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 Improving the Foundational Knowledge of Dependency in Human Reliability Analysis(2023) Paglioni, Vincent Philip; Groth, Katrina M; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Human reliability analysis (HRA) is the field tasked with understanding, modeling, and quantifying the human contribution to the reliability of complex engineering systems. Human machine teams (HMTs, the groups of operators and human-system interface technologies that together control a system) currently contribute to over 60% of industrial accidents and will continue to servean important operational role in complex engineering systems. As a result, it is critical to develop robust methods for characterizing HMT performance and reliability. One of the factors limiting the technical basis of HRA is the treatment of dependency, how task performances and influencing factors are causally connected. Currently, HRA does not have a sound framework for conceptualizing, modeling, or quantifying dependency. The concept of dependency is poorly defined, the modeling is lacks a causal basis, and the quantification of dependency is unsupported by literature or data. This research closes these gaps in the foundations of HRA dependency by enforcing a rigorous, quantitative causal basis for the conceptualization and modeling of dependency. First, this research addresses the definitional and conceptual foundations of HRA dependency to provide a consistent technical basis for the field. This work proposes a single, complete, and appropriate definition for the general concept of dependency; one that is rooted in causality. This research also provides definitions for dependency-related concepts from multiple fields including probability, statistics, and set theory. The definitional basis laid out by this work standardizes the foundations of the field and promotes the ability to more easily translate between previously disparate HRA methods. Second, this work develops the causal structure of dependency in HRA. Whereas current methods for dependency modeling in HRA focus on correlational attributes, this method recognizes that causality, not correlation, is the driving mechanism of dependency. This work identifies six distinct relationship archetypes (idioms) that describe the general dependency relationships possible between HRA variables. Furthermore, this work creates the graphical structures that describe the idioms using Bayesian Networks (BNs) as the modeling architecture. The task/function-level idiom structures created in this work provide robust, traceable models of dependency relationships that can be used to both build HRA models and decompose full models into more understandable pieces. Third, this work develops the methodology to build and quantify causal, formative dependency BN HRA models using the idiom structures and HRA data. Whereas many HRA methods rely on expert elicitation alone for assigning probabilities, this methodology quantifies the network directly from HRA data. The methodology developed in this work produces a full, causal, formative dependency scenario model without requiring expert elicitation of probabilities. This methodology is implemented to build and quantify a scenario model using real HRA data collected from operator crews working in a full-scope nuclear reactor simulator, which shows both that causal dependency can be modeled and quantified, and that the methodology is traceable and useful. Finally, this work develops a set of recommendations for the collection, storage, and use of HRA data, and for the implementation of this methodology within mature HRA frameworks. This dissertation will improve our knowledge of, and ability to model, dependency in human reliability.Item Development of Approaches to Common Cause Dependencies with Applications to Multi-Unit Nuclear Power Plant(2018) Zhou, Taotao; Modarres, Mohammad; Droguett, Enrique López; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The term “common cause dependencies” encompasses the possible mechanisms that directly compromise components performances and ultimately cause degradation or failure of multiple components, referred to as common cause failure (CCF) events. The CCF events have been a major contributor to the risk posed by the nuclear power plants and considerable research efforts have been devoted to model the impacts of CCF based on historical observations and engineering judgment, referred to as CCF models. However, most current probabilistic risk assessment (PRA) studies are restricted to single reactor units and could not appropriately consider the common cause dependencies across reactor units. Recently, the common cause dependencies across reactor units have attracted a lot of attention, especially following the 2011 Fukushima accident in Japan that involved multiple reactor unit damages and radioactive source term releases. To gain an accurate view of a site's risk profile, a site-based risk metric representing the entire site rather than single reactor unit should be considered and evaluated through a multi-unit PRA (MUPRA). However, the multi-unit risk is neither formally nor adequately addressed in either the regulatory or the commercial nuclear environments and there are still gaps in the PRA methods to model such multi-unit events. In particular, external events, especially seismic events, are expected to be very important in the assessment of risks related to multi-unit nuclear plant sites. The objective of this dissertation is to develop three inter-related approaches to address important issues in both external events and internal events in the MUPRA. 1) Develop a general MUPRA framework to identify and characterize the multi-unit events, and ultimately to assess the risk profile of multi-unit sites. 2) Develop an improved approach to seismic MUPRA through identifying and addressing the issues in the current methods for seismic dependency modeling. The proposed approach can also be extended to address other external events involved in the MUPRA. 3) Develop a novel CCF model for components undergoing age-related degradation by superimposing the maintenance impacts on the component degradation evolutions inferred from condition monitoring data. This approach advances the state-of-the-art CCF analysis in general and assists in the studies of internal events of the MUPRA.Item A MODEL-BASED HUMAN RELIABILITY ANALYSIS METHODOLOGY (PHOENIX METHOD)(2013) Ekanem, Nsimah J.; MOSLEH, ALI; Reliability Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Despite the advances made so far in developing human reliability analysis (HRA) methods, many issues still exist. Most notable are; the lack of an explicit causal model that incorporates relevant psychological and cognitive theories in its core human performance model, inability to explicitly model interdependencies between human failure events (HFEs) and influencing factors on human performance, lack of consistency, traceability and reproducibility in HRA analysis. These issues amongst others have contributed to the variability in results seen in the application of different HRA methods and even in cases where the same method is applied by different analysts. In an attempt to address these issues, a framework for a model-based HRA methodology has been recently proposed which incorporates strong elements of current HRA good practices, leverages lessons learned from empirical studies and the best features of existing and emerging HRA methods. This research completely develops this methodology which is aimed at enabling a more credible, consistent, and accurate qualitative and quantitative HRA analysis. The complete qualitative analysis procedure (including a hierarchical performance influencing factor set) and a causal model using Bayesian Belief network (BBN) have been developed to explicitly model the influence and dependencies among HFEs and the different factors that influence human performance. This model has the flexibility to be modified for interfacing with existing methods like Standard-Plant-Analysis-Risk-HRA-method. Also, the quantitative analysis procedure has been developed, incorporating a methodology for a cause-based explicit treatment of dependencies among HFEs, which has not been adequately addressed by any other HRA method. As part of this research, information has been gathered from sources (including other HRA methods, NPP operating experience, expert estimates), analyzed and aggregated to provide estimates for the model parameters needed for quantification. While the specific instance of this HRA method is used in nuclear power plants, the methodology itself is generic and can be applied in other environments.