Mines Repository (Colorado School of Mines)
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Numerical study of the semi-ellipsoidal TBM cutterhead on ground behavior and effects of the new design
No full textIncludes bibliographical references.2024 Fall.In this thesis, the effect of semi-ellipsoidal cutterhead design on tunnel boring machines (TBM) performance is evaluated by comparing the conventional cutterhead design through numerical modelling in FLAC3D. The study mainly focuses on key operational parameters such as ground displacement, stress distribution, thrust, torque and advance rate on both models i.e., semi-ellipsoidal cutterhead and conventional cutterhead in Earth Pressure Balance and Slurry TBMs. Results indicate that the semi-ellipsoidal design provides a more uniform distribution of stresses and displacements, reducing the risk of face collapse, ground failure and stability of the ground in softer grounds. However, this design requires significantly higher thrust and torque due to its larger surface area, leading to require better drive systems and more powerful thrust cylinders to excavate the same ground when compared to the conventional cutterhead, but these can be compensated with faster advance rate and saving costs and time of the project. In contrast, the conventional cutterhead achieves a higher advance rate with lower energy demands in harder soils/rocks. Stability of conventional cutterhead is lower when compared with ellipsoidal cutterhead in less competent soils. The findings underscore the need to carefully select TBM designs tailored to specific ground conditions, balancing operational efficiency with safety and stability requirements. This research contributes to the advancement of TBM technologies and gives insights into the idea of developing a new cutterhead design for TBM tunneling
Scheelite on fluorite (fluorescent)
No full textPhotographed by Ron Wolf.Yellow-white scheelite on mass of vitreous white fluorite crystals (A0179-0332); two views of scheelite on fluorite: top view shows specimen in plain light; bottom view shows fluorescent properties of blue under ultraviolet light (A0179-0333); yellow-white scheelite on mass of white fluorite, showing fluorescent properties of blue under ultraviolet light (A0179-0334); Dachang, Guanxi Province, China
Natrochalcite
No full textPhotographed by Ron Wolf.Small crystals of glassy green natrochalcite on a grey-white matrix, Calama, El Loa Province, Antofagasta Region, Chile
Beryl var. aquamarine
No full textPhotographed by Ron Wolf.Cloudy pale blue aquamarine (variety of beryl)
Anatase with quartz
No full textPhotographed by Ron Wolf.Metallic grey prisms of anatase on glassy quartz crystal, Matskorhae, Hordaland fylke, Norway
Development of an image-based inertial impact test for the identification of polymeric material parameters at high rates
No full textIncludes bibliographical references.2024 Spring.The mechanical properties of polymers are becoming increasingly important both on their own and as matrix materials in composites for military, automotive, aerospace, medical applications, to name a few. At the same time, it is well-known that the mechanical properties of polymers are highly strain rate dependent. Traditional methods used to characterize material model parameters under dynamic conditions are limited by several restrictive assumptions. Generally, they investigate one deformation mode at one strain rate at a time. Therefore, traditional material characterization requires many experiments to fully identify mechanical properties. This dissertation leverages ultra-high-speed imaging and full-field metrology, including the grid method and digital image correlation, along with computational simulation to design an image-based inertial impact test (IBII) for polymers that utilizes the acceleration fields as loading information. An inverse technique, the virtual fields method (VFM) is used to extract stiffness sensitivity across strain rates in tension and compression for a model material of PMMA. Moreover, this work expands the existing VFM framework to account for viscoelastic material parameter identification. Using finite element simulations, the Maxwell form of the standard solid model is employed. The resulting model displacement fields are used to simulate the full-field images that would be produced from a physical experiment, and VFM is used to extract the constitutive parameters over a sweep of processing parameters. Specifically, the effects of image noise and the ideal processing settings for spatial and temporal resolution are quantified for optimal experimental configurations. The simulations reliably produce the bulk modulus, shear modulus, and associated time constant from a single IBII test. This ability to identify multiple constitutive parameters' evolution over time from a single experiment demonstrates considerable promise towards reducing the number of experiments required to fully describe the mechanical behavior of polymers at high strain rates
Epistilbite on calcite
No full textPhotographed by Ron Wolf.Sugary brown rounded clumps of epistilbite on small white calcite crystals
Industrial microalgal characterization and enhancement for a sustainable future
No full textIncludes bibliographical references.2024 Spring.Microalgae are compelling renewable resources because of their rich biomass composition, with applications including biofuels, bioplastics, and nutraceuticals. However, economically viable industrial algal cultivation requires improved biomass productivity, stress tolerance, and product yield. This work addresses the need for better industrial microalgal strains. First, adaptive laboratory evolution (ALE) of Nitzschia inconspicua was utilized to increase high temperature tolerance. Second, biomass characterization and genetic engineering of Picochlorum celeri were deployed to better understand composition and carbon usage.
Nitzschia inconspicua is a diatomic microalga with high relative lipid content, making it a promising platform for sustainable aviation fuel (SAF). ALE was conducted to increase the temperature tolerance of N. inconspicua to 37.5 °C, a lethal temperature to the parent (WT) strain. Clonal isolation of the adapted strain resulted in two unique clones with increased cell size (~20 μm) relative to adapted strain prior to clonal isolation, indicative that a sexual cycle occurred. Preliminary outdoor pond data showed increased productivity of adapted clones compared to WT, enabling more viable SAF production from this strain.
Picochlorum celeri TG2 is a green microalga with rapid growth in high light, high CO2, and seawater. To characterize potential applications, a detailed biomass analysis was conducted. Nutrient-replete P. celeri contained protein-rich biomass. Gradual nitrogen restriction shifted biomass from primarily proteins to carbohydrates as cells transitioned into storage metabolite production. Hyper saline (2X) cultivation resulted in increased levels of the amino acid proline, which putatively acts as an osmolyte. This identification of biomass components yields critical information that informs how this strain might be utilized for renewable product production.
While P. celeri shows high biomass productivity with high CO2 supplementation, growth is slow in air. To understand carbon usage in P. celeri, eight carbonic anhydrases were identified through BLAST investigation and four of these characterized through transformation of fluorescently-tagged carbonic anhydrase constructs. By using confocal imaging, carbonic anhydrases were experimentally localized throughout the cell. Targeted CRISPR/Cas9 knock-out of several carbonic anhydrases revealed unique stationary phase functionalities for these enzymes. This work enables future engineering of more efficient P. celeri carbon usage, facilitating more economically viable algal bioproducts
Diffusion potential of CO₂ into caprock and forward modeling of a CO₂ sequestration site
No full textIncludes bibliographical references.2024 Spring.The increasing rates of global temperatures and climate change has necessitated the adoption of new technologies such as Carbon Capture and Utilization (CCU) and Carbon Capture and Sequestration (CCS). This thesis delves into both approaches across three comprehensive segments, highlighting their significance in the broader context of environmental sustainability and technological advancement. The first part presents an assessment of emissions reduction in CCU operations. Since CCU is presented as a more viable option to reduce emissions, the associated CO2 storage in CCU operations needs high reliability and an ability to rapidly detect and monitor leaks. The second part assesses how to monitor a compromised seal due to gas diffusion from the reservoir and the third part employs the use of machine learning for rapid investigation of gas migration and the detection of potential leaks.
The first part of this study is focused on emissions assessment. CCU and CCS technologies can play a crucial role in mitigating Green House Gas (GHG) emissions. CCU uses CO2 to enhance oil recovery (CO2-EOR) which compensates for the high cost of storing CO2 in the subsurface (CCS) via the increased production of oil (EOR-oil). CO2 used in such operations is obtained from captured or geological sources. CO2-EOR also produces additional CO2 due to usage of the increased oil production (EOR-emissions). Therefore, CO2-EOR needs an assessment of not only the economics but also the emissions generated by the additional oil as well as the complexity to capture these additional emissions. Here, I examine the CO2 budget in CO2-EOR operations by comparing the emissions generated from oil production to the amount of CO2 sequestered. I also investigate the costs associated with emissions mitigation and compare them with profits from additional oil revenue. I find that (a) the CO2 emissions from oil production operations surpass the amount of CO2 stored in the reservoir, (b) the incremental oil produced with CO2–EOR operations further contributes to the overall atmospheric emissions.
The second part of this study studies the seismic assessment of CO2 sorption in shales. CCS is limited by some risks and uncertainties; one of such risks is the issue of CO2 conformance in the reservoir. While leakage through the plume to spill points, migration along faults, fractures and abandoned wells have been studied, leakage via diffusion through the seal remains poorly determined. There are limited data that explore CO2 diffusion mechanisms in CCS studies. For example, it is acknowledged that CO2 will leak through the caprock through diffusion and dissolution, but the mechanism and the time rates of this leakage are poorly documented. Thus, in this part of the thesis, I acquired experimental data to estimate the CO2 diffusion coefficient from the reservoir formation into the shale and determined the effects of CO2 adsorption into the shale formations using ultrasonic measurements. I also analyzed mineralogical interactions in the seal with Scanning Electron Microscopy (SEM) imaging and Energy Dispersive X-ray Spectrometry (EDS).
The third part of this thesis uses machine learning to predict the velocity of CO2 storage formations. Predictive tools are required for critical decision-making for CO2 storage projects and most recently, machine learning tools have found numerous applications in geophysics. To predict long-term changes in the reservoir with CO2 injection, I applied machine learning to petrophysical properties that are critical for optimization of field scale CCS operations. In this part of the thesis, I developed models to predict the velocity of the reservoir formations. The models Random Forest Regression (RF), Multi-feed forward neural networks (MFNN) and Long Short-Term Memory (LSTM) were tested by combining all data points from the wells and by segregating only the reservoir zones. The Random Forest model performed better than the other two models, and the model development by reservoir zones reduces mismatches and allows for a more accurate prediction of the properties being trained.
All three aspects of this study; (a) the assessment of the carbon neutrality of CO2-EOR; (b) experimental studies to determine the diffusion of CO2 into the seal and the viability of the seal for CO2 storage and; (c) machine learning for the prediction of rock properties, are critical to ensure that CCU and CCS operations are safe, environmentally sustainable, and contribute towards
emissions reduction
Ferberite
No full textPhotographed by Ron Wolf.Metallic grey blades of ferberite, Atocha-Quechisla district, Potosi Department, Bolivia