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Evaluation of Multiple Approaches for Solidification Modeling of Advanced Nuclear Reactor Coolants
No full textThis dissertation focuses on developing and validating numerical tools for modeling solidification phenomena in advanced nuclear reactor coolants. The primary objective is to offer cost-effective alternatives to experimental studies and enable rapid simulation of multiphysics reactor transients. To achieve this, a hierarchical structured framework is established. High-fidelity models or experiments inform intermediate-fidelity data, which, in turn, provide training data for multidimensional coarse-mesh models capable of running on a single core and delivering fast results. The first phase of this research focuses on developing a solidification model using the Lattice Boltzmann Method (LBM) for 3D internal transient solidification modeling of Generation-IV nuclear reactor coolants. This model, based on the Total Enthalpy Model coupled with the Partially Saturated Method, addresses both high and low Prandtl numbers under laminar forced conditions. Key findings include the utility of the LBM total enthalpy Partially Saturated Method for solidification modeling, faster computational times compared to conventional Finite Volume Computational Fluid Dynamics (FV-CFD) methods for low Prandtl numbers, and the identification of stability limitations for high Peclet numbers.
In the second phase, we develop an intermediate-fidelity model based on a FV-CFD RANS enthalpy-porosity method, addressing high and low Prandtl numbers. We validate it against experimental data for high Prandtl numbers and against a high-fidelity LBM-LES model for low Prandtl numbers. Incorporating interface turbulence viscosity damping addresses RANS models��� tendency to overestimate heat transfer, enhancing their practical utility despite slightly reduced predictive accuracy compared to LES models.
In the third phase, we introduce a fine-to-coarse mesh upscaling technique enhanced by physics-based closure terms. Employing a data-driven strategy, we fine-tune the model���s closure coefficients utilizing the FV-CFD RANS intermediate-fidelity model on fine meshes. The calibrated coarse-mesh model reliably predicts essential performance metrics, including pressure drop, velocity profile, outlet temperature, and solid thickness distribution. This multidimensional approach marks a notable progression from conventional 1D methods, offering substantial time savings compared to fine-mesh models.
The highlights of this dissertation include (i) an innovative solidification model coupled with turbulence modeling based on LBM (ii) the validation and enhancement of an intermediate-fidelity RANS solidification model by incorporating a turbulence viscosity-damping source, preventing turbulence overproduction at the interface (iii) the development of a fine-to-coarse mesh upscaling solidification model, incorporating physics-based closure terms calibrated through a data-driven approach
A Study of Hard and Soft Materials Subjected to Ballistic and Hypervelocity Impact
No full textHypervelocity impacts (HVIs) (���2.0 km/s) can induce extremely high strain rates (>10��� s�����). Hence, developing materials to withstand HVIs is a key challenge in the effort to enhance protective infrastructure. Furthermore, computational software such as the Elastic Plastic Impact Computation (EPIC) code, can accelerate the development of these material systems.
Thermoplastics such as high-density polyethylene (HDPE) can be used to offer HVI damage resistance without compromising on weight and cost. This document reports a modeling effort to predict the HVI response of 6.35 mm HDPE plates when impacted by 10 mm Al spheres travelling 2.0���6.8 km/s. It was found that the simulation accurately predicted debris cloud velocity and shape, hole size, and mass loss for impact velocities <5.5 km/s.
Often, much denser high-performance concrete (HPC) mixtures like BBR9 are used as a HVI resistant building material. This work reports simulation and experimental efforts to predict the HVI response of BBR9 plates of varying thickness (25.4���127.0 mm) impacted by 10 mm S2 tool steel spheres travelling 1.8���3.1 km/s. Simulations reflected experimental fragmentation and energy absorption results well, and it was found that they even captured the transition region in which an increase in impact velocity resulted in a decrease in debris cloud velocity.
Layering materials to exploit their strengths can result in composites with increased kinetic energy dissipation and reduced target weight. The results reported in the HDPE and BBR9 investigations lead to their use in composite "sandwich" targets that can be optimized for key metrics. To test this principle, a multifaceted computational and experimental approach was used. By adjusting the volume fractions of the two materials in EPIC and subjecting the layered targets to a 2.0 km/s impact from a 10 mm S2 tool steel projectile, a mass-optimized, 50.8 mm thick composite target that dissipated maximum projectile kinetic energy was generated. Subsequently, the target was subjected to V������ ballistic limit testing with a 12.7 mm S2 tool steel spherical projectile, where it was found that the simulation-predicted and actual ballistic limits were within <1% of one another, justifying the idea that simulation-informed optimization is a useful tool in material design
Essays in Law and Economics
No full textMy dissertation focuses on issues related to the criminal justice system in the United States, especially with respect to indigent defense and policing.
First, I ask whether indigent defense attorneys secure better deals for same-race defendants. This is important since 80 percent of criminal defendants rely on assigned counsel for legal defense. To do this, I employ a difference-in-differences approach, exploiting the quasi-random assignment of court-appointed attorneys to cases in Travis County, Texas. Results indicate that while Black and White attorneys are similarly effective at securing dismissals for White defendants, Black attorneys are less effective than White attorneys at securing dismissals for Black defendants. Specifically, Black defendants who are represented by White rather than Black attorneys are 20-22 percent more likely to have their charges dismissed and 17-27 percent less likely to be incarcerated. Moreover, Black defendants who are represented by White attorneys are not more likely to re-offend in the future.
Second, I study the effect of police-involved shootings on gun violence and civilian cooperation with police. To distinguish between crime reporting and crime incidence, I use administrative data on 911 calls and ShotSpotter data from Minneapolis. Exploiting the variation in the timing and the distance to these incidents, I show that exposure to a police shooting increases gun-related crimes by 3-6 percent and reduces civilian crime reports to police by 4-6 percent.
Finally, I study the impact of internal affair investigations, the most common accountability system in policing, on police behavior. Using data from a large city where there is conditionally random assignment of officers to 911 calls, I employ regression discontinuity and difference-indifferences methods to distinguish the impact of investigations from confounding factors. Results indicate that increased oversight from internal investigations does not change an officer���s likelihood of making an arrest or using force. This is true across different types of allegations, including those that are sustained
Impacts of Hurricanes and Algae-Based Jet Fuel on Energy Production Economics and Markets
No full textThis thesis addresses energy economics aspects involving industry disruptions, low carbon technology, and market responses. The first essay explores the impacts of hurricanes on US refineries and fuel markets. Namely we examine hurricane effects on refinery input and output, crude oil imports, gasoline, diesel, and crude oil stocks, as well as pricing dynamics for gasoline and diesel. In terms of findings this study sheds light on disruptions caused by hurricanes and the resultant welfare implications. Findings reveal that hurricane landfall in the Gulf Coast is associated with decreased refinery production for up to six weeks thereafter, with varied effects beyond the Gulf Coast region across the US. Also, hurricanes lead to regional price increases. Overall, we find hurricane strikes decrease total US social welfare, but with differing effects on producers and consumers.
The second essay reports on life cycle assessment and techno-economic analysis of direct air capture supported algae cultivation systems. The analysis focuses on prospects for generating algae-based limonene that is transformed into aviation fuel and biomass for animal feed. The analysis examines the greenhouse gas and economic consequences of a potential technology that integrates CO2 capture to algae growth to limonene and animal feed productions. Challenges of CO2 source, integrating sorbent production and manufacturing, and nutrient recovery processes associated with algae cultivation are explored. The analysis reveals that achieving environmental and economic feasibility requires more than a hundredfold reuse of the CO2 sorbent and hydrogel, currently deemed unreachable. Additionally, the study underscores that utilization of renewable electricity could further diminish the carbon footprint.
In the third essay, we extend the second essay into the strategic domains of product mix and market penetration. We consider multiple limonene applications and algae biomass markets. We find that prioritizing limonene for cleaning and fragrance is superior to using it to produce jet fuel. We also find substantial penetration of the remaining algae-based biomass as animal feed and as a feedstock for electricity generation. Scenarios involving carbon credits and technology subsidies reinforce these findings. Furthermore, we investigate the influence of enhanced limonene yield which is needed for entry into the jet fuel market
Applied and Theoretical Approaches to Decision Making in Agriculture
No full textThis work consists of three essays, each demonstrating economic approaches to decision making.
In the first essay, the choice of which Title I farm program to enroll in is modelled as a quadratic integer programming problem. This framework is used to determine optimum program selection for upland cotton producers in Hale County Texas with average risk aversion.
The second essay quantifies the gains in efficiency resulting from recent infrastructure development in the Southern Plains region of Texas. The transportation of agricultural commodities on public roads imposes a number of costs on the state. These include increased road maintenance, ecological damage and traffic congestion in urban centers. The presence of these externalities can lead to inefficient market outcomes. This paper uses a linear programming model to study recent changes to the supply chain of cottonseed and lint and the associated improvement in efficiency.
The third essay is a theoretical piece that expands traditional consumer theory, allowing it to be used in new contexts. It uses this framework to explore and reconcile opposing viewpoints on the value of work. It demonstrates that the process of producing commodities is a potentially important source of utility and failing to account for this results in a misleading evaluation of welfare changes associated with a modification of manufacturing methods
Technical, Economic, and Life Cycle Assessment of Membrane-Based Air-Cooling Systems
No full textThe escalating demand for space cooling in Qatar, projected to triple by 2050, underscores the urgent need to shift from traditional refrigerant-based systems to more sustainable air-cooling technologies. This transition aims to mitigate the significant contributions to global warming, with a potential increase in global temperatures by 0.44��C by the century's end. Current research in membrane-based air-cooling systems, which combine adiabatic evaporative cooling with isothermal dehumidification, shows promise for enhanced efficiency. However, existing studies often overlook key operational energy requirements, such as the energy for low-temperature condensation, leading to underestimating system costs and efficiency. Moreover, environmental assessments of these systems frequently neglect the construction phase and criteria beyond electricity consumption. Experimentally, the focus has been on developing thin-film composites and nanocomposites on unaltered support membranes, which, despite increasing selectivity, do not significantly improve overall permeance. This limitation increases the need for membrane area, raising costs for membrane-based cooling solutions.
This research employs two primary methodologies to address the challenges of space cooling in Qatar, focusing on enhancing the efficiency and environmental sustainability of membrane dehumidification systems. A comprehensive simulation of various system configurations initially underpins these systems' exhaustive technoeconomic and environmental life-cycle assessment (LCA), benchmarked against conventional cooling technologies. Utilizing the Recipe2016 methodology for both Midpoint and Endpoint analyses, this LCA reveals a significant reduction (50-66%) in human health and ecotoxicity impacts compared to traditional AC systems while highlighting the minimal (<1%) environmental impact of the construction phase. Subsequent technoeconomic analysis identifies membrane cost as a critical factor in the viability of condenser-based membrane air-cooling systems. This underscores the necessity for membranes with a water vapor permeance (WVP) above 20,000 GPU. To address this, novel mixed matrix membranes containing hydrophilic nanomaterials into a polyethersulfone matrix were designed and fabricated using phase inversion to significantly enhance permeance without compromising mechanical stability. Among these MMM, the membrane containing 0.2 wt.% sulfonated graphene oxide with exceptional WVP of ~ 25,000, now protected by a US provisional patent, was coated with a thin active Pebax layer to enhance water vapor air selectivity. Economic assessment based on this SGO-TFN revealed a minimum loss of 15% saving in the total annualized relative to conventional systems, with a 60% increase in equipment manufacturing costs
Posttraumatic Stress, Alcohol Use, and Alcohol Use Motives Among Latina Survivors of Interpersonal Trauma: Examining Associations with Anxiety Sensitivity and Distress Tolerance
No full textHazardous alcohol use, interpersonal trauma, and posttraumatic stress disorder (PTSD) symptomatology are prevalent among college students, especially women who identify as Hispanic/Latinx. However, a dearth of literature has focused on alcohol use and PTSD relations among Hispanic/Latinx college student women, specifically. Thus, research is needed to investigate malleable transdiagnostic psychological factors involved in PTSD symptoms and alcohol use and motivations for alcohol use among Hispanic/Latinx students to inform culturally-tailored, evidence-based interventions. A growing body of literature has demonstrated that anxiety sensitivity (i.e., fear of anxiety-related bodily sensations) and distress tolerance (i.e., ability to tolerate negative emotional states) are two malleable transdiagnostic mechanisms with relevance to both alcohol use and PTSD. The current project examined, among 288 Hispanic/Latina college students (Mage = 23.3, SD = 5.4) with interpersonal trauma histories, the indirect effects of PTSD symptom severity on (1) alcohol use and (2) alcohol use motives (i.e., coping, conformity, enhancement, social) through distress tolerance and anxiety sensitivity, evaluated as parallel statistical mediators. Covariates included subjective social status, country of origin, and trauma load. Results revealed anxiety sensitivity, but not distress tolerance, mediated the link between PTSD symptom severity and a) alcohol use severity; b) conformity motives for alcohol use; and c) social motives for alcohol use. Further, PTSD symptom severity was associated with coping motives for alcohol use via both anxiety sensitivity and distress tolerance. This line of research has the potential to inform and advance culturally-informed literature focused on factors that may impact co-occurring PTSD symptoms and alcohol use among an understudied population
Theory and Applications of Mixed-Integer Fractional Programming
No full textThis dissertation is focusing on developing theoretical and practical results for a class of mixed-integer problems with fractional objectives. We introduce the generalized clique relaxation models with fractional objectives, namely the maximum ratio s-plex problem and the maximum ratio s-defective clique problem. We establish complexity results, describe solution methods, as well as introduce valid inequalities that are shown to substantially improve the performance of the proposed formulations. Then we we discuss the application of Bron-Kerbosh algorithm for solving fractional clique relaxation problems. We develop a new efficient combinatorial approach best suitable for sparse graphs. Finally, propose an extension of knapsack problems with fractional objectives arising from applications in service systems design and facility location problems with congestion
Improving the Ability of Activity Recognition Systems to Detect Activities of Daily Living Performed In-the-Wild
No full textFailing to keep track of the performance of activities of daily living (ADLs) can lead to adverse health outcomes for people with health concerns. However, current recommended practices for keeping track are tedious and burdensome, making it easy for people to forget or stop managing their health. Using activity recognition systems to automatically detect and record ADL performance would address this issue, but most works in activity recognition focus on controlled or semi-naturalistic data in contrast to real world, in-the-wild data. As such, real world ADL recognition remains an open problem. Specifically, real world ADL recognition requires tackling several fundamental challenges for machine learning systems, and it is unclear if existing approaches would be robust to these challenges. We expect that semi-naturalistic data does not capture the diversity of all of the everyday activities such a system would encounter and that robust performance requires using in-the-wild data.
In this work, we focus on quantifying the challenges associated with in-the-wild settings and investigating the design of in-the-wild ADL recognition systems. To achieve these goals, we conduct a series of analyses and machine learning experiments on two ADL datasets, one semi-naturalistic and one in-the-wild. First, we measure the class imbalance, interpersonal variability, and pairwise class overlap to motivate the difficulty of recognizing in-the-wild data. Second, we demonstrate the importance of training on negative samples, showing that training on NULL data results in more robust models than using unknown class rejection. Third, we investigate the design of in-the-wild ADL recognition systems, exploring both classical and deep learning methods as well as models with varying levels of context of the user���s hands. In doing so, we develop a recognition system that can recognize several ADLs with high event-based recall and precision with only the context of the dominant hand. These efforts represent a thorough investigation of a challenging open problem in human activity recognition. The results and insights serve as a meaningful step forward toward making robust in-the-wild ADL recognition a reality in order to make it easier for people to manage their health
An Exploration of Theoretical and Methodological Typologies of Faith-Based Health Interventions
No full textChronic diseases and conditions continue to pose a significant public health challenge in the United States, affecting a large portion of the population. Unhealthy lifestyle behaviors contribute significantly to chronic disease development, emphasizing the need for effective multi-level health promotion and prevention strategies. Faith communities have emerged as key community partners in addressing health disparities and promoting public health initiatives, particularly concerning chronic diseases. However, despite their commendable efforts, there needs to be more clarity and standardization in defining and operationalizing faith-based health interventions. This conceptual ambiguity hinders the development of evidence-based interventions and limits their effectiveness. This study aims to address the need for more clarity within faith-based health interventions (FBHIs) pertaining to 1) definitions and operationalization, 2) intervention typologies, and 3) conceptual frameworks, guidelines, and models. This systematic review explores the current state of research, implementation, and evaluation of faith-based health intervention typologies and methodologies. Additionally, the theory utilization quality scale (TQS) and methodological utilization quality scale (MQS) were used to assess articles in this review. This review synthesized n=27 articles and highlighted a need for standardized terminology and conceptual frameworks in FBHIs, diverse participant samples, rigorous methodologies, and systematic analyses. While many interventions show promise in improving health outcomes, mixed findings underscore the necessity for robust research designs. The dissertation proposes a faith-based health intervention model and checklist for researchers and practitioners to address current gaps and leverage evidence-based and practice-based approaches