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STRENGTH OF STEEL CYLINDRICAL SHELLS UNDER COMBINED ACTIONS WITH APPLICATION TO WIND TURBINE TOWERS
No full textThis dissertation studies the strength and stability of steel cylindrical shells under combined loading conditions with a focus on their applications in wind turbine towers. Increasing tower height is crucial for accessing stronger and more consistent winds at high elevations, which can be achieved through structurally efficient designs. Turbine towers are typically constructed from thin cylindrical shells, which are highly efficient but prone to sudden instability due to their high imperfection sensitivity. As a result, predicting the actual strength of these structures is challenging, as imperfections cause failure at much lower loads than those predicted for perfect shells. This research specifically examines the buckling behavior of thin cylindrical shells subjected to combined bending and torsion, a critical load case for the upper sections of wind turbine towers that has not seen significant study to date. To address this knowledge gap, 48 lab scale tubes were tested with diameter-to-thickness (D/t) ratios and torque-to-moment (T/M) ratios seen in real wind turbine towers. The structural response of the tubes, including failure loads and buckled shapes were recorded. To gain insight into the imperfection sensitivity of cylindrical shells, a laser scanner was used to measure geometric imperfections of each specimen before testing. Unlike traditional physical measurements, laser scans offer significantly more geometric data, but current design codes provide minimal guidance on how to convert this data into fabrication quality classes for strength predictions. This study provides one possible method for processing laser scans and discusses other possible approaches. Additionally, advanced finite element analysis (FEA) of the steel cylinders from the experimental test were also conducted for comparison of the test results and to study more load conditions. Models of varying complexities were developed to capture different aspects of shell behavior, from basic linear buckling analyses to highly detailed models that account for nonlinear behavior and geometric imperfections
METHODS FOR CAUSAL INFERENCE USING EXPERIMENTAL AND OBSERVATIONAL DATA
Get PDFThe recent development and adoption of electronic healthcare record systems has provided researchers a rich and detailed of data for researchers interested in studying aspects of clinical care. These observational datasets are well suited to supporting exploratory analyses and answering causal questions quickly. At the same time, randomized trials are still the gold standard for assessing causality. However, these interventional datasets are expensive to collect and are often collected with a single question in mind. Historically, methods have been developed to address the analysis of either observational or interventional data, but more recently there have been moves to analyze them jointly, or to use ideas and techniques developed for one context to create new methods in the other.
In this dissertation, I explore methods for causal inference that lie at the intersection of observational and interventional data. First, I provide novel algorithms for data fusion, an endeavor that seeks to combine various observational and interventional datasets to identify causal estimands. I provide improvements over existing algorithms by allowing datasets which have
unobserved variables or selection bias. I also introduce a framework of systematic selection in data
fusion, which allows for the fact that patients might enter an observational or interventional dataset not completely at random. Next, I present a method motivated by a randomized trial of drug
abuse therapies, in which patients often fail to report for testing. I propose a model for this behavior which is computationally tractable, and derive identification the relevant causal estimands using technology originally developed for observational datasets. Finally, I introduce a package for graphical causal inference, Ananke. Named for the Greek goddess of necessity, Ananke provides a suite of tools for interested analysts to more easily conduct end-to-end analyses by providing implementations of various algorithms in causal inference, including
algorithms in this dissertation
Uncertainty Quantification for State Estimates in the Extended Kalman Filter
Get PDFThe extended Kalman filter (EKF) is a widely used state estimation technique, generalizing the classical Kalman filter (KF) to nonlinear problem settings. The generic EKF algorithm considers first-order linearization of the system and inherits the KF iterations by using corresponding Jacobian matrices of nonlinear transformations. Moreover, several variants have been proposed either to reduce the computational cost—such as the constant-gain Kalman filter (CGKF) and gain-scheduled or deterministic-gain Kalman filter (DGKF)—or to enhance the estimation accuracy, such as the second-order EKF and the iterated EKF. Due to accumulated linearization error, the EKF is regarded as a suboptimal filter in terms of minimizing the mean square error (MSE), and the uncertainty quantification for the algorithm and its variants remains largely unresolved to date.
In this work, we focus on the uncertainty quantification of the generic EKF algorithm and its constant-gain variant. Specifically, we provide an analysis addressing two questions: First, under what conditions is the algorithm stable? This relates to the boundedness of the state estimation error (the difference between the unknown state and its estimate). Second, how can we construct uncertainty bounds for the error term? A popular practice is to approximate the error distribution by a Gaussian distribution with zero mean and the approximate error covariance matrix updated in the EKF iteration. However, due to the iterated nonlinear transformations and random noise, there is no guarantee that the approximated covariance is close to the true error covariance or that the real distribution of the error is Gaussian. Therefore, more theoretically justified methods are needed for calculating uncertainty bounds for the EKF and its variants.
In Chapter 1, we introduce the general nonlinear state estimation problem in the discrete-time state space model and review the Kalman-type filtering algorithms discussed in this work. Then, we discuss uncertainty quantification in the context of the filtering problem and highlight key literature.
In Chapter 2, we focus on the stochastic stability of a class of filtering algorithms, including the EKF, DGKF, and CGKF. We identify sufficient conditions for ensuring that the estimation error is bounded in mean square and almost surely using stochastic Lyapunov stability theory. We also examine the boundedness of the gain matrix and the approximate error covariance matrix for the EKF.
In Chapter 3, we propose a method to construct norm-wise uncertainty bounds for the aforementioned generic nonlinear filter in linear-measurement systems. Specifically, we calculate Chernoff inequality-based confidence bounds using an instrumental scalar-valued stochastic process.
In Chapter 4, we derive component-wise uncertainty bounds for the CGKF in linear-measurement systems. We analyze the ergodicity of the CGKF process and approximate the unknown filtering error process using a deterministic-coefficient first-order autoregressive (AR1) surrogate process.
In Chapter 5, we propose methods to calculate component-wise uncertainty bounds for the EKF error in three scenarios. For scalar models with linear measurements, we use stochastic comparison analysis to construct confidence intervals. For linear-measurement multivariate models, we demonstrate the distributional convergence of the EKF process and approximate its error with the CGKF error process. Lastly, for fully nonlinear multivariate models, we study the distributional convergence of the EKF error to the KF error process as model nonlinearity vanishes
MINERALS, AI, & THE AMERICAS: CRITICAL COMPONENTS OF THE U.S.-PRC COMPETITION
Get PDFThis paper considers the competition between the U.S. and the People’s Republic of China (PRC). Recent years have seen the development of a loose “China consensus” in Washington. This analysis takes this new paradigm as a baseline and seeks to add a contribution to academic and policy discussions through three case studies. The intersection of critical technologies and global supply chains is the focus of the analysis, and support is rendered for the general thesis of derisking. Chapter 1 explores China’s dominance in critical minerals via two historical case studies: the strategies employed by the War Production Board (WPD) in World War II to mitigate critical material shortages as well as the oil crises of the 1970s. Chapter 2 explores the AI competition between the U.S. and PRC by analyzing China’s AI progress as well as reviewing strategy proposals from policy experts and concludes with several recommendations for policymakers. Chapter 3 discusses U.S.-PRC competition in Latin American and the Caribbean, setting forth the view that the region offers a number of potential economic opportunities to both counter PRC influence as well as derisk critical supply chains. The paper concludes by proffering three overall conclusions: (1) so-called weaponized interdependence now characterizes the relationship between the U.S. and PRC, and policymakers must act accordingly; (2) protective economic policies will be required over the course of the U.S.-PRC competition, but they should be targeted and implemented in concert with allies whenever possible; (3) policymakers must be prepared to iteratively revise and update rules and procedures as necessary
Politicians vs. The Press: Analyzing The Shifting Relationship Between Congress And The Media In The Digital Age
Get PDFAlthough conventional wisdom has it that journalists play a critical role in American democracy, there are growing questions about whether the traditional press––coupled with new social and alternative platforms––ultimately helps or harms legislative processes. This thesis explores the dynamic relationship between the media and Congress while examining what it could portend for both the U.S. government and civil society. News organizations have long provided a public service by sharing information about current events and issues that help keep citizens informed. However, few scholars have assessed how the emergence of online networks and shifting norms among lawmakers might impact congressional productivity and public perceptions. Much of the most influential work is also outdated, given the ever-evolving media landscape. By analyzing contemporary data on political polarization, legislative productivity, and confidence levels, this paper illuminates how the interplay between journalists and members of Congress has grown increasingly contentious and, in some ways, dysfunctional over the past 50 years. Additionally, the following research sheds light on the media tactics of speakers of the House––one of the most powerful jobs in Washington––revealing how methods have changed during the digital age, with varying degrees of success. These findings are important and necessary because they provide further insight into what to expect from future Congresses and media outlets while assessing potential ways to make the institutions more productive and trusted. Moving forward, it is paramount that political scientists and media observers continue to investigate the nuances of the complex and complicated linkage between the press and lawmakers, considering its sweeping implications not just for national governance but for the general population
THE ISLAMIC RESISTANCE MOVEMENT: BALANCING BETWEEN IDEOLOGY AND GOVERNANCE
Get PDFThis thesis aims to answer the research question: “How has Hamas managed to
maintain control and govern Gaza since its 2006 electoral victory under the
Israeli-imposed blockade?” I argue that the group’s success in maintaining its
governance in Gaza can be attributed to its transition from an uncompromising
ideological commitment to the pursuit of armed resistance as the sole means of
attaining its desired outcome––namely, the liberation of historical Palestine and the
Islamization of Palestinian society––to a pragmatic approach to both the Palestinian
political framework, as an elected party with a political mandate, and to Israel.
Ultimately, Hamas was able to transform itself from an Islamist movement largely
uninterested in political participation to a Palestinian Islamist political party able to
challenge the PLO for dominance over Palestinian politics and capture votes in both
Gaza and the West Bank, as well as casting itself as the leader of the Palestinian quest
to achieve an independent state
Investigating the Dynamics of Immunologic and Pharmacologic Control of SIV Infection
No full textElite controllers are the exceedingly rare population of individuals that control HIV infection in the absence of pharmacologic intervention. These individuals represent hope for a functional cure of HIV infection. Because of logistical challenges, elite control is difficult to study, particularly in the early phase of infection when control is established. We used a unique animal model, the infection of pigtail macaques with a barcoded SIVmac239ΔGY virus, to evaluate quantitative and qualitative characteristics of infected cells during the first 66 weeks of infection. Our cohort included 6 animals that controlled the virus as expected, and 3 animals that maintained high viral loads and were placed on ART. We leveraged this unique opportunity to compare immunologic, elite control with pharmacologic, ART-mediated control. Intact SIV proviruses in blood and peripheral lymph nodes decayed with biexponential kinetics that were generally similar between controllers and non-controllers, indicating the robust nature of immunologic control. These decay rates were slower than those seen for the decay of the plasma virus. Longitudinal env sequencing indicated a dearth of diversity and lack of ongoing replication in controllers. In non-controllers, no mutations occurred that were obviously causal for the lack of control. This work represents the most comprehensive analysis of an NHP model of elite control to date and provides insights into the establishment and dynamics of control that are impossible to observe in humans
Biological and robotic studies towards intelligent physical interaction with complex terrain
No full textTerrestrial animals are amazingly good at traversing complex 3-D terrain with large obstacles by physically interacting with them and transitioning across various locomotor modes. In stark contrast, even the most advanced legged robots struggle to do so, possibly because they lack a fundamental framework to model robot-obstacle physical interaction paralleling artificial potential fields for obstacle avoidance. To remedy this, previous studies on cockroaches and robophysical models established a novel framework—potential energy landscape modeling—that explains and predicts the destabilizing mode transitions from physical interactions between animal/robot and obstacles, which governs a wide range of complex locomotion. This framework inspired new body shapes and feedforward control strategies to improve robot performance in traversing familiar obstacles. Despite this advance, the framework needed further development to better understand biological adjustments, to be applicable to unknown terrains, and to guide robotic mode transitions.
We further study locomotor-obstacle physical interaction using the potential energy landscape modeling framework in our biological and robotic model systems, where a cockroach or a cockroach-inspired robot traverses a pair of stiff, grass-like beams as cluttered, large obstacles. The animal or robot usually transitions from pitch to roll mode to traverse. This transition on a potential energy landscape over the body roll-pitch space requires escaping the entrapment in a pitch basin, crossing a potential energy barrier, and reaching a roll basin. The least-resistance transition occurs when crossing the barrier at a saddle point.
We observed that cockroaches adjust appendages in the locomotor transition. On a refined landscape, some adjustments substantially reduce the transition barrier, whereas others did not. We developed a minimalistic robot capable of sensing contact forces and torques. Using them as the gradient, we reconstructed the potential energy landscape of an unknown terrain. Finally, we proposed a bio-inspired control strategy that enabled a robot to use feedback control to find unknown modes and transition with the least-resistance path. We verified this strategy using a simulated robotic system. Building on the potential energy landscape modeling framework, our findings advanced the understanding of animal behaviors, enlarged robots' accessible terrain, and improved robots' mobility
Six Attempts to Make Sense
Get PDFIn this project, I motivate and explain a meta-normative account of personal narration. Narration is a practice concerned with the inter-dependency of identity, action, and understanding. I begin by noting that we commonly and earnestly ask ourselves what is good, right, worthwhile, or appropriate for us to do. Because we are liable to err, we may be motivated to take an active interest in efficaciously representing ourselves and our endeavors. Narration serves this purpose; to do so dependably, we -- narrative subjects -- commit to a higher-order representation that credibly posits that we matter non-accidentally. This is posited in a principled way, in a spirit of love and respect toward every one of us who makes sense of what matters and endeavors to behave per that sense
