Theses and Dissertations from UMD
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New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a give thesis/dissertation in DRUM
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Item NUMERICAL MODELING AND EXPERIMENTAL STUDY OF A NOVEL METAL-POLYMER COMPOSITE HEAT EXCHANGER FOR SENSIBLE AND LATENT THERMAL ENERGY STORAGE APPLICATIONS(2022) KAILKHURA, GARGI; Ohadi, Michael; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Compact, lightweight, and low-cost heat exchangers (HXs) have the potential to improve efficiencies and save power and carbon foot print in a wide array of applications. The present study investigates an entirely additively-manufactured novel metal-polymer composite heat exchanger, enabled by an innovative and patented cross-media thermal exchange approach, which yields an effective thermal conductivity of 130 W/m-K for the heat exchanger. This record-high thermal conductivity is more than an order of magnitude higher that the previously reported thermal conductivity for polymer and polymer composite HXs. Drawing on the concept of external flow over the tube banks, the proposed HX features a staggered arrangement of fins. This class of HXs are often used for gas-to-liquid sensible cooling applications. However, they can also be designed for latent thermal energy storage applications by employing low-cost and high energy-storage-density phase change materials (PCMs) such as salt-hydrates and alike in either the hot or cold side of the HX, depending on the application. An extensive literature survey on tube banks shows that, though numerous correlations exist in the literature for flow over tube banks, these correlations usually fall outside the range for the current HX design for low-Reynolds number applications (Re<100). Furthermore, the PCM models present in the literature are either very challenging to solve analytically or are computationally expensive. Thus, the dissertation emphasizes developing computationally-efficient and robust numerical models for sensible and latent cooling applications.The numerical models compute the overall thermal and pressure-drop performance metrics based on segment-level modeling, and they integrate the performance parameters such as Euler, Nusselt numbers, or latent thermal energy with the entire HX analytically, thus significantly reducing the computational cost. For steady-state sensible thermal energy storage applications, a realistic 3D CFD-based modeling approach is used, based on the actual dimensions of the printed HXs rather than a traditional 2D CFD-based model. It also resolves the issues due to the 3D velocity field which aren’t captured in the 2D CFD models, and are particularly important for HXs utilizing narrow/micro channels. This modeling approach is used to obtain optimized HXs for case examples of 5-40 kW air-conditioning applications and 250-W electronic cooling applications for nominal operating and flow conditions. The 250-W unit is further validated experimentally and is observed to be within 17% for waterside pressure drop, 11% for airside pressure drop, and within 8% for thermal resistance when compared against experimental measurements. For transient latent thermal storage applications, an analytical-based 1D reduced order model (ROM) for segment-level modeling is developed based on 1D radial conduction inside the PCM. It is numerically validated with commercial CFD tools to within 10% except for cases where axial conduction in PCM is possible due to the high resistance of wire embedded in the PCM. The 1D ROM is used in optimizing a 1.44-MJ TES unit for peak-load building cooling applications and a 19.2-kJ HX for pulsed-power cooling applications. The 1.44-MJ unit is experimentally tested and observed to be within 17% for the melting time of complete PCM and about 8% for the freezing time of the complete PCM. Lastly, another novel and hybrid thermal energy storage design is formulated, which utilizes two different PCMs: shape memory alloys (SMAs) instead of metal wires and salt-hydrates contained inside polymer channels similar to the reference designs. Besides the thermal energy storage design, a novel methodology on Wilson plot for finned surfaces on both fluid-sides is introduced, which is first of its kind in the literature. Ongoing and future work in both these areas is also recommended in the final chapter of the thesis.Item Taking Perspective: A Theory of Prejudice Reduction and Political Attitudes(2021) Safarpour, Alauna C.; Hanmer, Michael; Banks, Antoine; Government and Politics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This dissertation develops and tests Engagement, Perspective-Taking, and Re-calibration (EPR), a theory of how to reduce prejudice and its consequences on political attitudes. I theorize that an intervention that uses engagement to encourage perspective-taking reduces prejudice and re-calibrates the subject’s attribution of blame for America’s racial problems. This last step, “re-calibration,” shifts the target of blame from out-group members to the forces of racism and discrimination which alters political attitudes rooted in prejudice. I employ my theory of EPR to develop interventions to reduce anti-Black prejudice among U.S. citizens using online perspective-taking tasks. The interventions encourage participants to adopt the perspective of an African American individual who experiences racial prejudice and make choices regarding how to respond to the bias they encounter. Interventions designed according to EPR theory were evaluated in three randomized experiments in which participants completed either the perspective-taking treatment or a placebo task. I find that participation in the perspective-taking task significantly reduces multiple forms of racial prejudice including racial resentment, negative affect, and belief in anti-Black stereotypes. The largest effects were among those with the highest levels of baseline prejudice. These studies also show that reducing prejudice increases support for policies that would help African Americans, including government assistance to Blacks, additional changes to ensure racial equality, affirmative action, and reparations for slavery. Similarly, reducing prejudice increases support for the belief that Blacks are not treated fairly in American society, increases support for policing reforms, and increases support for the Black Lives Matter protests against police violence. My results demonstrate that a substantial amount of opposition to racial policies is rooted in racial animus. But neither animus nor opposition to racial policies are immutable, reducing prejudice through my technique increases support for policies to redress racial inequities. This dissertation offers two empirically evaluated interventions that may be used as low-cost bias reduction trainings to combat the rising hate-related incidents in the United States. More broadly, my results provide insight into the nature of racial prejudice and its impact on political attitudes.Item COMPUTING APPROXIMATE CUSTOMIZED RANKING(2009) Wu, Yao; Raschid, Louiqa; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)As the amount of information grows and as users become more sophisticated, ranking techniques become important building blocks to meet user needs when answering queries. PageRank is one of the most successful link-based ranking methods, which iteratively computes the importance scores for web pages based on the importance scores of incoming pages. Due to its success, PageRank has been applied in a number of applications that require customization. We address the scalability challenges for two types of customized ranking. The first challenge is to compute the ranking of a subgraph. Various Web applications focus on identifying a subgraph, such as focused crawlers and localized search engines. The second challenge is to compute online personalized ranking. Personalized search improves the quality of search results for each user. The user needs are represented by a personalized set of pages or personalized link importance in an entity relationship graph. This requires an efficient online computation. To solve the subgraph ranking problem efficiently, we estimate the ranking scores for a subgraph. We propose a framework of an exact solution (IdealRank) and an approximate solution (ApproxRank) for computing ranking on a subgraph. Both IdealRank and ApproxRank represent the set of external pages with an external node $\Lambda$ and modify the PageRank-style transition matrix with respect to $\Lambda$. The IdealRank algorithm assumes that the scores of external pages are known. We prove that the IdealRank scores for pages in the subgraph converge to the true PageRank scores. Since the PageRank-style scores of external pages may not typically be available, we propose the ApproxRank algorithm to estimate scores for the subgraph. We analyze the $L_1$ distance between IdealRank scores and ApproxRank scores of the subgraph and show that it is within a constant factor of the $L_1$ distance of the external pages. We demonstrate with real and synthetic data that ApproxRank provides a good approximation to PageRank for a variety of subgraphs. We consider online personalization using ObjectRank; it is an authority flow based ranking for entity relationship graphs. We formalize the concept of an aggregate surfer on a data graph; the surfer's behavior is controlled by multiple personalized rankings. We prove a linearity theorem over these rankings which can be used as a tool to scale this type of personalization. DataApprox uses a repository of precomputed rankings for a given set of link weights assignments. We define DataApprox as an optimization problem; it selects a subset of the precomputed rankings from the repository and produce a weighted combination of these rankings. We analyze the $L_1$ distance between the DataApprox scores and the real authority flow ranking scores and show that DataApprox has a theoretical bound. Our experiments on the DBLP data graph show that DataApprox performs well in practice and allows fast and accurate personalized authority flow ranking.