Mechanical Engineering Theses and Dissertations

Permanent URI for this collectionhttp://hdl.handle.net/1903/2795

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    A Probabilistic Risk Assessment Based Approach to Understanding and Managing Risks of Natural Gas Distribution Piping in the United States
    (2020) Lyons, Sara; Modarres, Mohammed; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Two hundred sixty-nine regulated pipeline system accidents caused fatalities and/or injuries in the United States between 2010 and 2018, resulting in 106 fatalities and 599 injuries requiring hospitalization. About 84% of these serious accidents occurred on gas distribution systems, which primarily transport natural gas. This study adapts probabilistic risk assessment (PRA) methods which are used predominantly in the space and nuclear industries to gas distribution systems in the U.S. Nationwide system and accident data are used to evaluate natural gas distribution system risks, estimate how many additional resources the public would be willing to dedicate to reduce or eliminate these risks, and determine which improvement areas warrant further evaluation. Recommendations regarding the overall PRA-based framework, as well as the scope, quality, and level of detail of the underlying data, are provided.
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    AN EVENT CLASSIFICATION SCHEMA FOR CONSIDERING SITE RISK IN A MULTI-UNIT NUCLEAR POWER PLANT PROBABILISTIC RISK ASSESSMENT
    (2012) Schroer, Suzanne; Modarres, Mohammad; Reliability Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Today, probabilistic risk assessments (PRAs) at multi-unit nuclear power plants consider risk from each unit separately and do not formally consider interactions between the units. These interactions make the operation of multiple units dependent on each other and should be accounted for in the PRAs. In order to effectively account for these risks in a multi-unit PRA, six main dependence classifications have been created: initiating events, shared connections, identical components, proximity dependencies, human dependencies, and organizational dependencies. This thesis discusses these six classifications that could create dependence between multiple units. As a validation of the classification, this thesis will also discuss multi-unit events that have occurred in operating plants. Finally, this thesis will present existing methodologies that could be used to quantify unit-to-unit dependencies in the PRA for each classification.
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    Automatic Generation of Generalized Event Sequence Diagrams for Guiding Simulation Based Dynamic Probabilistic Risk Assessment of Complex Systems
    (2007-11-27) Nejad-Hosseinian, Seyed Hamed; Mosleh, Ali; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Dynamic probabilistic risk assessment (DPRA) is a systematic and comprehensive methodology that has been used and refined over the past two decades to evaluate the risks associated with complex systems such as nuclear power plants, space missions, chemical plants, and military systems. A critical step in DPRA is generating risk scenarios which are used to enumerate and assess the probability of different outcomes. The classical approach to generating risk scenarios is not, however, sufficient to deal with the complexity of the above-mentioned systems. The primary contribution of this dissertation is in offering a new method for capturing different types of engineering knowledge and using them to automatically generate risk scenarios, presented in the form of generalized event sequence diagrams, for dynamic systems. This new method, as well as several important applications, is described in detail. The most important application is within a new framework for DPRA in which the risk simulation environment is guided to explore more interesting scenarios such as low-probability/high-consequence scenarios. Another application considered is the use of the method to enhance the process of risk-based design.