Theses and Dissertations from UMD
Permanent URI for this communityhttp://hdl.handle.net/1903/2
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
More information is available at Theses and Dissertations at University of Maryland Libraries.
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Item APPLICATION OF A BAYESIAN NETWORK BASED FAILURE DETECTION AND DIAGNOSIS FRAMEWORK ON MARITIME DIESEL ENGINES(2022) Reynolds, Steven; Groth, Katrina; Systems Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Diesel engine propulsion has been the largest driver of maritime trade and transportation since its development in the early 20th century and the technology surrounding the operation and maintenance of these systems has grown in complexity leading to rapid advancement in amount and variety of data being collected. This increase in reliability data provides a fantastic opportunity to improve upon the existing tools troubleshooting and decision support tool used within the maritime engine community to enable a more robust understanding of engine reliability. This work leverages this opportunity and applies it to the Coast Guard and its acquisition of the Fast Response Cutter (FRC) fleet powered by two MTU20V4000M93 engines integrated with top of line monitoring and control equipment.The purpose of this research is to create procedures for creating a Failure Detection and Diagnosis (FDD) model of a maritime diesel engine that updates existing Probabilistic Risk Analysis (PRA) data with input from the engine monitoring and control system using Bayesian inference. A literature review of existing work within the PRA and Prognostics and Health Management (PHM) fields was conducted with specific focus on the advancement and gaps in the field specific to their use in maritime engine applications. Following this, a hierarchal ruleset was created that outlines procedures for integrating existing PRA data and PHM metrics into a Bayesian Network structure. This methodology was then used to build a Bayesian Network based FDD model of the FRC engine. This model was then validated by Coast Guard Engineers and run through a diagnostic use case scenario demonstrating the model’s suitability in the diagnostic space.Item COMPARATIVE ANALYSIS OF MINIATURE INTERNAL COMBUSTION ENGINE AND ELECTRIC MOTOR FOR UAV PROPULSION(2017) Chiclana, Branden; Cadou, Christopher; Aerospace Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This thesis compares the performance of an engine/fuel tank based propulsion system to a motor/battery based propulsion system of equal total mass. The results show that the endurance of the engine/fuel system at the same thrust output is approximately 5 times greater than that of the motor/battery system. This is a direct result of the fact that the specific energy of the fuel is 20 times that of the lithium-polymer batteries used to power the motor. A method is also developed to account for the additional benefits of fuel consumption (and hence weight reduction) over the course of the flight. Accounting for this effect can increase endurance exponentially. Taken together, the results also demonstrate the dramatic performance improvements that are possible simply by replacing motor/battery systems with engine/fuel systems on small unmanned air vehicles.Item Performance Measurement, Simulation, and Analysis of the Cox Tee Dee 0.010, the World's Smallest Production Internal Combustion Engine(2006-12-15) Sookdeo, Troy; Cadou, Christopher; Aerospace Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The Cox Tee Dee 0.010 is a two-stroke 0.010 cubic inch model engine designed to power small propeller-based hobby aircraft. First manufactured in 1961, it remains the smallest working piston engine ever mass-produced, but no scientific measurements of its performance are available in the open literature. These measurements are important because they could facilitate the development of small unmanned air vehicles. This thesis reports measurements of power output and efficiency using a specialized dynamometer. An unsuccessful attempt is made to correlate the measurements with simulations based on Stanford University's Engine Simulation Program (ESP). Instead, the results are compared to the predictions of a simple zero-dimensional thermodynamic MATLAB simulation of an engine cycle developed at the University of Maryland. Differences and correlations are discussed and the engine performance is analyzed in the context of propulsion systems for small UAVs and for compact power generation.