Thesis (M.B.A.)--University of Hong Kong, 1996. / Includes bibliographical references (leaf 65-67).

Thesis (Ph. D.)--University of Wisconsin--Madison, 1959. / Typescript. Vita. eContent provider-neutral record in process. Description based on print version record. Bibliography: leaves 457-488.

Thesis--Neuchâtel. / "Ouvrages consultés": p. [119]-124.

Thesis (Ph. D.)--University of Texas at Austin, 2005. / Supervisor: Robert Helmreich. Vita. Includes bibliographical references.

This inquiry examines aviation futures, within the broad context of sustainable consumption and ecological sustainability. Increasing mobility and rapid growth of road and air transport have been identified as key consumption issues, especially in relation to calls for deep cuts in greenhouse gas emissions. Economics and globalisation concerns have dominated recent academic studies of air transport. There is a growing critique, however, of growth-based aviation futures on a range of ecological, resource, security and health grounds. This inquiry adds to that critique. Its primary research aim is to identify and articulate visions and policies for aviation futures within an ecological framework. Analysis of relevant literatures, including those on sustainable consumption, transport and tourism, is complemented by an analysis of aviation and tourism growth trends worldwide, with specific reference to Asia-Pacific and Australian data. A case study of the privatisation and expansion of Canberra International Airport in the Australian Capital Territory is used to provide a global-local link and to address the airport aspects of aviation. The range of visions and policy measures are finally considered within broad consciousness and frameworks strategies for the transition to sustainability. An important conclusion is the strong need for independent institutions such as policy bodies and think tanks - working in conjunction with community groups- to develop and promote policy discourses and futures visions that challenge the prevailing hegemony of the business-political nexus. / Doctor of Philosophy (PhD)

Thesis (Ph.D.)--Ohio University, June, 2006. / Title from PDF t.p. Includes bibliographical references (p. 104-112)

Thesis (M.B.A.)--University of Hong Kong, 1983.

Thesis (S.J.D.)--University of Western Australia, 2003.

Thesis: Ph. D. in Computational Science and Engineering, Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2018 / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 255-266). / The polar ice sheets have enormous potential impact on future global mean sea level rise. Recent observations suggest they are losing mass to the ocean at an accelerated rate. Skillful prediction of the ice sheets' future mass loss remains difficult, however; observations of key variables are insufficient and physical processes are poorly understood. Even when a relatively accurate dynamical model is available, computational limitations make it difficult to characterize uncertainties associated with the model's predictions. To address this prediction challenge, this thesis presents complementary developments in glaciology and in Bayesian computation. / In particular, (i) we develop new models of marine-terminating glaciers whose dynamics are controlled by an extended set of physical processes and geometric constraints; and (ii) we develop new sampling algorithms to efficiently characterize selected marginals of a high-dimensional probability distribution describing uncertain parameters. The latter algorithms have broader utility in Bayesian modeling and inference with computationally intensive models. We begin by studying laterally confined ice streams that terminate in the ocean, where they may form floating ice shelves. Such marine-terminating outlet glaciers are the main conduits by which Greenland and Antarctica drain their ice mass into the ocean. Ice shelves play an important role in buttressing the grounded inland ice. The seaward ice flow is typically accompanied by acceleration and thinning. Increased thinning eventually leads to flotation of the ice supported by buoyant forces from the ocean. / The transition region from grounded to floating ice is referred to as the grounding line (or zone), and the mass transport across the grounding line as the output flux. Previous work by Weertman (1974) and Schoof (2007) considers laterally unconfined ice streams, showing that their output flux is a monotonically increasing function of the bedrock rock depth at the grounding line. This scenario leads to the marine ice sheet instability (MISI): retreating into deeper water increases the output flux, and retreat accelerates. Therefore, stable steady states cannot exist on downward sloping beds. We extend this analysis to laterally confined glaciers and investigate when side-wall drag is sufficient to stabilize glaciers on downward sloping beds. Additionally, we include a parameterization of sub-shelf melt. We find that, whereas lateral drag can stabilize glaciers that would otherwise be subject to the MISI, sub-shelf melt can destabilize them. / Our ultimate goal is to predict future ice sheet volume and to quantify its uncertainty. We do so in the Bayesian statistical setting, conditioning our prediction on available observations. Yet characterizing a posterior distribution-using, for example, Markov chain Monte Carlo (MCMC)-involves repeated evaluations of an ice stream model, which are prohibitively expensive. Furthermore, the model parameters that need to be inferred are high dimensional, even though we are primarily interested in a low dimensional quantity: the future ice volume. We address this computational challenge by developing new structure-exploiting Monte Carlo methods that combine marginalization with surrogate modeling. Given a high-dimensional (posterior) distribution on the model parameters, whose density evaluations are computationally intensive, we construct an MCMC chain that directly targets a particular low-dimensional marginal of interest. In general, the marginal density is not available analytically. / Instead, we can compute unbiased noisy estimates of this density. Our MCMC algorithm incrementally constructs a local regression approximation of the target marginal density using these estimates. Continual refinement of the approximation, as MCMC sampling proceeds, leads to an asymptotically exact characterization of the desired marginal distribution. Analysis of the bias-variance tradeoff guides an ideal refinement strategy that balances the decay rates of different components of the error. Our approach exploits regularity in the marginal density to significantly reduce computational expense relative to both full-dimensional and pseudo-marginal MCMC. / by Andrew D. Davis. / Ph. D. in Computational Science and Engineering / Ph.D.inComputationalScienceandEngineering Massachusetts Institute of Technology, Department of Aeronautics and Astronautics

This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. / Thesis: Ph. D. in Computational Science and Engineering, Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2018 / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 187-195). / Design optimization methods are a popular tool in computational fluid dynamics for designing components or finalizing the flow parameters of a system. The adjoint method accelerates the design process by providing gradients of the design objective with respect to the system parameters. But, typically, adjoint-based design optimization methods have used low fidelity simulations like Reynolds Averaged Navier-Stokes (RANS). To reliably capture the complex flow phenomena like turbulent boundary layers, turbulent wakes and fluid separation involved in high Reynolds number flows, high fidelity simulations like large eddy simulation (LES) are required. Unfortunately, due to the chaotic dynamics of turbulence, the adjoint method for LES diverges and produces incorrect gradients. In this thesis, the adjoint method for unsteady flow equations is modified by adding artificial viscosity to the adjoint equations. The additional viscosity stabilizes the adjoint solution and maintains reasonable accuracy of the gradients obtained from it. The accuracy of the method is assessed on multiple turbulent flow problems, including subsonic flow over a cylinder and transonic flow over a gas turbine vane. The utility of the method is then tested in performing shape optimization of the trailing edge of a transonic turbine vane. The optimal design, found using a modified gradient-based Bayesian optimization algorithm, shows approximately 15% better aero-thermal performance than the baseline design. Such design optimizations are possible due to the availability of massively parallel supercomputers. Designing high performance fluid flow solvers for the next generation supercomputers is a challenging task. In this thesis, a two-level computational graph method for writing optimized distributed flow solvers on heterogeneous architectures is presented. A checkpoint-based automatic differentiation method is used to derive the corresponding adjoint flow solver in this framework. / by Chaitanya Anil Talnikar. / Ph. D. in Computational Science and Engineering / Ph.D.inComputationalScienceandEngineering Massachusetts Institute of Technology, Department of Aeronautics and Astronautics