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Browsing by Author "Troxell, Wade, committee member"

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    A novel smoother-based data assimilation method for complex CFD
    (Colorado State University. Libraries, 2024) Hurst, Christopher L., author; Gao, Xinfeng, advisor; Guzik, Stephen, advisor; Troxell, Wade, committee member; van Leeuwen, Peter Jan, committee member
    Accurate computational fluid dynamics (CFD) modeling of turbulent flows is necessary for improving fluid-driven engineering designs. Traditional CFD often falls short of providing truly accurate solutions due to inherent uncertainties stemming from modeling assumptions and the chaotic nature of fluid flow. To overcome these limitations, we propose the integration of data assimilation (DA) techniques into CFD simulations. DA, which incorporates observational data into numerical models, offers a promising avenue to enhance predictability by reducing uncertainties associated with initial conditions and model parameters. This research aims to advance our understanding and application of DA for CFD modeling of highly chaotic dynamical systems. This dissertation makes several novel contributions in DA and CFD: i) A novel DA algorithm, the maximum likelihood ensemble smoother (MLES), has been developed and implemented to provide better model parameter estimation and assimilate time-integrated observations while addressing nonlinearity, ii) Multigrid-in-time techniques are applied to enhance the computational efficiency of the MLES by improving the optimization processes, and iii) The MLES+CFD framework has been validated by classical test problems such as the Lorenz 96 model and the Kuramoto-Sivashinsky equation. The effectiveness of the MLES has been demonstrated through a few test problems featuring chaos, discontinuity, or high dimensionality.
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    Employee engagement: critique, theory, and model
    (Colorado State University. Libraries, 2023) Stampka, Scott A., author; Makela, Carole, advisor; Folkestad, James, committee member; Troxell, Wade, committee member; Venneberg, Donald, committee member
    Organizational behavior researchers fail to critically evaluate the congruence between their conceptualizations, definitions, and measures of employee engagement. Three manuscripts are presented to illuminate this unrecognized confusion in employee engagement research. The first manuscript explored the employee engagement, motivation, and performance literature, and presented a definition of employee engagement necessary for the three constructs to fall within the same nomological network. The definition was unique and counter to the most prevalent conceptualization of employee engagement. It was shown, for employee engagement to provide organizational value, it should be defined to include physical behaviors aligned with the goals of the organization. This positions employee engagement as a motivated state, contrary to the most prolific conceptualization, which describes employee engagement as a motivational state. The second manuscript explored the motivation and employee engagement literature to develop a Motivation Model of Engagement (MMOE). It was shown traditional motivation theories focus on 'why' someone is motivated. However, the MMOE elucidates 'how' someone becomes motivated. The MMOE described how employees become engaged and what influences the likelihood of engagement. The MMOE is unique and strengthens motivation theory by filling in common gaps in existing theories and showcases how existing motivation theories complement each other. The third manuscript illuminated the incongruence of current employee engagement research designs, and presented a heuristic model, which aligns conceptualization, definition, and measure. A measurement model was presented, which described influencers of employee engagement. A conceptual measurement instrument was presented, which captures the likelihood of employees engaging in behaviors beneficial to the organization and illuminates potential interventions to increase the likelihood of employees engaging. The manuscripts were presented such that each built on the one preceding. However, each was intended to be applicable to research and practice on its own. Implications for research and practice were discussed, as well as potential applications. Further, suggestions for future research were proposed to entice, strengthen, and grow organizational behavior research.
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    Helical pile capacity to torque ratio: a functional perspective
    (Colorado State University. Libraries, 2019) Souissi, Moncef, author; Siller, Thomas, advisor; Charlie, Wayne, committee member; Perko, Howard A., committee member; Troxell, Wade, committee member
    The capacity to installation torque ratio, Kt, has been used in the design of helical piles and anchors for over half a century. Numerous researches have been conducted to accurately predict this capacity-torque correlation factor. However, almost of all these Kt factors published or released by the manufacturers are based on shaft geometry alone, Hoyt and Clemence (1989). Recent full-scale tests (axial compression and tension) in clay, sand and bedrock have shown that the traditional Kt used, based on shaft size only, can be improved upon. The capacity to torque ratio seems to depend on the shaft size, shaft geometry, helix configuration, load direction and soil type, Lutenegger (2015). Seven hundred ninety-nine (799) full scale load tests in compression and tension were conducted on helical piles of varying shaft sizes, shaft geometry, helix configurations and different soil type (sand clay, and bed rock). The objective of this research is to determine the effect of these variables on the capacity torque correlation factor, develop a new empirical relationship between pile capacity and installation torque, and determine its reliability in comparison to the published Kt values used in the pile industry.
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    Numerical algorithms for two-fluid, weakly-compressible flows
    (Colorado State University. Libraries, 2024) Brodin, Erik, author; Guzik, Stephen, advisor; Colella, Phillip, advisor; Gao, Xinfeng, committee member; Troxell, Wade, committee member; Bangerth, Wolfgang, committee member
    A multifluid numerical method is developed for flows of two fluids in a single domain at low Mach numbers. An all-speed formulation of the Navier-Stokes equations governs the dynamics of both fluids and the level-set method defines the interface between them and the domain of each fluid. The algorithm represents velocity and pressure as single valued throughout the whole domain, and fluid dependent variables, density and bulk modulus, only in the domain of their respective fluid. The all-speed equations are not subject to the divergence-free velocity constraint through use of a redundant velocity equation, and are evolved in time using an implicit-explicit additive Runge-Kutta method resulting in a time step constrained only by the bulk fluid velocity. Each fluid is evolved conservatively with respect to the moving interface between them. Due to errors in the evolution in the interface, perturbations in the volume of each fluid, and thereby the density, can develop. A thermodynamically consistent correction is made to the position of the interface to reduce these unphysical perturbations. The algorithm developed here includes three novel contributions: (i) the use of a multifluid all-speed algorithm with a level-set method for evolution of the solution in time, (ii) a multifluid algorithm using the level-set to capture the interface in the weakly compressible regime that is thermodynamically consistent, and (iii) an initialization method for sharp corners in the level-set. Numerical tests have demonstrated that the algorithm exhibits the expected low Mach number behavior, achieves second order-accuracy, and ensures fluid volumes are bounded and convergent.
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    Raw material optimization and CO₂ sensitivity-predictive analytics in cement manufacturing: a case study at Union Bridge Plant, Heidelberg Materials, Maryland
    (Colorado State University. Libraries, 2024) Boakye, Kwaku, author; Simske, Steve, advisor; Bradley, Tom, committee member; Troxell, Wade, committee member; Goemans, Chris, committee member
    Cement has been in use by humans throughout history, and its manufacturing process has undergone many changes. The high increase in economic growth around the world and the demand for rapid infrastructure development due to population growth are the underlying reasons for the globally high cement demand. Cement is produced by grinding clinker together with a mixture of ground gypsum. The clinker is produced using a rotary kiln which burns a mixture of limestone, clay, magnesium, silica, and iron with desired atomic percentages through the calcination process. The quarry serves as the main source of raw material for the rotary kiln in cement production. Over the years cement manufacturing has hurt environmental, social, and political aspects of society. This negative impact includes the overuse of raw material which is obtained by mining resulting in disturbed landmass, overproduction of rock waste material, and the emission of CO2 resulting from the calcination of limestone in the pyro process. The study looks at three cement manufacturing systems and uses different methodologies to achieve results that can be implemented in the cement industry. These three systems were (1) the quarry (2) the preheat tower and (3) the kiln. Ensuring the consistency of material feed chemistry, with the quarry playing a pivotal role, is essential for optimizing the performance of a rotary kiln. The optimization of the raw material also allows limited use of raw materials for cement manufacturing, cutting down waste. The study employed a six-step methodology, incorporating a modified 3D mining software modeling tool, a database computer loop prediction tool, and other resources to enhance mining sequencing, optimize raw material utilization, and ensure a consistent chemistry mix for the kiln. By using overburden as a raw material in the mix, the quarry nearly universally reduced the environmental impact of squandering unwanted material in the quarry. This has a significant environmental impact since it requires less space to manage the overburdened waste generated during mining. In addition, raw material usage was optimized for clinker production, causing a reduction of 4% in sand usage as raw material, a reduction in raw material purchase cost, a reduction of the variability of kiln feed chemistry, and the production of high-quality clinker. The standard deviation of kiln feed LSF experienced a 45 percent improvement, leading to a 65 percent reduction in the variability of kiln feed. The study also uses machine learning methods to model different stages of the calcination process in cement and to improve knowledge of the generation of CO2 during cement manufacturing. Calcination plays a crucial role in assessing clinker quality, energy requirements, and CO2 emissions within a cement-producing facility. However, due to the complexity of the calcination process, accurately predicting CO2 emissions has historically been challenging. The objective of this study is to establish a direct relationship between CO2 generation during the raw material manufacturing process and various process factors. In this paper, six machine-learning techniques are explored to analyze two output variables: (1) the apparent degree of oxidation, and (2) the apparent degree of calcination. Sensitivity analysis of CO2 molecular composition (on a dry basis) utilizes over 6000 historical manufacturing health data points as input variables, and the findings are utilized to train the algorithms. The Root Mean Squared Error (RMSE) of various regression models was examined, and the models were then run to ascertain which independent variables in cement manufacturing had the largest impact on the dependent variables. To establish which independent variable had the biggest impact on CO2 emissions, the significance of the other factors was also assessed.
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    Understanding collaboration of university, government, and industry leaders to enhance local economic development: a phenomenological study
    (Colorado State University. Libraries, 2023) Douglas, Brianna B., author; Anderson, Sharon K., advisor; Gloeckner, Gene, committee member; Kuk, Linda, committee member; Troxell, Wade, committee member
    This qualitative dissertation explored the research question, "How do presidents at small private universities collaborate with local government and industry leaders in their host communities to enhance economic development?" The data were collected from three presidents that had been a university president at a qualifying institution for at least three years and had experienced efforts to collaborate with local government and industry to enhance economic development. Data were analyzed using Interpretative Phenomenological Data Analysis and revealed that presidents at small private universities collaborate with local government and industry leaders in four ways: (a) informally, (b) selectively, (c) without the experts, and (d) with mediocre leadership engagement. These findings aligned with three categories of the Wilder framework: environment, membership characteristics, and resources. Three key insights for presidents of small private universities come to light that provide insight into how to successfully collaborate with local government and industry leaders to enhance economic development in their host communities: (a) succeed at being an exceptional leader, (b) foster a culture of adaptability to change, (c) be courageous and establish a history of tripartite collaboration with local government and industry leaders.