Mines Repository (Colorado School of Mines)
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Phosphorus in iron-based lunar in-situ resource utilization (ISRU) alloys
No full textIncludes bibliographical references.2024 Summer.With a growing global space industry and the plan to return humans to the Moon within the decade, countries are examining in-Situ Resource Utilization (ISRU) as a means to reduce costs and expand capability. For the past 50 years, ISRU research has primarily focused on propellant production, only recently growing to seriously consider the production of metals. Through additive manufacturing (AM), these ISRU metals could be used for surface construction and repairs, opening new opportunities in addition to substantial improvements to mission costs and capability. This dissertation examines iron-based alloys (steels) produced by hydrogen reduction of lunar regolith simulants, including testing of the first publicly demonstrated consolidated metal produced from regolith or simulants. Phosphorus was found to be a major contaminant within the hydrogen reduction steel at a concentration between 0.86-0.91wt% due to phosphorus naturally found in regolith, transferred to the steel during the melt refining operation. Phosphorus leads to brittle behavior in steel; testing of analog alloys from 0.18-1.09 wt% phosphorus focused on maximizing ductility through heat treatment while considering lunar restrictions. Boron was tested as a potential addition for mitigating the embrittling effects of phosphorus but was unsuccessful at the levels tested.
In addition to finding the high phosphorus content of lunar steels, this research contributed by developing a heat treatment procedure to induce room temperature ductility in samples with 0.36wt% phosphorus, with an estimated maximum phosphorus content of 0.45-0.50wt%. Four methods are proposed for reducing phosphorus below this maximum. The mechanisms of phosphorus transfer mean that phosphorus contamination in a steel product will occur in all four leading ISRU technologies (hydrogen reduction, carbothermal reduction, molten regolith electrolysis (MRE), and the FFC process) making this finding important across the field. This research additionally examined the processing required to convert ISRU steels into a feedstock for additive manufacturing, demonstrating wire drawing and wire arc additive manufacturing (WAAM) using a commercial low carbon steel. A 6-axis robot arm was used to produce walls and components in non-vertical orientations as well as example components to highlight potential applications for these steels
Malachite, tenorite and pyrolusite
No full textPhotographed by Ron Wolf.Botryoidal green malachite occuring with grey-black tenorite and pyrolusite
Amazonite, smoky quartz and cleavelandite
No full textPhotographed by Ron Wolf.Blocky pale blue amazonite occuring with smoky quartz, Crystal Peak area, Teller County, Colorado
Bayesian approach to alloy simulation, A
No full textIncludes bibliographical references.2024 Fall.Alloy simulation is—seemingly—rife with intractable mathematical problems: the combinatorial explosion of atomic decorations, the irreducible global nature of convex hulls, and the curse of dimensionality in configuration space. Due to the importance of alloys across materials science, considerable attention has been given to surmounting these computational obstacles. The typical angle is to tackle daunting mathematical problems with increasingly complex model-Hamiltonians, ranging from lattice models to machine-learned interatomic potentials. Even in the fortunate cases where these models provide accurate predictions, extracting insight from large-scale simulation is challenging, limiting our theoretical understanding.
Herein, I take a Bayesian approach to alloy simulation. All predictions provided from calculations are thus represented as probabilistic distributions over possible outcomes rather than isolated, singular values. The classic materials science questions are used to ground this thesis: i) can a given chemical composition be made as a single-phase alloy; and ii) what will be its local and long-range atomic structure? Equipped with Bayesian modeling, I show that relatively few calculations are necessary to provide sufficient estimations for the stated questions. As such, advanced alloy simulation is returned to the domain of first-principles calculations, enabling accuracy and functionality. While I focus solely on first-principles simulation, model-Hamiltonian research also stands to benefit from the developed Bayesian approach, which could accelerate exploration across time scales, length scales, and composition spaces. If there is an overarching thesis to this amalgamation of work, it is the following. Bloated mathematical constructs conceal the underlying simplicity of alloys—through the success of relatively simple simulation, I highlight the elegance within these disordered materials
Philipsburgite on quartz
No full textPhotographed by Ron Wolf.Green microcrysalline layer of philipsburgite on quartz, Philipsburg district, Granite County, Montana
Design and development of a data acquisition software pipeline for superconducting tunnel junction sensors in the BeEST and SALER experiments, The
No full textIncludes bibliographical references.2024 Fall.Expanding the frontiers of nuclear and subatomic physics research demands highly specialized data acquisition systems that can handle the complexities of modern quantum sensing technologies. This thesis details the creation of a dedicated data acquisition software pipeline tailored to the unique requirements of superconducting tunnel junction (STJ) sensors in the BeEST and SALER experiments. Central to this system is SALERScope, a versatile, multi-threaded C backend that interfaces with MPX-32d digitizers developed by XIA LLC. SALERScope, in conjunction with a high-performance Python API, provides researchers with the tools to quickly build custom interfaces and automate procedures, enabling flexible and efficient experimentation with STJ arrays. This hybrid architecture seamlessly integrates Python’s intuitive control with C’s speed and stability, coordinating high-throughput data collection while managing complex functionalities like automated STJ bias adjustments, peak tracking, spectrum generation, and preliminary analysis. Serving as a powerful alternative to XIA native software, ProSpect, the SALERScope platform overcomes limitations in handling STJ-specific data acquisition and control needs, offering superior flexibility and operational reliability. Paired with a Python-based graphical user interface, the software provides immediate diagnostic feedback and long-term data acquisition capabilities, making it a robust, adaptable platform for STJ applications. This system achieves new levels of automation, data accuracy, and user-friendliness, paving the way for expanded experimental capabilities and more efficient workflows in research with STJs
Quantifying causes and variability of rockfall activity: comparison of rock slopes monitored using terrestrial remote sensing
No full textIncludes bibliographical references.2024 Spring.Rockfall is a major geohazard in mountainous areas where infrastructure is developed adjacent to steep, rocky terrain. Multiple major roadways, including Interstate-70, were constructed through the Rocky Mountains of Colorado by blasting and excavating rock to create space for the road, resulting in unstable road cuts. Inventories of rockfall created using remote sensing or observational methods are frequently used to characterize rockfall hazard by analyzing the volume distribution of past rockfalls. The relationship between rockfall magnitude and frequency can be represented by a negatively scaled power law with two fit parameters: an activity constant (A) and scaling exponent (B). Spatially normalizing the power law by rockfall source zone area allows for comparison of rockfall activity between different slopes through the normalized activity constant (Ast), which can be used to prioritize rockfall scaling and mitigation.
This research seeks to determine the slope, rockmass, climate, and database variables that influence the rockfall power law. The impact of total monitoring time on uncertainty of the power law was assessed using bootstrapped 95% confidence intervals on the fit parameters. Site-specific effects were found; slopes that experience more uniform rockfall frequency and volume over time have lower parameter uncertainty over the observed volume range, while the parameters for inventories that contain large events relative to the rest of the volume distribution are more uncertain. Sufficient monitoring time to adequately characterize a rock slope for hazard analysis ultimately depends on the rockfall rate, the range of observed volumes, and the maximum geologically feasible event size. Uncertainty of the power law parameters tends to be lower for larger inventories.
Analysis-of-variance and regression were used to identify the variables that influence Ast and overall rockfall activity for a database of 44 rockfall inventories. Monitoring method was found to have a large, confounding effect on the results, so only the 34 terrestrial remote sensing inventories were considered for further analysis. Rockmass condition, lithology, whether a slope is natural or cut, freezing intensity, and winter precipitation displayed possible influence on the forecast frequency of different rockfall volumes using the power law.
The power laws for seven Colorado cut slopes were compared with slope features, rockmass characteristics, and climate at each site. Variations in lithology and general rockmass structure are the best predictors of rockfall activity for Colorado cut slopes. Sedimentary slopes experience more rockfall activity than crystalline slopes, which is attributed to differential erosion of sedimentary layers causing frequent rockfalls. Local climate (including precipitation, snowmelt, and freeze-thaw) mostly affects seasonal trends in rockfall frequency and not long-term rockfall activity differences