrystalline monolayer composed of the surface layer protein Sap is an important virulence factor that enables the spread of the disease anthrax. In many bacterial species such as Haliangium ochraceum, self-assembling bacterial microcompartments compartmentalize enzymes that aid in the energy production and metabolism of the organism. The understanding of the mechanisms of assembly of both complexes has important implications for therapeutic and metabolic engineering. In vivo, nanobody-mediated disruption of the B. anthracis surface layer attenuated bacterial growth and prevented lethality. Revealing the mechanism of self-assembly may provide insight into the design of protein therapeutics with increased affinity and effectiveness. Metabolic engineers are also interested in repurposing bacterial microcompartments for non-native functions. Identifying molecular driving forces and assembly conditions that direct the morphology and cargo of bacterial microcompartment shells can guide the engineering of novel microcompartment shells to encapsulate non-endogenous enzymes and expand the range of metabolic activity. Here, I propose the use of multiscale modelling to highlight the effect of protein flexibility on the exposure of key protein-protein interfaces in these self-assembling proteins. By generating and validating an atomistic model of the Sap protein lattice, I aim to identify the interactions responsible for lattice assembly to guide future nanobody design. I also provide a framework for the coarse-grained modeling of a hexameric H. ochraceum microcompartment subunit. The ability of this model to accurately capture the morphologies observed in in vivo and in vitro experiments will enable the in silico modeling of other microcompartment subunits that can be used collectively to explore the morphological landscape of microcompartment assemblies, further leading to the engineering of microcompartments that expand the range of microbial metabolism.
-
Gallium extraction from zinc plant residues by chlorination roastingThe demand for semiconductors and the renewable energy transition has increased the demand for gallium, indium, and germanium. The US Department of Energy considers these elements critical due to their scarcity, lack of substitutes, and import dependence. Some sphalerite concentrates contain trace amounts of Ga, In, and Ge. During the processing of sphalerite, Ga, In, and Ge are concentrated during leaching. However, Ga, In, and Ge co-precipitate with the iron sulfates and other impurities during the iron removal stage. These zinc processing residues are promising sources for Ga, In, and Ge; however, there are technological limitations to recovering these elements. Chlorine metallurgy has been studied to recover metals from complex ores, treat waste, or recover critical elements in waste streams. It has been evaluated for the recovery of valuable material from sulfides, oxides, and silicate ores. Moreover, chlorine metallurgy offers several advantages, such as opening ores, selectivity, regeneration of the chlorinating agents, and process versatility. Thus, the following study presents chlorination roasting as a possible method to recover gallium from domestic zinc processing residues. The study consists of chlorination roasting of two different zinc processing residues for the recovery of gallium. The study discusses the effects of temperature and chlorine gas concentration on the extraction via chloride fuming of Ga, Fe, Pb, and Zn. Gallium extractions of 50%, 60 %, and 80 % were achieved from zinc processing residues. Moreover, zinc extractions of 60% and 80% were also achieved. Selective volatilization of gallium, germanium, and indium chlorides was studied and found to be a promising approach for refining. A preliminary flow sheet for gallium recovery from zinc processing residues via chlorination roasting was identified and evaluated. The preliminary techno-economic analysis found that the gallium grade in the zinc processing residues is crucial for an economically feasible process. It was determined that chlorination roasting is a technologically feasible process for the recovery of gallium.
-
Integrating full-bore formation micro-imager (FMI) data for Niobrara reservoir characterization, Postle area, Wattenberg field, Colorado, USAThe Ancestral Rocky Mountain orogeny formed the paleo-Denver Basin approximately 300 million years ago and was followed by the Laramide orogeny that created the present-day Denver Basin. The Western Interior Basin existed between the orogenic events during the Cretaceous. Deposition of the Niobrara Formation in the Western Interior occurred when interactions between the warm paleo-Gulf of Mexico waters flowed northward and the cooler currents from the Artic flowed southward. The Niobrara Formation lithologies represent periods of fluctuating sea-level conditions resulting in the deposition of chalks, marls, sandstones, and shale cycles. The Wattenberg Field is in the Denver-Julesburg (DJ) Basin in northeast Colorado, north of Denver across the synclinal axis of the Denver Basin and covers approximately 81 townships. The Wattenberg Field development and production started in 1970, with the majority of production coming from vertical drilling of the Lower Cretaceous J Sandstone. The Upper Cretaceous Niobrara and Codell Formations became important producers in the 1980s. In addition, production in the Wattenberg Field is found in the Dakota, D Sandstone, Greenhorn, Terry, and Hygiene Sandstones. Continuous hydrocarbon accumulations are common throughout the Field. The Wattenberg gas Field is one of the largest natural gas fields in the United States, with resource estimates from the Niobrara being approximately 3-4 billion barrels equivalent (BBOE). This study will include a detailed fracture characterization of the Niobrara Formation in the Postle area of the Wattenberg Field, which will help characterize both natural and induced fractures within the Niobrara Formation. Formation micro-resistivity image (FMI) log interpretation indicates a strong orientation preference created through hydraulic stimulation and suggests that the present-day stress orientation is not a result of reorientation due to production and stimulations. Interpretation of the image log data can establish the spatial geometry of the natural and induced fractures within the wells and will help characterize the fractures that help produce hydrocarbons by hydraulic stimulation.
-
Effect of defects and a build pause on fatigue life of additively manufactured 316L stainless steelBuild pauses can occur during metal additive manufacturing (AM) with laser beam powder bed fusion (PBF-LB) for a variety of reasons such as power outage, insufficient gas flow, or sensor failure. It is economically desirable to continue a build after the issue is resolved. The effect on part quality, namely microstructure, surface roughness, geometric features, and the impact on the fatigue performance is not well understood. This study considers parts fabricated with a 2-hour build pause in the center of the gauge section. Comparisons of the dimensions, as-printed surface features, microstructure, and fatigue performance are determined. Geometric deviation from part shift during the build pause is significant, however other flaws inherent to AM can also dominate failure depending on severity. A stress intensity factor approach is utilized to determine the influence that the geometric variation and the individual flaws had on the fatigue life. A model is developed to predict and determine part performance after a build pause. Understanding the effect of mechanical changes and geometric shifts from a build pause can help reduce scrap from unintended build interruptions.
-
Full-waveform inversion of time-lapse seismic data using physics-based and data-driven techniquesTime-lapse (4D) full-waveform inversion (FWI) is an advanced seismic technique that can enable accurate estimation of changes in the subsurface properties, such as fluid saturation or reservoir depletion, by utilizing amplitude and phase information in seismic data. However, most existing 4D FWI research is limited to isotropic and, often, acoustic media, which hinders its application to realistic subsurface models. In this thesis, I develop an efficient 4D FWI algorithm for elastic transversely isotropic media with a vertical (VTI) and tilted (TTI) symmetry axis. In addition, I employ a ``source-independent" technique to mitigate the influence of errors in the source wavelet on the results of time-lapse FWI. Furthermore, the thesis presents machine-learning techniques with uncertainty quantification to efficiently perform real-time monitoring with high spatial resolution. First, I extend the methodology of time-lapse FWI to elastic VTI media. The algorithm is tested on multicomponent and pressure data using three common time-lapse strategies: the parallel-difference (PD), sequential-difference (SD), and double-difference (DD) techniques. The multiscale approach is adopted to mitigate cycle-skipping. Synthetic tests show that the proposed methodology can reconstruct localized time-lapse parameter variations with sufficient spatial resolution. The DD strategy produces the most accurate results for clean and repeatable time-lapse data because it directly inverts the data difference for the parameter changes. VTI algorithms become inadequate in the presence of an even moderate symmetry-axis tilt. Therefore, next the time-lapse FWI methodology is extended to 2D tilted TI models. The symmetry-axis tilt is incorporated into the modeling code and computation of the inversion gradients by rotating the stiffness tensor using the Bond transformation. Comparison between the TTI and VTI algorithms confirms that incorporating tilt improves the accuracy of the inverted medium parameters, especially when the reservoir is located in a dipping layer. In addition, I discuss the influence of several common nonrepeatability issues on the time-lapse inversion results for TTI media. FWI requires an accurate estimate of the source wavelet, which is both time consuming and often challenging for field-data applications. The so-called ``source-independent'' (SI) technique is designed to reduce the influence of the employed source wavelet on the FWI results. Therefore, I incorporate the convolution-based SI technique into the developed 4D FWI algorithm for both VTI and TTI models. The SI method substantially reduces the dependence of the estimated parameters on the accuracy of the source wavelet and guides the inversion toward the global minimum of the objective function even for a strongly distorted wavelet and noisy data. Then the developed 4D FWI methodology that incorporates the SI technique is applied to the time-lapse streamer data from Pyrenees oil/gas field in offshore Australia. Despite the pronounced nonrepeatability in the baseline and monitor surveys, the developed algorithm successfully reconstructs the velocity variations in the reservoir caused by hydrocarbon production. The case study demonstrates the importance of accounting for anisotropy and elasticity in time-lapse inversion and confirms the effectiveness of the SI technique when the source wavelet is distorted. To alleviate the ill-posedness and high computational cost of FWI, I propose an efficient ``hybrid'' time-lapse workflow that combines physics-based FWI and data-driven machine-learning (ML) inversion. The scarcity of the available training data is addressed by developing a new data-generation technique that operates with physics constraints. The proposed approach is validated on a synthetic CO2-sequestration model based on the Kimberlina reservoir in California. A large volume of high-quality and physically realistic training data, generated by the algorithm, proves to be critically and efficiently important in accurately characterizing the CO2 movement in the reservoir. The deterministic neural network described above, however, yields only one prediction for a certain input, which may not properly reflect the distribution of the entire testing data, especially when those data are out-of-distribution. To estimate the entire distribution of the target variable along with the prediction accuracy, I incorporate the Simultaneous Quantile Regression method into the developed convolutional neural network. Testing on the Kimberlina data demonstrates the accuracy of the obtained uncertainty estimates, even if the testing data are distorted due to problems in the field-data acquisition. In addition, the proposed novel data-augmentation method can further improve the spatial resolution of the determined time-lapse velocity field and reduce the prediction error.
-
Open-pit mine planning with operational constraintsOpen-pit mines must be designed to develop the Earth’s natural resources in the most responsible, sustainable, and economic way. Traditional mine planning optimization methods do not consider operational constraints; such as minimum mining width or minimum pushback width constraints, and often do not generate realistic, actionable designs. This dissertation develops techniques to incorporate operational constraints into open-pit mine planning which allows for engineers to more accurately convert mineral resources into mineral reserves and better evaluate the economic viability of open-pit mining projects. A major practical challenge is that the resulting mathematical models are very large, with potentially hundreds of millions of variables and constraints. Addressing this challenge and delivering tools which are usable on real-world 3D datasets requires a theoretically motivated and computationally grounded approach. The first contribution of this dissertation is an efficient implementation of the pseudoflow algorithm for the well known ultimate pit problem. Modest theoretical improvements and practical computational improvements combine to create a fast and efficient open source ultimate pit optimizer, called “MineFlow,” which is more performant than all evaluated commercial alternatives. A model with sixteen million blocks which takes over three minutes to solve with a commercial ultimate pit optimizer is solved in nine seconds with this implementation. The second contribution is a formulation and methodology for the ultimate pit problem with minimum mining width constraints. These operational constraints restrict the shape of the ultimate pit in order to provide suitably large operating areas which can accommodate the large machinery used in open-pit mining. This problem is shown to be NP-complete and several optimization approaches are developed in order to compute high quality results for large block models in a reasonable amount of time. The two most effective approaches use Lagrangian relaxation and the Bienstock-Zuckerberg algorithm which are modified for this problem. Moreover, the formulation is extended to open pit direct block scheduling problems with operational constraints and solved using a newly developed method based on the Bienstock-Zuckerberg algorithm. This approach is applicable to large, realistic, open-pit planning problems that span multiple time periods and multiple possible destinations for each block.
-
Instigating buoyancy driven convection to improve membrane distillation performanceMembrane distillation is a thermally driven desalination process that is capable of treating complicated wastewaters with high concentrations of dissolved solids. However, temperature polarization and concentration polarization reduce its distillate production and impede its industrial adoption. Surprisingly, no prior work has considered that temperature and concentration polarization both increase the feed density near the membrane. In this exploratory study, we explore whether these differences in local feed density can instigate buoyancy driven convection in the feed flow and mitigate polarization. We also explore whether we can strengthen this convection by actively heating the feed channel in a direct contact membrane distillation (DCMD) system. For that purpose, we develop several actively heated prototypes and perform a series of experiments to explore how active heating affects distillate production. The prototypes are tested in multiple orientations to determine if buoyancy driven convection is truly taking place. The tests are also performed at multiple feed flowrates and feed concentrations to explore how temperature and concentration polarization affect the development of buoyancy driven convection. Overall, our experiments suggest that, with the cell oriented properly, buoyancy driven convection can be harnessed to significantly improve distillate production. For all flowrates tested, we observed a linear increase in distillate flux with increasing wall heating. The impact of wall heating also increases as the flowrate decreases.
-
RNN seismic velocity model building: improving generalization using a frequency-stepping approach and hybrid training dataData-driven artificial neural networks (ANNs) demonstrably offer a number of advantages over conventional deterministic methods in a wide range of geophysical problems. For seismic velocity model building, judiciously trained ANNs lead to the possibility of estimating high-resolution subsurface velocity models at a low computational cost. However, a significant challenge of ANNs is training generalization, which is the ability of an ANN to apply the learning from the training process to evaluate test data not previously encountered during the training process. In the context of velocity model building, this means learning the relationship between velocity models and the corresponding seismic data from a set of training data, and then using acquired seismic data to accurately estimate unknown velocity models. While generalizing to testing models with structures similar to those found in the training data has become a manageable task as evidenced in the recent literature, extending generalization to more realistic scenarios where testing models may exhibit drastically different velocity structures and/or distributions than those in the training data set remains an important and ongoing research challenge. To address this issue, this thesis develops and present the applications of a multi-scale approach inspired by physics-based full-waveform inversion that uses recurrent neural networks to invert frequency-domain seismic data using a frequency-stepping scheme. The input data consist of a sequence of seismic frequency slices that are fed to the network progressively from the lowest available to the highest usable in the data. I combine this approach with a hybrid training approach that merges background velocity gradient models with purely geometrical and geologically realistic model structures. This combination increases the range of spatial wavenumbers as wells as the variability of geological structures present in the training data. I demonstrate the potential for improved generalization by comparing the model estimates results from two trained networks: one using a hybrid set of models, the other with only geological models. I test the two networks using subsets of the community BP2004 benchmark model with complex salt structures fully absent from the models used in the training process. Qualitative analysis shows that models recovered using hybrid training data are significantly more accurate than those recovered using geological training data alone, with arbitrarily shaped salt bodies being accurately delineated by the trained hybrid network. In addition, I demonstrate through a quantitative SSIM metric analysis that the developed RNN extends the range of structures recoverable by the trained ANN. The developed approach illustrates the potential of neural networks to learn the seismic velocity model building problem at a general level from a representative set of training models, and opens the way for more research into improving the design of non-geological training data to further improve network generalization.
-
Comparison of respirable dust characteristics from full-scale cutting tests with conical picks at three stages of wearIn environments where mechanical excavation systems break rock, airborne rock dust is generated and could pose respiratory health threats to workers. These environments include mining and civil applications with roadheaders, continuous miners, or similar machines used in underground operations. Therefore, this study aims to compare the characteristics of rock dust generated by conical pick cutters at various wear conditions. With supplemental experiments, the results can aid future evaluations of proper bit management, dust suppression, and control systems. The work conducted under this study included full-scale laboratory cutting of concrete, limestone, and sandstone samples. Three symmetrically worn conical picks cut each sample at a new, moderately worn, and fully worn stage of wear. Equipment collected airborne dust samples and fines during and after cutting for qualitative and quantitative characteristics. Analysis of collected dust revealed the dust's characteristics, including the concentrations, silica contents, particle size distributions, and particle shapes. Findings indicated that the dust generated in the cutting process increases as the pick tip wears. With a superimposed circle at the pick tip, further analyses show that as the tip radius increased by one millimeter during excavation, the dust generation at the pick increased by an average of 50 mg/m3 for all the samples. Furthermore, analysis of the cutting forces and specific energy of cutting (the amount of energy used to excavate a unit volume of rock) show that as the pick radius (mm) increases, the concentration (mg/m3) of dust per specific energy (kW-hr/m3) increases linearly. The correlation between dust and specific energy showed an average increase of 3.0 [(mg/m3) / (kW-hr/m3)] / mm of tip radiusin the rock samples tested. In terms of silica, the silica content is a function of mineralogy, and all the rock types contained traces of quartz. The airborne respirable particle size distributions insignificantly shifted between pick wear. The fines size distributions slightly increased as the pick wear level increased. However, the differences are also deemed insignificant and, therefore, are negligible. Finally, the pick wear does not influence change in the particle shapes. All the picks consistently generated suspended respirable particles with similar particle shapes that are slightly oval with mostly smooth edges.
-
Aging effects on sheared edge formabilityAdvanced high strength steels (AHSS) combine high strength with good formability, making them an appealing class of materials for the automotive industry. Despite good global formability, many AHSS grades have poor sheared edge formability. Additionally, previous studies have documented that sheared edge ductility, measured via hole expansion ratio (HER), changes with room temperature aging time between blanking and testing for several AHSS grades. The present study designed experiments to investigate strain aging and hydrogen embrittlement as possible mechanisms. Five steel grades were tested to evaluate effects of microstructure, tensile properties, and aging temperature on aging sensitivity of sheared edge formability. These steels include dual phase (DP), complex phase (CP), bake hardening (BH), cold rolled martensitic (CRM), and transformation induced plasticity (TRIP) aided bainitic ferrite (TBF) grades. Edge formability was measured via HER, with aging times of 0.5-240 h between punching and testing. Hydrogen embrittlement and strain aging effects were examined by: applying elevated temperature treatments during and before aging, measuring hydrogen content of different conditions, masking the sheared edge after blanking, and testing burr up and burr down. To investigate Zn coating effects on aging sensitivity, aging effects were measured for DP 800 specimens produced in an annealing/galvanizing simulator with the same thermal history and different coating and atmosphere conditions. No consistent microstructure or tensile property effects on HER or aging sensitivity were found. Of the grades in the comparative study, the CP and CRM grades experienced aging effects on sheared edge formability. The HER of both grades increased in the first several hours of aging, followed by a decrease out to 240 h of aging. Aging temperature impacted the rate of HER loss significantly for the CP grade, but had less of an effect on the CRM grade. A heat treatment which elicited bake hardening in tensile specimens of the BH grade did not change the HER of that grade, suggesting strain aging in the sheared edge does not necessarily reduce HER. The addition of a Zn coating incited aging sensitivity in the DP 800 grade, but a Zn coating was not necessary for a steel to experience aging effects, as exemplified by the CRM grade, which likely has the highest hydrogen embrittlement susceptibility of the grades tested. Overall, the results suggest internal hydrogen is responsible for aging effects.
-
Empowering women in low-income communities in Colombia by extracting values from construction and demolition waste through recycling concreteWhile the construction industry is an important source of income for many countries and essential to dealing with population growth and urban expansion, it is contributing to many environmental and social issues. Due to its observed contributions to pollution, carbon dioxide emissions, energy consumption, natural resource depletion, and waste generation globally, the construction sector is a well-known major contributor to environmental degradation. Although many countries are developing strategies to manage construction and demolition waste (C&DW), oftentimes, these strategies are limited to final disposal policies which ignore the potential positive impacts utilizing this waste could have on environmental, social, cultural, economic, and political levels (Mendez-Fajardo, 2011). In Latin America, significant advances in managing C&DW have been lacking. Colombia is one such country that is generating massive amounts of C&DW yet has not made significant advances in managing it until very recently. When developing solutions to treat and manage C&DW, as some groups are disproportionately exposed to and affected by adverse environmental effects due to social and economic factors, such as gender, class, race, etc., special consideration should be given to those more vulnerable to the effects of environmental degradation. Utilizing a participatory, mixed methods—primarily qualitative—approach to work with women in a low-income community in Colombia to understand how value can be extracted from C&DW safely, determine how these recycled materials can be made useful, and how to do all this in a way that enhances social cohesion in their communities, this investigation aims to connect C&DW management, empowerment, and sustainable community development by utilizing a participatory, community-based workshop. Overall, this study demonstrates the importance of using community-based participatory research and mixed methods approaches to understand how empowerment is contextually situated to ensure projects are truly empowering to the groups they intend to serve.
-
Effect of neutron irradiation damage on the microstructural and mechanical properties of precipitation hardened Inconel 718 produced by laser-based additive manufacturing techniquesThe unique cellular structure of Additively Manufactured (AM) alloys produced by laser-based processes may enable the production of parts with improved radiation resistance for nuclear applications. It may be possible to manipulate the microstructure of AM parts during production to optimize features that resist neutron radiation damage in the resulting material. The ability to produce customized microstructures through additive manufacturing may allow the development of parts with enhanced irradiation damage tolerance, which would facilitate the design and implementation of advanced nuclear reactors. This thesis project explores the possibility of producing neutron irradiation damage resistant materials by laser-based additive manufacturing processes and contribute to the validation of precipitation hardened Inconel 718, Inconel 625, and stainless steel 316L produced by laser-based additive manufacturing techniques for use in nuclear reactor environments. This includes characterizing the microstructural and mechanical properties of Inconel 718, Inconel 625, and stainless steel 316L produced by Laser Powder Bed Fusion (LPBF) and Laser Powder Feed (LPF), as well as evaluating the impact of varying doses of neutron radiation on the mechanical properties of these materials. This work is an important first step in characterizing characterize the post-irradiation physical properties of these AM alloys to determine the viability and possible advantages of these materials in neutron irradiation environments.
-
Comprehensive evaluation of advanced water treatment processes for the treatment and desalination of produced waterWith increasing population, industrial development, and global temperatures, water scarcity is the biggest challenge the world is facing today. As a result, the importance of the research, development, and implementation of advanced water treatment processes to treat waste streams previously considered too difficult and expensive for beneficial reuse has never been higher, with an emphasis on waste streams affecting the water-food-energy nexus. One such waste stream is the wastewater generated from unconventional oil and gas operations, particularly waste from hydraulic fracturing (HF). This water, termed produced water (PW), is currently being managed mostly through disposal by deep-well injection as this is the least expensive way to deal with PW. Therefore, it is critical to develop treatment processes that are effective in removing contaminants while reducing treatment costs closer to the costs of disposal. Biological wastewater treatment of conventional waste streams (i.e., municipal wastewater) is an effective pretreatment strategy for the removal of organic compounds before membrane desalination (e.g., reverse-osmosis (RO)) to mitigate biofouling. Unfortunately, PW can limit biological pretreatment and desalination options due to the high concentrations of total dissolved solids (TDS) which can devastate a microbial community and eliminate the effectiveness of RO when TDS concentrations exceed 50 g/LTDS (a common occurrence with PW with TDS ranging from 10 g/L to 250 g/L). A more thorough understanding of how these processes perform when treating PW, through the use of pilot-scale experiments and data collection, is therefore necessary to fill this knowledge gap. Thus, the objective of this dissertation was to evaluate the efficacy and environmental impact of a cost-effective treatment trains for high salinity PW. This was be accomplished through long-term pilotscale testing on the effectiveness and resiliency of a biological pretreatment process on high salinity PW, a membrane bioreactor (MBR). Additionally, this dissertation describes a comprehensive environmental water quality analysis of PW chemistry, including toxic effects on human cells, throughout a complete treatment train that includes MBR pretreatment and membrane distillation for desalination. Finally, this dissertation evaluated the performance and practicality of a novel desalination process, LSRRO, to desalinate high salinity PW after several pretreatment steps, including MBR.
-
Extreme ultraviolet polarization optics for polarimetry of structured high harmonicsHigh Harmonic Generation (HHG) is a leading way to generate table-top coherent extreme ultraviolet (EUV) and attosecond pulses. Polarization characterization in this spectral range is important to understand the phenomena of HHG and quantify the light used for experiments. Furthermore, polarization control allows for polarization-dependent experiments in the EUV. This thesis proposes and demonstrates the usage of reflection-based polarization optics to act as a polarizer and a quarter wave retarder for 44 nm light, the 9th harmonic of 400nm. The design process for these polarization optics, so-called k-mirrors, in the EUV wavelength range is explained with possible design extensions for other wavelengths. These polarization optics in EUV are extended with full-beam polarimetry of harmonic light generated with structured illumination. Characterization of the fundamental light’s structured illumination and polarization profile combined with similar characterization of the harmonics can reveal insights into the HHG process. This thesis outlines steps to adapt Stokes polarimetry to perform measurements on EUV, allowing the characterization of novel generation schemes.
-
Magmatic-hydrothermal evolution of the Tuvatu alkalic epithermal Au-Telluride deposit, Viti Levu, FijiThe Tuvatu deposit is a high-grade alkalic epithermal Au deposit in Fiji that consists of steeply dipping lode veins, flat lying lodes, and breccias hosted in alkalic intrusive and volcanic rocks. This study performed drill core logging of veins and alteration, vein sample petrography, and geochemical microanalysis of biotite, pyrite, and marcasite to reconstruct the evolution of the magmatic-hydrothermal system and to identify the relative timing of Au mineralization. Eight vein types have been identified, including four porphyry-style veins which formed at higher temperatures and larger pressures, and four epithermal-style veins associated with shallow Au mineralization. The two environments were found to be genetically distinct. Secondary hydrothermal and primary magmatic biotite grains were analyzed with an electron microprobe and are found to contain different major element ratios. Hydrothermal biotite at Tuvatu contains element ratios associated with the porphyry environment and high temperature potassic alteration. Quartz in epithermal veins is shown to have textures indicating recrystallization from originally noncrystalline silica, which is interpreted to have deposited through flashing. Ore- stage pyrite and marcasite show distinct enrichments in As. Laser ablation-ICP-MS analysis revealed high As and Au incorporation in a specific stage of arsenic-rich pyrite which contains Au as a lattice-bound ion. This study emphasizes the potential importance of hydrothermal fluid flashing to high-grade Au deposition in the epithermal environment and the contribution of invisible Au in Fe sulfides to the total Au contained in alkalic epithermal deposits.
-
Quantifying the effects of curing stresses and soil type on the in-situ compressive strength of cement-treated soils via deep mixing methodThe Deep Mixing Method (DMM) is an ever-growing ground improvement technique used worldwide to improve in-situ soils using cementitious binders and/or other types of admixtures to either increase the strength and/or decrease the permeability of soils for various types of civil and/or environmental engineering applications. The process involves using specialized high torques drilling equipment with specially design augers or cutters that allow for the injection of flowable grout (suspension) through the auger or cutter to liquify the soil in-situ while blending in the cementitious suspension. When the suspension and soil mixture solidify, the designed engineering material properties are achieved. This study investigates the curing conditions in which in-situ materials are exposed to and how these conditions influence the strength of the deep mixed materials. The findings of this study reveal that the curing conditions for in-situ materials, particularly the applied stresses and temperatures, differ significantly from the conditions under which typical quality control specimens are cured. Consequently, these conditions can lead to a significant underestimation of the strength of the in-situ materials if curing conditions are not considered. This study incorporates field and laboratory data collected from various studies that directly or indirectly studied the influences of curing stresses on deep mixed material’s mechanical properties. The data compiled for this investigation included strength and temperature data derived from specially designed laboratory bench test studies, field conducted modified oedometer tests, in-situ instrumentation tests from deep mixed columns, and a review of independent coring data results from datasets collected from deep mixed project across north America. Upon reviewing these collective studies, it becomes evident that there exists a correlation between curing stress and unconfined compressive strength (UCS). The observed rate of strength gains follows a linear pattern and is influenced by the type of soil being treated. Notably, fine-grain soils exhibit a higher rate of strength gain when subjected to in-situ curing stress (with depth) in comparison to granular soils. Additionally, this study puts forth several hypothesized physio-chemical mechanisms that could potentially explain the varying strength gains observed based the influence of the treated soil’s particle size.
-
Estimation of the utilization rate for pressurized face shielded TBMs using discrete event simulationDuring tunnel construction using a Tunnel Boring Machine (TBM), various tasks are performed to excavate and support the ground. The ratio of TBM excavation duration to the total working time is called the machine utilization rate and represents the efficiency of operations, operational, and management factors. Calculation of the TBM utilization rate involves dividing the duration of the boring time by the total duration of all construction activities, which includes all the delays and downtimes in the operation that are difficult to quantify in many operations. The type and duration of activities and their interactions are influenced by project-specific factors and TBM types. The high variability and uncertainty in activity type and durations complicate the prediction of utilization rate and make theoretical and deterministic approaches less accurate for this purpose. Often experts rely on past experiences and empirical equations derived from historical data to estimate machine utilization. The results are usually unreliable and biased because they cannot incorporate the impacts of new developments and different site setups. This thesis explores the application of Discrete Event Simulation (DES) techniques in predicting the TBM utilization rate, as a follow-up to previous studies in this area. Previously, the CSM2020 model was established using ARENA and MATLAB for hard rock TBMs. The existing modules in the CSM2020 DES model were adjusted and improved with a focus on simplicity to enhance usability and adaptability and to minimize the complexity of setting up and running the model by users. Additionally, the activity modules and workflow of the pressurized face TBMs are added to the simulation model to extend the model’s area of application. The new model employs Python and relevant libraries. The shift to Python and integration of the SimPy framework improve the model’s flexibility and accessibility. The developed simulation tool is a versatile, customizable, and predictive tool that can be applied to various types of TBM and transportation systems, ground conditions, and project specifications to forecast TBM performance, recognize the bottleneck and do what-if scenario analysis during the construction. Due to insufficient data, an evaluation of the model’s accuracy in adapting to varying ground conditions was not conducted. While the prediction of components of operations and pertinent activity times is very helpful, the results of the prediction using DES models rely on the available database of the recorded activity times. Hence, the accuracy of the model and its prediction can be improved when used during construction by incorporating field measurements during the tunnel construction phase. In this study, construction activity durations, geological data, and design parameters were collected from recently completed pressurized face TBM projects. After collecting, cleaning, and restructuring the data, in comparative statistical studies, the effects of tunnel diameter and curvatures on operational parameters and machine utilization were explored. Based on the results, it is clear that the duration of ring building is affected by the diameter of the tunnel. However, the skills of the ring builder, the equipment used, and the ring building systems also play a significant role. The curvatures along the alignment have a minimal operational impact but increase surveying duration and segment failures. Similarly, the preliminary analysis of the recorded activities showed an increase in machine diameter can increase the duration of activities and hence reduce machine utilization, especially when the segment installation is the dominant activity. As part of this study, an analysis was conducted to investigate activity times and corresponding downtime allocation. The study can be practical in identifying the critical causes of delays in projects by indicating the impact of parallel activities on delays and, consequently, the machine utilization rate. The analysis reveals that recording downtimes related to parallel activities are rather biased, suggesting a need for closer scrutiny of shift reports. The investigation also highlights the difficulty in retroactively calculating activity time based on recorded delay times. After completing the data analysis phase and preparing the data for use in the DES model, a user data entry interface is created to feed the input data into the DES model. This interface ensures the consistency and appropriateness of input parameters according to the model setup while facilitating the modification of data and execution of what-if scenario analysis. The data entry platform is integrated into the DES model, and simulations were conducted using the DES model for validation. The simulation model is verified by comparing the model’s predictions to the observed and recorded downtime and utilization rates. The model accurately predicted machine performance revealing functionality in operational settings for pressurized face TBMs. The model’s effectiveness spans across various phases of tunneling operations. However, its accuracy and reliability can be significantly enhanced by incorporating more precise data, specifically field measurements obtained during construction. With this integration, the model achieves improved forecasts of tunnel cost, completion time, and identification bottlenecks in the operation. This valuable information plays a crucial role in guiding the planning, optimization, and management throughout the entire tunneling process.
-
Computational framework for modeling blood clotting in extravascular injuries, AHemostasis is the process by which a blood clot forms to prevent bleeding at the site of an injury. The formation time, size, and structure of a blood clot depends on the local hemodynamics and the nature of the injury. Previous computational models have focused primarily on intravascular clotting, where there is no blood loss and clotting is limited to the interior of a vessel. Models of extravascular clotting, where blood leaks from a vessel into the extravascular space, are still early in development. In this thesis we present a mathematical and computational framework for simulating clotting in both intravascular and extravascular settings. The framework we developed includes an open-source software package called clotFoam, that simulates blood clotting using a continuum model of blood coagulation and platelet aggregation within a dynamic fluid environment. The governing equations include advection, diffusion, reaction, and Navier-Stokes-Brinkman equations, and are solved using a finite volume approach built upon the open-source libraries of OpenFOAM. Within this new framework, we investigate the influence of transmural pressure and injury size on clot structure and occlusion times in extravascular injuries. We found that increasing the transmural pressure prolonged the occlusion times and increased the density of the blood clots that formed in the extravascular space. However, occlusion times were longer than expected for the high-shear environments that we studied. This motivated the development of a new mathematical model that incorporated shear-dependence into the platelet aggregation. Simulations with this model resulted in shorter, more relevant occlusion times when shear was increased, highlighting the importance of including shear-dependence into the model. Our framework provides the foundation on which one can build more complex models and perform reliable simulations in almost any computational domain.
-
Improving electron beam induced current analysis of CdTe devices through principal component analysisThe development of modern photovoltaic devices requires the ability to correlate uncontrolled micro-structure growth with changes in the electronic properties of the device. While Electron Beam Induced Current (EBIC) characterization should be well positioned to lead this analysis, its interpretation has proven difficult in thin film devices. In this document, we present improvements to two methods reducing interpretation ambiguity in EBIC analysis. First we demonstrate voltage-biased EBIC on a low short-circuit current device. Device choice allowed us to obtain high resolution voltage dependent collection maps. We combine these collection maps with simulation and principal component analysis to experimentally demonstrate low correlation between short circuit collection and voltage response as well as a method for identifying regions with similar voltage responses. Next we demonstrate variable beam-current injection dependent EBIC. We expanded on the single study that had previously demonstrated this technique, combining it with principal component analysis. From this we developed the ability to differentiate regions of similar collection efficiency so as to identify barriers to collection. This dissertation serves to demonstrate how much additional information can be obtained through the variation of imaging conditions. Through the combination of variable imaging conditions and principal component analysis, we are able to decrease characterization ambiguity with no additional investment for the tool owner. We are able to two forms of lateral grain boundaries in CdTe devices. These techniques are able to definitively show the role grain boundaries play in short circuit collection efficiencies.
