Plasma-Actuated Flow Control of Hypersonic Crossflow-Induced Boundary-Layer Transition

Yates, Harrison B ProQuest Dissertations & Theses University of Notr 2019 해외박사(DDOD)

The purpose of this research was to control crossflow-induced boundary-layer transition on a cone at angle of attack in hypersonic quiet flow. A 7° half-angle cone model with interchangeable nosetips was designed and fabricated from stainless steel, polyether ether ketone (PEEK), and Macor. Transition was characterized using infrared thermography and Kulite pressure transducers in the Boeing/AFOSR Mach-6 Quiet Tunnel at Purdue University. A plasma-based active flow-control system was used to control the transition location of the stationary crossflow waves, which manifested themselves as hot streaks on the cone. The transition location was accelerated by critical forcing (where the actuator wavenumber equals the wavenumber of naturally largest amplitude waves) and delayed by subcritical forcing (where the actuator wavenumber is larger than the natural waves). The disturbance wavenumber input of the plasma actuators was observed downstream on the model for many of the plasma-on runs, demonstrating that the plasma actuators introduced discrete forcing into the flow. The precise locations of the hot streaks varied for different nosetips, presumably due to differences in their microscale roughness. The experimental data were used to inform an improved stability analysis. Stationary crossflow vortex N-factors were calculated over the surface of a yawed circular cone using computationally predicted and experimentally observed wavenumber distributions. A wavelet analysis was conducted on the experimental surface heat-flux data to construct a spatial mapping of the local largest amplitude wavenumbers of the stationary crossflow waves, which were between 40 and 80 per circumference. Significant spatial variation was observed. The results from the wavelet analysis informed the stability analysis. The computed integration marching directions demonstrated good agreement with the experimentally observed paths. N-factors were calculated by integrating the local amplification rate corresponding to the most amplified experimental wavenumbers. The calculations were repeated based on non-dimensional computationally varying wavenumber ratios, which were dimensionalized by the experimental data. The computed N-factors showed good agreement between the two techniques. N-factors were also computed using the computationally predicted most unstable wavenumbers. The results showed decreased agreement with the other two cases, suggesting that this assumption does not properly model the crossflow transition process.

The Reasons Management Framework

Lau, Ting Cho ProQuest Dissertations & Theses University of Notr 2019 해외박사(DDOD)

In the dissertation, I explore a notion of practical deliberation that sits at the mean between two extremes: (a) reality does not favor any way of acting, and (b) reality requires us to adopt specific projects and values. To be specific, I motivate the following two theses from Lord (2018):Possessed Reasons: What we ought to do is determined by our possessed reasonsPossession: What it is for agent A to possess reason R to φ provided by fact F is for A to be in a position to manifest knowledge about how to use R to φ.I then defend the following thesis:Management: An agent is rationally permitted, in some instances, to control which reasons she possesses by controlling whether she satisfies the conditions for their possession. These three theses, taken together, constitute the Reasons Management Framework (RMF).In the dissertation, I motivate and develop the implications of RMF. I make the case that RMF allows us to adopt a broadly realist view of normativity that is familiar to us: it (a) acknowledges the existence of objective reasons and values, but also (b) gives agents flexibility in deciding which objective reasons and values will play a role in determining what they ought to do.The dissertation is organized into four chapters. First, I prepare the way for RMF by defending normative realism against a recent anti-realist objection. Second, I motivate RMF and consider its implications. Third, I consider the objection that RMF allows for moral rationalization and reflect on the rational justification for reasons management broadly speaking. Finally, I apply RMF to the context of romantic love and the Trading Up Problem.

A Non-Invasive Patient-Specific Modeling Approach for Predicting Group II Pulmonary Hypertension as a Clinical Indicator of Diastolic Heart Failure for Patients with Uncertain Clinical Data

Harrod, Karlyn Karissa ProQuest Dissertations & Theses University of Notr 2022 해외박사(DDOD)

Diastolic dysfunction (sometimes referred to as diastolic heart failure or heart failure with preserved ejection fraction, HFpEF) is a common pathology occurring in about one-third of patients affected by heart failure. However, this condition may not be associated with a marked decrease in cardiac output or systemic pressure and therefore is more challenging to diagnose than its systolic counterpart. Compromised relaxation or increased stiffness of the left ventricle leads to elevated pressures in the pulmonary arteries. This condition is classified as Group II pulmonary hypertension or pulmonary hypertension due to left heart disease, one of the five classification sub-groups of hypertension \\cite{simonneau2013updated}. In addition, the left ventricle's impaired relaxation or stiffness may increase the right ventricular afterload, which can lead to right ventricular failure. Therefore, elevated pulmonary pressures are an important clinical indicator of diastolic heart failure and significantly correlate with associated mortality. However, accurate measurements of this quantity are typically obtained through invasive cardiac catheterization. Moreover, the measurements are usually only obtained after the onset of symptoms. This thesis uses the hemodynamic consistency of a differential-algebraic circulation model to predict pulmonary pressures in adult patients from other, possibly non-invasive, clinical data. We investigate several aspects of the problem, including the ability of model outputs to represent a sufficiently broad pathologic spectrum, the identifiability of the model's parameters, and the accuracy of the predicted pulmonary pressures. We also find that a classifier using the assimilated model parameters as features is free from problems that arise from missing data and can detect pulmonary hypertension with sufficiently high accuracy. For a cohort of 82 patients suffering from various degrees of heart failure severity, we show that systolic, diastolic, and wedge pulmonary pressures can be estimated on average within 8, 6, and 6 mmHg, respectively. We also show that, in general, increased data availability leads to improved predictions. An introduction to the cardiovascular system, its components, and the underlying mathematical relationships used to describe blood flow are contained in Chapter 1. A review of the clinical motivation for the aforementioned work is contained in Chapter 2, along with a review of heart failure and hypertension. An overview of the origins and history of modeling of the cardiovascular system, including lumped parameter models, is covered in Chapter 3. In addition, Chapter 3 includes the formulation of the 0D hemodynamic circulation model used throughout the body of this thesis. Chapter 4 is devoted to both the methodologies used for the model and the model's parameters. It introduces the statistical model, tuning of the model, and the use of Bayesian computation methods. This chapter also covers parameter estimation and optimization methods in addition to parameter sensitivity and identifiability analysis techniques. Finally, the chapter ends with an overview of the classification method implemented in this thesis, followed by a brief introduction to the computational framework utilized throughout this thesis. The final chapter, Chapter 5, introduces the two data set and presents the main research questions. The first two research questions presented focus on validating the model formulation by accessing the model's physiological admissibility and the model's ability to represent systolic and diastolic dysfunction mechanisms. The final questions address the sensitivity and identifiability analysis of the model parameters, the prediction of the pulmonary pressures, the identification of the most impactful clinical measurements, and finally, the use of patient-specific trained model parameters to identify pulmonary hypertension in patients of varying data availability. The results for each research question directly follow the questions. Finally, this chapter ends with a conclusion resulting from the implications drawn from each of the results.

Combining Hydrology with Data Science and Economics to Address Modern Water Resources Challenges

Mullen, Connor ProQuest Dissertations & Theses University of Notr 2022 해외박사(DDOD)

Historically, hydrology was born out of the necessity to inform engineering (e.g., river engineering, water supply and urban drainage). It developed further as people noticed that water is at the intersection of many systems on the planet. Proper attribution of hydrologic change requires sufficient data, understanding of landscape processes, and often the construction of models at a range of scales. The world is rapidly changing due to climate and anthropogenic drivers that present new issues that need to be addressed. In that context, modern hydrology faces a range of fundamental and well-established challenges, three of which are addressed in this dissertation: (i) data scarcity, with only a few hydrological systems having in situ data globally, (ii) heterogeneity of hydrologic processes and variables and the need to provide high-resolution hydrologic information and data at local scales, while also generating consistent information and data at landscape scale, and (iii) feedback within water resource systems and determining cause and effect in observational data (e.g. feedback loops between human and natural systems). Recently, there have been promising opportunities to address these challenges through combining hydrology with complimentary (similarity in the approaches and methods) disciplines and is the focus of this dissertation.Each chapter of this dissertation illustrates the promise of interdisciplinary approaches combining hydrology with other fields of study to address important challenges in the hydrologic sciences. General insight can be drawn from each study in this dissertation to address one or more of the three fundamental challenges. In the first study, I address the challenges of clouds and missing images in the determining of water extent in remote sensing. I found that the approach was robust to major deviations from most of its underlying assumptions. In the second study, I address the challenge of modelling wetlandscape processes, both hydrological and ecological, without full landscape in situ data. I investigate the potential to use water extent information from satellite imagery to calibrate landscape-scale process-based hydrological models. Compared to in situ observation of instrumented wetlands, satellite imagery encounters challenges in detecting water but it is not subject to the sampling errors associated with modelling arbitrary subsets of a heterogeneous landscape. In the third study, I address the challenge of modeling the common pool depletion of shared aquifers. I couple human decisions (game theory) with hydrogeological models of groundwater to exhibit user incentives to overpump in shared aquifers. The coupled model is analytically tractable, which allows it to be suitably non-dimensionalized. This, in turn, allowed it to be leveraged to draw generalized insights on how economic differences between aquifer users interact with their spatial configuration to determine incentives to over-pump.

Bias in Face Recognition: From Causes to Mitigation

Albiero, Vitor ProQuest Dissertations & Theses University of Notr 2022 해외박사(DDOD)

Face recognition technology has recently become a topic of controversy over concern about possible bias across demographics. However, media coverage has not always been concerned with an accurate understanding of face recognition technology and the underlying causes for the observed bias. In this dissertation, we perform a detailed examination of face recognition bias. First, we explore the problem by looking into differences in face recognition accuracy across race, where our experiments show a mixed set of results for African-American and Caucasians, but African-Americans usually have higher false match rates (FMR) and lower false non-match rates (FNMR). We investigate the speculation that darker skin tones somehow causes higher FMR, but our experiments show no evidence to support this speculation. Second, we investigate face recognition accuracy across age groups. Varying face recognition accuracy across age ranges receives almost no attention compared to varying accuracy across race or gender. Also, it is the most difficult demographic aspect to investigate, since datasets with correct meta-data and enough data across middle and older age ranges are required for investigation. Using the best available dataset, we present an extensive analysis of face recognition accuracy across age groups, where our results show that there is a drop in performance when subjects are older. Third, we investigate face recognition bias across gender, which is the main focus of this dissertation. Our results show consensus higher FMR and higher FNMR for women ("gender-gap"), agreeing with previous works. We investigate various speculated causes for this gender gap, and find face visibility to be the main cause for females' higher FNMR. We speculate that differences in face shape similarity are a main cause for females' higher FMR. We arrive at this speculation for the FMR because other possible causes that were investigated did not result in a major difference in FMR. We also show results of extensive experiments on the correlation of gender balance in training datasets and accuracy on test sets. Using face segmentation methods, we show that face regions have different effects across demographics, suggesting that matchers should weight face regions differently. Moreover, motivated by the apparent media confusion between facial analytics (such as ``gender from face'') and face recognition, we present an evaluation of the correlation of gender classification errors and face recognition errors. Finally, we investigate the effect that the training margin during network training has on gender bias. We train models with different margins for each gender, and analyze the effect that this has on training and testing accuracy. When margins are the same for both genders, we show that the gender-gap is present in both training and test datasets. However, when margins are different during training, with females given a larger margin than males, the gender-gap in test accuracy is reduced.

Investigation of Range-based and Range-free Radio Frequency-based Localization Techniques

Golestanian, Mehdi ProQuest Dissertations & Theses University of Notr 2019 해외박사(DDOD)

Without any doubt, location is one of the most important and key requirements of most mobile and wireless networks. Localization is necessary to provide a physical context to sensor readings for services such as intrusion detection, inventory, and supply chain management. It is also a fundamental task for sensor network services such as geographic routing and coverage area management. Over the last few decades, localization technologies have undergone significant progress and they now play a crucial role for many locations and context-aware services and applications such as navigation, robotics, patient monitoring, and emergency response systems. This work studies the topic of localization using Radio-Frequency (RF) technologies for several applications. We study the localization concept in the context of range-free, range-based and machine learning algorithms, presented in three chapters of this thesis.We aim at addressing the heterogeneity problem in Range-free localization algorithms where the non-uniform distribution of nodes and different transmission power are the main cause of heterogeneity. Our two proposed approaches improve the localization accuracy in heterogeneous networks using a joint route discovery and localization approach, and elliptical range estimation algorithm. The joint route discovery and localization algorithm deploys a variable-range route discovery that improves the connectivity of nodes in a heterogeneous network and then performs a hop-length estimation for localization of nodes. In our other approach, we introduced an elliptical range-estimation, which is based on studying the shape and geometry of path between nodes in a heterogeneous network. The simulation results confirm the improvement of localization accuracy compared with popular range-free methods such as DV-hop, ZBLM, and EZBLM.In the next part of the thesis, we investigate the main challenges and problems of Range-based algorithms using RF technologies such as BLE, WiFi, XBee, and DSRC. Mainly the fading characteristics of the wireless channel, i.e. path loss exponent, multipath fading, and shadowing, with unknown distribution, are themain issues with rangebased methods. In the third chapter, we introduce several approaches to address these issues. First, we investigate the diversity concept to reduce the impact of the multipath fading on the received signal strength (RSS). We show that by using time diversity or transmitting beacon signals at different time slots, spatial diversity or using multiple antennas for transmitting beacons, and frequency diversity or transmitting beacons at different channels or bands, we can reduce the impact of multipath fading. We investigate these concepts by experimentation using BLE andWiFi in indoor environments. The experiment results demonstrate an improvement in ranging by leveraging the time diversity and averaging over the RSS samples at different time slots. Similar to time diversity, having multiple copies of a signal from different antennas can improve the ranging accuracy. The ranging accuracy significantly improves by using the frequency diversity by deploying WiFi beacons working in 2.4GHz and 5GHz bands. More specifically, a submeter accuracy was achieved using 5GHz for short distances, and for larger distances, 2.4GHz showed more accurate ranging. Besides the diversity concept, we introduce an algorithm based on multi-range beaconing where beacons are allowed to change their transmission power and range. We have shown that the multi-range beaconing is robust against multipath fading and can estimate the distance accurately even in the presence of deep fading. As the last part of the range-based localization algorithm, we focus on the application of RF technologies such as DSRC for ranging and localization in vehicular environments. Due to the dynamic of the environment, multipath fading and shadowing can significantly degrade the ranging performance. To address the localization accuracy, we introduced parametric and non-parametric (learning-based) approaches to estimating the distance and noise profile. The non-parametric algorithm leverages a learning approach and the mobility of devices (e.g. the vehicle) for estimating the distance of a transmitter and receiver device. The results demonstrate that by having enough RSS samples, we can reach an average ranging error of 1 meter. One of the main concern of this approach is the convergence time. We address this issue by using a filtering scheme and trilateration. Furthermore, we introduced a novel parametric algorithm to estimate the path loss exponent and shadowing distribution in a network of beacons using centroid localization algorithm. For the parametric approach, we also studied different parameters such as beacon configuration having direct impact on localization accuracy.The last chapter of the thesis leverages the power of artificial intelligence and machine learning frameworks as a new and interesting approach to solve the localization problem. We present a survey of existing work in this area and propose a supervised learning model for RSS-based localization in vehicular environments. The performance of the proposed approach shows a significant improvement in localization accuracy and convergence time for ranging.

Adsorbate induced reconstructions of metal surfaces

Michalka, Joseph R ProQuest Dissertations & Theses University of Notr 2016 해외박사(DDOD)

In this dissertation I present work on the modeling of adsorbate-metal interactions with a specific focus on carbon monoxide (CO) induced restructuring of platinum stepped surfaces. (Abstract shortened by ProQuest.).

Exploring the Structure-property Relationships of Linear and Crosslinked Poly(ethylene Oxide) Polymer Membranes for Gas Separations

Kline, Gregory K ProQuest Dissertations & Theses University of Notr 2018 해외박사(DDOD)

The development of polymeric materials suitable for gas separation membrane applications is discussed in this dissertation. Compared to conventional gas separation systems, such as absorption, gas separation membrane systems are inherently smaller in size and easier to operate, and potentially, more economically viable. Membranes with high permeability (for a high gas throughput) and adequate selectivity (ability to separate a given gas from a mixture) are desired. However, due to the natural properties of polymeric materials, generally, membranes with high permeabilities unfortunately operate with low selectivities and vice versa. To combat this natural trade-off, to produce materials with both high permeabilities and sufficiently high selectivities, the chemical and physical properties of polymeric materials must be strategically designed.The majority of this work explores strategies for incorporating rubbery poly(ethylene oxide) (PEO) into gas separation membranes. PEO is a promising material for CO2 related-separations due to its high solubility selectively for CO2 and its high diffusivity, which together, give PEO-based materials excellent CO2-separation performance. However, pure PEO is mechanically weak and suffers from high crystallinity which prevent its use in gas separation membranes. Therefore, this work explores strategies to incorporate PEO into copolymers, into crosslinked networks, and into semi-interpenetrating networks (s-IPNs). These systems have demonstrated improved mechanical properties, mitigated PEO crystallinity, and highly promising CO2-related gas separation performance.

In-situ studies of catalysts for understanding of catalytic reactions

Nguyen, Luan Thanh ProQuest Dissertations & Theses University of Notr 2016 해외박사(DDOD)

Catalysis plays important roles in society. In particular, heterogeneous catalysis has proven to be the cornerstone of chemical and energy transformation, e.g. petroleum refining, chemical production, and plays a significant role in the development of new technology for pollution control. Understanding of catalysis requires in-situ/operando studies of catalysts in their working condition. This requirement is non-trivial hence there exist a "materials gap" and "pressure gap" in fundamental studies of heterogeneous catalysis. Attempt to narrow this "pressure gap" is described in this thesis. Through development of in-situ surface characterization techniques: high temperature near ambient scanning tunneling microscopy (HT-NAP STM) and ambient pressure X-ray photoelectron spectroscopy (AP-XPS), in-situ studies of catalysts are realized. Examples of insitu studies using these techniques, including visualization and surface chemistry characterization of model catalysts (mono-metalic and alloy) at atomic level under CO environment, and during CO oxidation, are described. The results reveal a dynamic atomic packing at the step edge of the Pt(111) surface which suggests restructuring of step edges of metal catalysts under reaction conditions and during catalysis. For Pt/Cu/Pt(111) near-surface alloy, In situ studies using HP-STM suggest formation of nanoclusters-like features at a relatively high pressure of CO (2 Torr) at room temperature with the restructured surface being active for CO oxidation at room temperature. In addition, Rh(110) surface restructures from the (1 x 2) phase to (1 x 1) phase under CO oxidation environment at Torr regime. These results overall demonstrate the necessity of in-situ surface characterization of catalysts for comprehensive understanding of heterogeneous catalysis.

Self-propagating High-temperature Reactions in Heterogeneous Reactive Nanocomposites

Pauls, Joshua M ProQuest Dissertations & Theses University of Notr 2019 해외박사(DDOD)

Self-propagating high temperature reactions are of interest due to the potential for fabrication of unique materials, including metastable phases and super refractory alloys and ceramics. Reactions occurring in the TiN/B, Ni/Al, and Ti/BN systems were investigated and characterized using mechanically-induced nanostructured composites through high energy ball milling (HEBM). A wide range of diagnostic equipment was applied, and experimental techniques were developed to characterize the reaction mechanisms, reaction kinetics, and diffusion behavior of these systems with particular focus on TiN/B, which was investigated here for the first time.TiN/B belongs to a subset of combustion reactions, known as solid flame systems, where the adiabatic combustion temperature does not exceed any phase transition temperature or eutectic point of the reactants, intermediates, or products, but for which no ternary phases or substantial solid solutions form. In situ time-resolved x-ray diffraction, infrared video recording, and thermal gravimetric analysis/differential scanning calorimetry were employed to determine the reaction pathway of this system, which is one of the primary goals of this dissertation. Cubic boron nitride, the high-pressure BN polymorph, was produced by shock-induced reactive synthesis using the TiN/B system.High speed microscope video analysis identified two propagation modes for Ni/Al nanostructured composites: a micro-heterogeneous mode limited by heat transfer between composite particles, and a nano-quasi-homogeneous mode limited by reaction kinetics within single particles. An in situ TEM technique that couples energy dispersive spectroscopy mapping with a heated transmission electron microscope stage was used to measure diffusivity in the temperature range of 623 K – 723 K, which is relevant for understanding solid-state ignition behavior of Ni-Al.The development of mechanical processing methods for fabricating nanostructured composites presents an opportunity to prepare novel reactive mixtures from systems for which thermodynamic calculations indicate substantial heats of formation but were not previously identified as feasible due to solid-solid diffusion limitations.