UMD Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/3
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 given thesis/dissertation in DRUM.
More information is available at Theses and Dissertations at University of Maryland Libraries.
Browse
3 results
Search Results
Item Direct Nonlinear Trajectory Optimization and State Estimation for a Tethered Underwater Energy Harvesting Kite(2022) Bhattacharjee, Debapriya; Fathy, Hosam K.; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This dissertation addresses the coupled challenges of state estimation and trajectory optimization for a marine hydro-kinetic energy harvesting kite. The optimization objective is to maximize the kite's average mechanical power output. This work is motivated by the potential of ``pumping-mode" tethered kites to provide attractive levelized costs of electricity, especially when cross-current motion is exploited to maximize energy harvesting. In ``pumping-mode" kites, the kite is tethered to platform carrying a motor/generator, and electricity generation is achieved by reeling the kite out and in at high and low tether tension levels, respectively. Marine hydro-kinetic (MHK) systems are heavily influenced by wind energy systems. In both contexts, for instance, tethered kites can be used for electricity generation instead of stationary turbines. Similar to airborne wind energy (AWE) systems, the power production capacities of MHK kites are heavily influenced by their flight trajectories. While trajectory optimization is a well-established research area for AWE systems, it is a nascent but growing field for MHK kites. Moreover, although both AWE and MHK kites have the potential to benefit from trajectory optimization, the lessons learned from AWE systems might not be directly applicable to MHK kites, since MHK systems are often close to neutral buoyancy whereas AWE systems are not. Finally, there is little work in the literature that co-optimizes the spooling and cross-current trajectories of a pumping-mode MHK kite. The first contribution of this dissertation is to explore the simultaneous optimization of the cross-current trajectory and the spooling motion of a pumping-mode kite using direct transcription. While the results highlight the degree to which simultaneous optimization can be beneficial for these systems, they also motivate the need for a solution approach that satisfies the constraints imposed by the kite dynamics exactly, as opposed to approximately. This leads to the second contribution of this dissertation, namely, finding an analytic solution to the inverse dynamics of the MHK kite, i.e., mapping a desired combination of kite position, velocity, and acceleration onto the corresponding actuation inputs. The dissertation then proceeds to its third contribution, namely, solving the kite trajectory optimization problem based on the above exact solution of the kite's inverse dynamics. The resulting simulation provides more realistic optimization results. However, all of the above work focuses on the special case where the free-stream fluid velocity is known and spatio-temporally constant. This motivates the fourth and final contribution of this dissertation, namely, the development of an unscented Kalman filter for simultaneously estimating both the kite's state and the free-stream fluid velocity. One interesting outcome of the estimation study is the finding that simple unscented Kalman filtering is not able to estimate the fluid velocity accurately without the direct measurement of the attitude of the kite.Item DATA-DRIVEN SIMULATIONS OF WILDFIRE SPREAD AT REGIONAL SCALES(2018) Zhang, Cong; Trouve, Arnaud; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Current wildfire spread simulators lack the ability to provide accurate prediction of the active flame burning areas at regional scales due to two main challenges: a modeling challenge associated with providing accurate mathematical representations of the multi-physics multi-scale processes that induce the fire dynamics, and a data challenge associated with providing accurate estimates of the initial fire position and the physical parameters that are required by the fire spread models. A promising approach to overcome these limitations is data assimilation: data assimilation aims at integrating available observations into the fire spread simulator, while accounting for their respective uncertainties, in order to infer a more accurate estimate of the fire front position and to produce a more reliable forecast of the wildfire behavior. The main objective of the present study is to design and evaluate suitable algorithms for regional-scale wildfire spread simulations, which are able to properly handle the variations in wildfire spread due to the significant spatial heterogeneity in the model inputs and to the temporal changes in the wildfire behavior. First we developed a grid-based spatialized parameter estimation approach where the estimation targets are the spatially-varying input model parameters. Then we proposed an efficient and robust method to compute the discrepancy between the observed and simulated fire fronts, which is based on a front shape similarity measure inspired from image processing theory. The new method is demonstrated in the context of Luenberger observer-based state estimation strategy. Finally we developed a dual state-parameter estimation method where we estimate both model state and model parameters simultaneously in order to retrieve more accurate physical values of model parameters and achieve a better forecast performance in terms of fire front positions. All these efforts aim at designing algorithmic solutions to overcome the difficulties associated with spatially-varying environmental conditions and potentially complex fireline shapes and topologies. It paves the way towards real-time monitoring and forecasting of wildfire dynamics at regional scales.Item Problems in Spatiotemporal Chaos(2007-11-26) Cornick, Matthew Tyler; Ott, Edward; Physics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In this thesis we consider two problem areas involving spatiotemporally chaotic systems. In Part I we investigate data assimilation techniques applicable to large systems. Data assimilation refers to the process of estimating a system's state from a time series of measurements (which may be noisy or incomplete) in conjunction with a model for the system's time evolution. However, for practical reasons, the high dimensionality of large spatiotemporally chaotic systems prevents the use of classical data assimilation techniques such as the Kalman filter. Here, a recently developed data assimilation method, the local ensemble transform Kalman Filter (LETKF), designed to circumvent this difficulty is applied to \RaBen convection, a prototypical spatiotemporally chaotic laboratory system. Using this technique we are able to extract the full temperature and velocity fields from a time series of shadowgraphs from a Rayleigh-Benard convection experiment. The process of estimating fluid parameters is also investigated. The presented results suggest the potential usefulness of the LETKF technique to a broad class of laboratory experiments in which there is spatiotemporally chaotic behavior. In Part II we study magnetic dynamo action in rotating electrically conducting fluids. In particular, we study how rotation effects the process of magnetic field growth (the dynamo effect) for a externally forced turbulent fluid. We solve the kinematic magnetohydrodynamic (MHD) equations with the addition of a Coriolis force in a periodic domain. Our results suggest that rotation is desirable for producing dynamo flows.