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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Subject: search.filters.subject.Uncertainty Quantification

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    Application of Uncertainty Quantication of Turbulence Intensity on Airfoil Aerodynamics
    (2017) Salahudeen, Atif; Baeder, James; Aerospace Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Traditional CFD results have a number of freestream inputs. In the physical world, these input conditions often have some uncertainty associated with them. However, this uncertainty is often omitted from the CFD results. The effects of uncertainty in CFD can be determined through application of Uncertainty Quantification (UQ). The primary objective of the present work is to determine the effect of uncertainty in freestream turbulence intensity (FSTI) on the coefficients of lift, drag, and moment for four different airfoils: S809, NACA 0012, SC1095, and RC(4)-10. In this work, the Monte Carlo method is used to calculate the sensitivities of the aerodynamic coefficients to Gaussian distributions of uncertainty in FSTI over a range of angles of attack (AOA) at various Reynolds numbers and Mach numbers. However, the Monte Carlo method would require hundreds of thousands of CFD calculations in order to converge to the correct results. A surrogate surface is therefore generated using a parametric study using the in-house flow solver OVERTURNS. Rather than run a separate CFD run for each Monte Carlo run, all of the results can be attained virtually instantaneously via the surrogate surface. The UQ analysis shows how varying these parameters affects the sensitivies of the aerodynamic coefficients to uncertainty in FSTI. In most cases, the response is nearly Gaussian and the mean response is not too dierent from the discrete FSTI response without uncertainty. However, the output standard deviation for drag and pitching moment can become large when the transition location changes rapidly with changing FSTI.
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    Fast Solvers and Uncertainty Quantification for Models of Magnetohydrodynamics
    (2014) Phillips, Edward Geoffrey; Elman, Howard C; Applied Mathematics and Scientific Computation; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    The magnetohydrodynamics (MHD) model describes the flow of electrically conducting fluids in the presence of magnetic fields. A principal application of MHD is the modeling of plasma physics, ranging from plasma confinement for thermonuclear fusion to astrophysical plasma dynamics. MHD is also used to model the flow of liquid metals, for instance in magnetic pumps, liquid metal blankets in fusion reactor concepts, and aluminum electrolysis. The model consists of a non-self-adjoint, nonlinear system of partial differential equations (PDEs) that couple the Navier-Stokes equations for fluid flow to a reduced set of Maxwell's equations for electromagnetics. In this dissertation, we consider computational issues arising for the MHD equations. We focus on developing fast computational algorithms for solving the algebraic systems that arise from finite element discretizations of the fully coupled MHD equations. Emphasis is on solvers for the linear systems arising from algorithms such as Newton's method or Picard iteration, with a main goal of developing preconditioners for use with iterative methods for the linearized systems. In particular, we first consider the linear systems arising from an exact penalty finite element formulation of the MHD equations. We then draw on this research to develop solvers for a formulation that includes a Lagrange multiplier within Maxwell's equations. We also consider a simplification of the MHD model: in the MHD kinematics model, the equations are reduced by assuming that the flow behavior of the system is known. In this simpler setting, we allow for epistemic uncertainty to be present. By mathematically modeling this uncertainty with random variables, we investigate its implications on the physical model.