Mechanical Engineering Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/2795
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Item Second Wave Mechanics(2024) Fabbri, Anthony; Herrmann, Jeffrey W; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The COVID-19 pandemic experienced very well-documented "waves" of the virus's progression, which can be analyzed to predict future wave behavior. This thesis describes a data analysis algorithm for analyzing pandemic behavior and other, similar problems. This involves splitting the linear and sinusoidal elements of a pandemic in order to predict the behavior of future "waves" of infection from previous "waves" of infection, creating a very long-term prediction of a pandemic. Common wave shape patterns can also be identified, to predict the pattern of mutations that have recently occurred, but have not become popularly known as yet, to predict the remaining future outcome of the wave. By only considering the patterns in the data that could possibly have acted in tandem to generate the observed results, many false patterns can be eliminated, and, therefore, hidden variables can be estimated to a very high degree of probability. Similar mathematical relationships can reveal hidden variables in other underlying differential equations.Item Trajectory Optimization of a Tethered Underwater Kite(2021) Alvarez Tiburcio, Miguel; Fathy, Hosam; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This dissertation addresses the challenge of optimizing the motion trajectory of a tethered marine hydrokinetic energy harvesting kite in order to maximize its average electric power output. The dissertation focuses specifically on the “pumping” kite configuration, where the kite is periodically reeled out from a floating base station at high tension, then reeled in at low tension. This work is motivated by the significant potential for sustainable electricity generation from marine currents such as the Gulf Stream. Tethered systems can increase their energy harvesting potential significantly through cross-current motion. Such motion increases apparent flow speed, which is valuable because the instantaneous maximum power that can be harvested is proportional to the cube of this apparent speed. This makes it possible for tethered systems to achieve potentially very attractive power densities and levelized costs of electricity compared to stationary turbines. However, this also necessitates the use of trajectory optimization and active control in order to eke out the maximum energy harvesting capabilities of these systems. The problem of optimizing the trajectories of these kites is highly non-linear and thus challenging to solve. In this dissertation we make key simplifications to both the modeling and the structure of the optimal solution which allows us to learn valuable insights in the nature of the power maximizing trajectory. We first do this analysis to maximize the average mechanical power of the kite, then we expand it to take into account system losses. Finally, we design and fabricate an experimental setup to both parametrize our model and validate our trajectories. In summary, the goal of this research is to furnish model-based algorithms for the online optimal flight control of a tethered marine hydrokinetic system. The intellectual merit of this work stems from the degree to which it will tackle the difficulty of solving this co-optimization problem taking into account overall system efficiency and the full range of possible system motion trajectories. From a broader societal perspective, this work represents a step towards experimentally validating the potential of pumped underwater kite systems to serve as renewable energy harvesters in promising environments such as the Gulf Stream.Item Towards Trust and Transparency in Deep Learning Systems through Behavior Introspection & Online Competency Prediction(2021) Allen, Julia Filiberti; Gabriel, Steven A.; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Deep neural networks are naturally “black boxes”, offering little insight into how or why they make decisions. These limitations diminish the adoption likelihood of such systems for important tasks and as trusted teammates. We employ introspective techniques to abstract machine activation patterns into human-interpretable strategies and identify relationships between environmental conditions (why), strategies (how), and performance (result) on both a deep reinforcement learning two-dimensional pursuit game application and image-based deep supervised learning obstacle recognition application. Pursuit-evasion games have been studied for decades under perfect information and analytically-derived policies for static environments. We incorporate uncertainty in a target’s position via simulated measurements and demonstrate a novel continuous deep reinforcement learning approach against speed-advantaged targets. The resulting approach was tested under many scenarios and performance exceeded that of a baseline course-aligned strategy. We manually observed separation of learned pursuit behaviors into strategy groups and manually hypothesized environmental conditions that affected performance. These manual observations motivated automation and abstraction of conditions, performance and strategy relationships. Next, we found that deep network activation patterns could be abstracted into human-interpretable strategies for two separate deep learning approaches. We characterized machine commitment by the introduction of a novel measure and revealed significant correlations between machine commitment, strategies, environmental conditions, and task performance. As such, we motivated online exploitation of machine behavior estimation for competency-aware intelligent systems. And finally, we realized online prediction capabilities for conditions, strategies, and performance. Our competency-aware machine learning approach is easily portable to new applications due to its Bayesian nonparametric foundation, wherein all inputs are compactly transformed into the same compact data representation. In particular, image data is transformed into a probability distribution over features extracted from the data. The resulting transformation forms a common representation for comparing two images, possibly from different types of sensors. By uncovering relationships between environmental conditions (why), machine strategies (how), & performance (result) and by giving rise to online estimation of machine competency, we increase transparency and trust in machine learning systems, contributing to the overarching explainable artificial intelligence initiative.Item NOISE-INFLUENCED DYNAMICS OF NONLINEAR OSCILLATORS(2015) Perkins, Edmon; Balachandran, Balakumar; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Noise is usually considered detrimental to the performance of a system and the effects of noise are usually mitigated through design and/or control. In this dissertation, noise-influenced phenomena and qualitative changes in responses of nonlinear systems with noise are explored. Here, the author considers a range of nonlinear dynamical systems, including an array of nonlinear, coupled oscillators, a vertically excited pendulum, the Duffing oscillator, and a Rayleigh-Duffing mixed type oscillator. These systems are studied analytically and numerically via stochastic direct numerical integration, and analytically via the Fokker-Planck equation. The array of nonlinear, coupled oscillators is also experimentally studied. The topics covered in this dissertation are as follows: i) the destruction and formation of energy localizations in an array of oscillators, ii) a technique to stabilize an inverted pendulum by using noise, iii) a noise-utilizing control scheme, iv) the effects of noise on the response of a nonlinear system that exhibits chaotic behavior, v) and the effects of phase lag on the information rate of a Duffing oscillator. The understanding gained through this dissertation efforts can be of benefit to a variety of nonlinear systems, including structural systems at the macro-scale, micro-scale, and nano-scale.Item Velocity transformation for compressible wall turbulence with heat transfer(2015) Trettel, Andrew James; Larsson, Johan; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)A transformation is derived that removes the effects of variable properties from wall-bounded turbulent flows. The transformation derives from the logarithmic velocity profile and the universality of the stress balance. The Van Driest transformation and the viscous sublayer transformation form subsets of this proposed transformation. This transformation is validated against direct numerical simulations of compressible turbulent channel flows.Item Dynamics of Slender, Flexible Structures(2014) Vlajic, Nicholas A.; Balachandran, Balakumar; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Dynamics of slender beam-like structures subjected to rotational motions is studied experimentally, numerically, and analytically within this dissertation. As the aspect ratio of beam-like structures is increased (i.e., as the structures become slender), the structure can undergo large elastic deformations, and in addition, the torsional and lateral motions can be strongly coupled. Two different paradigms of rotor systems are constructed and used to investigate coupled torsional-lateral motions in slender rotating structures. The first rotor model is a modified version of the classical Jeffcott rotor, which accounts for torsional vibrations and stator contact. Analysis and simulations indicate that torsional vibrations are unlikely to exist during forward synchronous whirling, and reveal the presence of phenomena with high-frequency content, such as centrifugal stiffening and smoothening, during backward whirling. The second rotor model is a nonlinear distributed-parameter system that has been derived with the intent of capturing dynamics observed in an experimental apparatus with slender, rotating structures. Nonlinear oscillations observed in the experiments contain response components at frequencies other than the drive speed, a feature that is also captured by predictions obtained from the distributed-parameter model. Further analysis of the governing partial-differential equations yields insights into the origins (e.g., nonlinear gyroscopic coupling and frictional forces) of the nonlinear response components observed in the spectrum of the torsion response. Slender structures are often subject to large deformations with pre-stress and curvature, which can drastically alter the natural frequencies and mode shapes when in operation. Here, a geometrically exact beam formulation based on the Cosserat theory of rods is outlined in order to predict the static configuration, natural frequencies, and mode shapes of slender structures with large pre-stress and curvature. The modeling and analysis are validated with experiments as well as comparisons with a nonlinear finite element formulation. The predictions for the first eight natural frequencies are found to be in excellent agreement with the corresponding experimentally determined values. The findings of this dissertation work have a broad range of applications across different length scales, including drill strings, space tethers, deployable structures, cable supported structures (e.g., bridges and mooring cables), DNA strands, and sutures for non-invasive surgery to name a few.Item Nonlinear Fluid-Structure Interactions in Flapping Wing Systems(2013) Fitzgerald, Timothy; Balachandran, Balakumar; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This work relates to fluid-structure interactions in the context of flapping wing systems. System models of flapping flight are explored by using a coupling scheme to provide communication between a fluid model and a structural model describing a flexible wing. The constructed computational models serve as a tool for investigating complex fluid-structure interactions and characterizing them. Primary goals of this work are construction of models to understand nonlinear phenomena associated with the flexible flapping wing systems, and explore means and methods to enhance their performance characteristics. Several system analysis tools are employed to characterize the coupled fluid-structure system dynamics, including proper orthogonal decomposition, dimension calculations, time histories, and frequency spectra. Results obtained from two-dimensional simulations conducted for a combination of a two-link structural system and a fluid system are presented and discussed. Comparisons are made between the use of direct numerical simulation and the unsteady vortex lattice method as the fluid model in this coupled dynamical system. To enable three-dimensional studies, a novel solid model is formulated from continuum mechanics for geometrically exact finite elements. A new partitioned fluid-structure interaction algorithm based on the Generalized-α method is formulated and implemented in a large scale fluids solver inside the FLASH framework. Consistent boundary conditions are also formulated by using Lagrangian particles. Several examples demonstrating the effectiveness of the methods and implementation are shown, in particular, for flapping flight at low Reynolds numbers. Unique experiments have also been undertaken to determine the first few natural frequencies and mode shapes associated with hawkmoth wings. The computational framework developed in this dissertation and the research findings can be used as a basis to understand the role of flexibility in flapping wing systems, further explore the complex dynamics of flapping wing systems, and also develop design schemes that might make use of nonlinear phenomena for performance enhancement.