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.
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Item Prediction of Marine Timber Pile Damage Ratings Using a Gradient Boosted Regression Model(2023) Willmott, Carly; Attoh-Okine, Nii O.; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Marine pilings are critical structural elements exposed to harsh environmental conditions. Specialized routine inspection and regular maintenance are essential to keep marine facilities in good working condition. These activities generate data that can be exploited for knowledge gain with machine learning tools. A gradient boosted random forest regressor machine learning algorithm, XGBoost, was applied to datasets that contain timber pile underwater inspection and repair data over a period of 23 years. First, the data was visualized to show the longevity of different timber pile repair types. An XGBoost model was then tuned and trained on a dataset for timber piles at one pier. Variables in the dataset were evaluated for feature importance in predicting damage ratings assigned during routine underwater inspections. Next, an ensemble of XGBoost models was trained and applied to a second dataset containing the same features for an adjacent pier. This dataset was reserved for testing to demonstrate whether the ensemble trained on one pier’s data could be generalized to predict timber pile damage ratings at a nearby but separate pier. Finally, the ensemble was used to predict damage ratings on piles that had earlier data but were not rated in the two most recent inspection events. Results suggest that the ensemble is capable of predicting timber pile damage ratings to approximately +/- one damage rating on both the training and test datasets. Feature importances revealed that half of the variables (time since the first event, repair type, exposed pile length, and time since the last repair) contributed to two thirds of the relative importance in predicting damage ratings. Data visualization showed that a few repair types, such as pile replacements and encapsulations, appeared to be most successful over the long term compared with shorter-lived repairs like wraps and encasements. These results are promising indications of the advantages machine learning algorithms can offer in processing and gleaning new insights from structural repair and inspection data. Economic benefits to marine facility owners can potentially be realized through earlier anticipation of repairs and more targeted inspection and rehabilitation efforts. There are also opportunities for better understanding of deterioration rates if more data is gathered over the lifespans of structures, as well as more detailed data that can be introduced as new features.Item Modeling, Estimation, and Control of Actuator Dynamics for Remotely Operated Underwater Vehicles(2019) Boehm, Jordan; Paley, Derek A; Aerospace Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Modeling and control of remotely operated underwater vehicles is a challenging problem that depends greatly on how the dynamics of their thrusters are compensated. In this thesis a novel method for characterizing thruster dynamics using a six-axis load cell is presented. Multiple dynamic models are characterized with this test setup. Model-based control design strategies are used to compensate for nonlinearities in the dynamics, which include input dead zones and coupling with fluid dynamics. Multiple estimation methods are presented to construct an estimate of fluid velocity which is handled as an unmeasured state. The different models, controllers, and estimators are comparatively evaluated in closed-loop experiments using the six-axis load cell to measure thrust tracking performance. Full vehicle simulations using the experimentally characterized models provide additional opportunities for comparison of control and estimation strategies. The potential tracking control benefits from the variety of presented thruster dynamics compensation strategies are evaluated for a remotely operated underwater vehicle with multiple thrusters.Item Covariance Localization in Strongly Coupled Data Assimilation(2019) Yoshida, Takuma; Kalnay, Eugenia; Penny, Stephen G; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The recent development of accurate coupled models of the Earth system and enhanced computation power have enabled numerical prediction with the coupled models in weather, sub-seasonal, seasonal, and interannual time scales as well as climate projection. In the shorter timescales, the initial condition, or the estimate of the present state of the system, is essential for accurate prediction. Coupled data assimilation (DA) based on an ensemble of forecasts seems to be a promising approach for this state estimate due to its inherent ability to estimate flow-dependent error covariance. Strongly coupled DA tries to incorporate more observations of the other subsystems into an analysis (e.g., ocean observations into the atmospheric analysis) using the coupled error covariances; the covariance is estimated with a finite ensemble, and spurious covariance must be eliminated by localization. Because the coupling strength between subsystems of the Earth is not a simple function of a distance, we develop a better localization strategy than the distance-dependent localization. Based on the estimated benefit of each observation into each analysis variable, we first propose the correlation-cutoff method, where localization of strongly coupled DA is guided by ensemble correlations of an offline DA cycle. The method achieves improved analysis accuracy when tested with a simple coupled model of the atmosphere and ocean. As a related topic, error growth and predictability of a coupled dynamical system with multiple timescales are explored using a simple chaotic model of the atmosphere and ocean. A discontinuous response of the attractor's characteristics to the coupling strength is reported. The characteristic of global atmosphere-ocean coupled error correlation is investigated using two sets of ensemble DA systems. This knowledge is essential for effectively implementing global strongly coupled atmosphere-ocean DA. We report and discuss common and uncommon features, and the importance of ocean model resolution is stressed. Finally, the correlation-cutoff method is realized for global atmosphere-ocean strongly coupled DA with neural networks. The combination of static information provided by the neural networks and flow-dependent error covariance estimated by the ensemble improves the atmospheric analysis in our proof-of-concept experiment. The neural networks' ability to reproduce the error statistics, computation cost in a DA system, as well as analysis quality are evaluated.Item Developments in Lagrangian Data Assimilation and Coupled Data Assimilation to Support Earth System Model Initialization(2019) Sun, Luyu; Carton, James A.; Penny, Stephen G.; Applied Mathematics and Scientific Computation; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The air-sea interface is one of the most physically active interfaces of the Earth's environments and significantly impacts the dynamics in both the atmosphere and ocean. In this doctoral dissertation, developments are made to two types of Data Assimilation (DA) applied to this interface: Lagrangian Data Assimilation (LaDA) and Coupled Data Assimilation (CDA). LaDA is a DA method that specifically assimilates position information measured from Lagrangian instruments such as Argo floats and surface drifters. To make a better use of this Lagrangian information, an augmented-state LaDA method is proposed using Local Ensemble Transform Kalman Filter (LETKF), which is intended to update the ocean state (T/S/U/V) at both the surface and at depth by directly assimilating the drifter locations. The algorithm is first tested using "identical twin" Observing System Simulation Experiments (OSSEs) in a simple double gyre configuration with the Geophysical Fluid Dynamics Laboratory (GFDL) Modular Ocean Model version 4.1 (MOM4p1). Results from these experiments show that with a proper choice of localization radius, the estimation of the state is improved not only at the surface, but throughout the upper 1000m. The impact of localization radius and model error in estimating accuracy of both fluid and drifter states are investigated. Next, the algorithm is applied to a realistic eddy-resolving model of the Gulf of Mexico (GoM) using Modular Ocean Model version 6 (MOM6) numerics, which is related to the 1/4-degree resolution configuration in transition to operational use at NOAA/NCEP. Atmospheric forcing is first used to produce the nature run simulation with forcing ensembles created using the spread provided by the 20 Century Reanalysis version 3 (20CRv3). In order to assist the examination on the proposed LaDA algorithm, an updated online drifter module adapted to MOM6 is developed, which resolves software issues present in the older MOM4p1 and MOM5 versions of MOM. In addition, new attributions are added, such as: the output of the intermediate trajectories and the interpolated variables: temperature, salinity, and velocity. The twin experiments with the GoM also show that the proposed algorithm provides positive impacts on estimating the ocean state variables when assimilating the drifter position together with surface temperature and salinity. Lastly, an investigation of CDA explores the influence of different couplings on improving the simultaneous estimation of atmosphere and ocean state variables. Synchronization theory of the drive-response system is applied together with the determination of Lyapunov Exponents (LEs) as an indication of the error convergence within the system. A demonstration is presented using the Ensemble Transform Kalman Filter on the simplified Modular Arbitrary-Order Ocean-Atmosphere Model, a three-layer truncated quasi-geostrophic model. Results show that strongly coupled data assimilation is robust in producing more accurate state estimates and forecasts than traditional approaches of data assimilation.Item DATA-DRIVEN STUDIES OF TRANSIENT EVENTS AND APERIODIC MOTIONS(2019) Wang, Rui; Balachandran, Balakumar; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The era of big data, high-performance computing, and machine learning has witnessed a paradigm shift from physics-based modeling to data-driven modeling across many scientific fields. In this dissertation work, transient events and aperiodic motions of complex nonlinear dynamical system are studied with the aid of a data- driven modeling approach. The goal of the work has been to further the ability for future behavior prediction, state estimation, and control of related behaviors. It is shown that data on extreme waves can be used to carry out stability analysis and ascertain the nature of the transient phenomenon. In addition, it is demonstrated that a low number of soliton elements can be used to realize a rogue wave on the basis of nonlinear interactions amongst the basic elements. The pro- posed nonlinear phase interference model provides an appealing explanation for the formation of ocean extreme wave and related statistics, and a superior reconstruction of the Draupner wave event than that obtained on the basis of linear superposition. Chaotic data, another manifestation of aperiodic motions, which are obtained from prototypical ordinary differential and partial differential systems are considered and a neural machine is realized to predict the corresponding responses based on a limited training set as well to forecast the system behavior. A specific neural architecture, called the inhibitor mechanism, has been designed to enable chaotic time series forecasting. Without this mechanism, even the short-term predictions would be intractable. Both autonomous and non-autonomous dynamical systems have been studied to demonstrate the long-term forecasting possibilities with the de- veloped neural machine. For each dynamical system considered in this dissertation, a long forecasting horizon is achieved with a short historical data set. Furthermore, with the developed neural machine, one can relax the requirement of continuous historical data measurements, thus, providing for a more pragmatic approach than the previous approaches available in the literature. It is expected that the efforts of this dissertation work will lead to a better understanding of the underlying mechanism of transient and aperiodic events in complex systems and useful techniques for forecasting their future occurrences.Item ON THE IMPACT BETWEEN A WATER FREE SURFACE AND A RIGID STRUCTURE(2017) Wang, An; Duncan, James H; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In this thesis, the impact between a water surface and a structure is addressed in two related experiments. In the first experiment, the impact of a plunging breaking wave on a partially submerged 2D structure is studied. The evolution of the water surface profiles are measured with with a cinematic laser-induced flourescence technique, while the pressure distribution on the wall is measured simultaneously with an array of fast-response pressure sensors. When the structure is placed at a particular streamwise location in the wave tank and the bottom surface of the structure is located 13.3~cm below the mean water level, a ``flip-through'' impact occurs. In this case, the water surface profile between the crest and the front face of the structure is found to shrink to a point as the wave approaches the structure without breaking. High acceleration of the contact point motion is observed in this case. When the bottom of the structure is located at the mean water level, high-frequency pressure oscillations are observed. These pressure oscillations are believed to be caused by air that is entrapped near the wave crest during the impact process. When the bottom of the structure is sufficiently far above the mean water level, the first contact with the structure is the impact between the wave crest and the bottom corner of the structure. This latter condition, produces the largest impact pressures on the structure. In the second experiment, the slamming of a flat plate on a quiescent water surface is studied. A two-axis high-speed carriage is used to slam a flat plate on the water surface with high horizontal and vertical velocity. The above-mentioned LIF system is used to measure the evolution of the free surface adjacent to the plate. Measurements are performed with the horizontal and vertical carriage speeds ranging from zero to 6 m/s and 0.6 to 1.2 m/s, respectively, and the plate oriented obliquely to horizontal. Two types of splash are found, a spray of droplets and ligaments that is ejected horizontally from under the plate in the beginning of the impact process and a highly sloped spray sheet that is ejected later when the high edge of the plate moves below the water surface. Detailed measurements of these features are presented and simple models are used to interpret the data.Item An Experimental Study of Water Surface Features in Response to Rain(2017) Liu, Ren; Duncan, James H.; Liu, Xinan; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Water surface features induced by the impact of raindrops on a deep water pool are studied experimentally in an artificial rain facility. Artificial rain is produced by a rain generator that consists of a rectangular tank with an array of 738 hypodermic needles attached to its bottom and that is mounted at various heights above a deep water pool. In this thesis, three rain intensities and four raindrop impact velocities are used while the diameters of raindrops remain approximately the same. For comparison with some of the results of the rain experiments, a set of single drop impacts on a quiescent water surface were also performed. In the single drop impact experiments, cinematic shadowgraph methods were used to measure the drop diameter (D) and velocity (V ) just before impact, to observe qualitatively the water surface response and to measure the height of the vertical water jet (stalk) that is typically part of the water surface response. It is found that the stalk height varies with impact Froude number (Fr = V^2/(gD), where g is the acceleration of gravity) in three different ways depending on the Froude number range. In the rain experiments, the drop diameters and velocities are measured with a cinematic shadowgraph technique while the temporal evolution of the surface profile along the center plane of the target water pool is measured with a cinematic Laser Induced Fluorescence (LIF) technique. The history of rain-induced stalk height and the profiles of the rain-induced surface waves are extracted at each instant in time. It is found that the stalk height varies considerably in the rain field and the average stalk heights are less than in cases with single drop impacting a quiescent surface at the same Froude number (Fr). The stalk height distribution correlates with the rain intensities rather than the impact velocity. Occasional bubble en- trainment was observed at the lowest raindrop impact velocity (Fr = 500) while bubble entrainment only occurred for Froude numbers greater than 1800 in single drop experiments. Furthermore, surface waves outside of the rain field propagate faster than that inside the rain field. Radar backscattering powers from raindrops, surface waves in front of the rain field and the water surface features inside a rain field are measured. The measurement results show that strong radar return signals are observed from the water surface inside the rain field while the radar return signals from both raindrops and the surface waves in front of the rain field are weak. The experimental results also show that the radar return intensity increases as the rain intensity increases from 85 to 300 mm/hr. In addition, it is found that the attenuation of the radar backscattering from the rain field is likely correlated with a high-water-content layer of secondary droplets generated in the rain field.Item Planning for Autonomous Operation of Unmanned Surface Vehicles(2016) Shah, Brual; Gupta, Satyandra K; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The growing variety and complexity of marine research and application oriented tasks requires unmanned surface vehicles (USVs) to operate fully autonomously over long time horizons even in environments with significant civilian traffic. The autonomous operations of the USV over long time horizons requires a path planner to compute paths over long distances in complex marine environments consisting of hundreds of islands of complex shapes. The available free space in marine environment changes over time as a result of tides, environmental restrictions, and weather. Secondly, the maximum velocity and energy consumption of the USV is significantly influenced by the fluid medium flows such as strong currents. Finally, the USV have to operate in an unfamiliar, unstructured marine environment with obstacles of variable dimensions, shapes, and motion dynamics such as other unmanned surface vehicles, civilian boats, shorelines, or docks poses numerous planning challenges. The proposed Ph.D. dissertation explores the above mentioned problems by developing computationally efficient path and trajectory planning algorithms that enables the long term autonomous operation of the USVs. We have developed a lattice-based 5D trajectory planner for the USVs operating in the environment with the congested civilian traffic. The planner estimates collision risk and reasons about the availability of contingency maneuvers to counteract unpredictable behaviors of civilian vessels. Secondly, we present a computationally efficient and optimal algorithm for long distance path planning in complex marine environments using A* search on visibility graphs defined over quad trees. Finally, we present an A* based path planning algorithm with newly developed admissible heuristics for computing energy efficient paths in environment with significant fluid flows. The effectiveness of the planning algorithms is demonstrated in the simulation environments by using systems identified dynamics model of the wave amplitude modular vessel (WAM-V) USV14.Item SEA LEVEL RISE AND ITS ECONOMIC EFFECTS ON NAVAL INSTALLATIONS(2015) Schedel, Angela Luzier; Baecher, Gregory B; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Global sea level is rising. Coastal lands are at risk from eventual inundation, property loss and economic devaluation. The threat is impending but not rapidly approaching. With sea level rise projections ranging from 0.1 meters to 2 meters by the year 2100, there are concerns but little action being taken to adapt and prepare. Given the potential economic impact of future flood events, it appears that many government agencies and municipalities are not taking enough action to prevent the threat of sea level rise. Due to its large footprint of real estate within the coastal zone worldwide, one of the largest organizations threatened directly by sea level rise is the U.S. Navy. Adapting to sea level rise will require strategic planning and policy changes in order to prevent the encroaching sea from limiting naval operations and threatening national security. This study provides a tool to aid Navy decision makers in Implementing Sea Level Adaptation (ISLA). The ISLA tool applies the methodology of decision trees and Expected Monetary Value (EMV), using probability to estimate the cost of potential flood damage and compare this cost to adaptation measures. The goal of this research is for ISLA to empower decision makers to evaluate various adaptation investments related to sea level rise. A case study is used to illustrate the practical application of ISLA. The case study focuses on when to implement a variety of adaptation measures to one asset at the naval base at Norfolk, Virginia. However, its method can be applied to any asset in any location. It is not limited to only military bases. ISLA incorporates a unique method for analyzing the implementation of adaptation measures to combat future coastal flooding which will be worsened by sea level rise. It is unique in its use of decision tree theory to combine the probability of future flood events with the estimated cost of flood damage. This economic valuation using Expected Monetary Value allows for comparison of a variety of adaptation measures over time. The projections of future flood damage costs linked to adaptation allows the decision maker to determine which adaptation measures are economically advantageous to implement and when to implement them.