The identification of potential accident conditions for a nuclear power plant requires a systematic evaluation of postulated hazards, and accurate methods for predicting the behaviour of the system if these hazards were to occur. It is particularly important to identify scenarios which carry severe consequences (e.g., large radioactive releases to the environment), even if the conditions have a low probability of occurrence, so that preventative measures can be implemented. Dynamic probabilistic risk assessment (DPRA) is a field of analysis that aims to determine the failure pathways of complex systems while simultaneously analyzing the time-evolution of the proposed accident. By studying the dynamics of the system, DPRA methods are capable of analyzing the impact of impaired or late equipment response, human actions during the transient, and the inter relationship between different systems and failures. This approach promotes realistic predictions of the complex response of the system under accident conditions, and for the dynamics of the accident progression to unfold with timing that is not pre-determined by an analyst, thereby removing potential user bias from the results. The work that is outlined in this thesis was undertaken in order to demonstrate the DPRA software platform called RAVEN, and to leverage its application in the near-future probabilistic assessment of accident conditions applied to CANDU reactor simulation models. Features of the work include: • Demonstration of the capability of RAVEN to produce predictable results using the dynamic event tree (DET) approach; • The development of a code interface to allow RAVEN to drive DET simulations of TRACE simulation models; and • Demonstration of the capability of the developed RAVEN-TRACE interface to produce predictable results for systems that are well-understood. / Thesis / Master of Applied Science (MASc)

The Fukushima Daiichi accident prompted the nuclear community to find a new solution to reduce the risky situations in nuclear power plants (NPPs) due to beyond-design-basis external events (BDBEEs). An implementation guide for diverse and flexible coping strategies (FLEX) has been presented by Nuclear Energy Institute (NEI) to manage the challenge of BDBEEs and to enhance reactor safety against extended station blackout (SBO). To assess the effectiveness of FLEX strategies, probabilistic risk assessment (PRA) methods can be used to calculate the reliability of such systems. Due to the uniqueness of FLEX systems, these systems can potentially carry dependencies among components not commonly modeled in NPPs. Therefore, a suitable method is needed to analyze the reliability of FLEX systems in nuclear reactors. This thesis investigates the effectiveness and applicability of Bayesian networks (BNs) and Discrete-Time Bayesian Networks (DTBNs) in the reliability analysis of FLEX equipment that is utilized to reduce the risk in nuclear power plants. To this end, the thesis compares BNs with two other reliability assessment methods: Fault Tree (FT) and Markov chain (MC). Also, it is shown that these two methods can be transformed into BN to perform the reliability analysis of FLEX systems. The comparison of the three reliability methods is shown and discussed in three different applications. The results show that BNs are not only a powerful method in modeling FLEX strategies, but it is also an effective technique to perform reliability analysis of FLEX equipment in nuclear power plants. / Master of Science / Some external events like earthquakes, flooding, and severe wind, may cause damage to the nuclear reactors. To reduce the consequences of these damages, the Nuclear Energy Institute (NEI) has proposed mitigating strategies known as FLEX (Diverse and Flexible Coping Strategies). After the implementation of FLEX in nuclear power plants, we need to analyze the failure or success probability of these engineering systems through one of the existing methods. However, the existing methods are limited in analyzing the dependencies among components in complex systems. Bayesian networks (BNs) are a graphical and quantitative technique that is utilized to model dependency among events. This thesis shows the effectiveness and applicability of BNs in the reliability analysis of FLEX strategies by comparing it with two other reliability analysis tools, known as Fault Tree Analysis and Markov Chain. According to the reliability analysis results, BN is a powerful and promising method in modeling and analyzing FLEX strategies.

A probabilistic risk assessment (PRA) for the discharge of excessive nitrogen from nonpoint sources (NPSs) to a stream was performed for a small agricultural watershed in northern Virginia. Risk, by definition, is the product of the frequency of occurrence of an event and the consequences of that event. The purpose of this research was to determine the probability of occurrence of a nitrogen discharge event (i.e., frequency). The consequences of such a discharge event were not explicitly determined but were implicitly assumed to be negative in nature. An event tree was developed to show the basic hydrologic processes at work in a small watershed. However, the event tree could not be used to discover the causes for nitrogen loss from the watershed. Therefore, a fault tree was developed for excessive nitrogen discharge in surface runoff on any day from agricultural sources. The development of the fault tree was found to be a useful exercise in understanding the intricate cause and effect relationships between agricultural practices and NPS pollution. Based on the results, the fault tree methodology might be used as an effective teaching or communication tool. The fault tree was also evaluated quantitatively to determine a probability of occurrence for excessive nitrogen discharge to the stream on any day. Land use, fertilization, monitoring, and long-term weather records were used in conjunction with scientific judgment and expert opinion to establish the probabilities within the fault tree and to calculate the overall probability of nitrogen discharge to the stream on any day. The results obtained from the fault tree calculations tend to underestimate the importance of cropland best management practices (BMPs) over the long term, because the fault tree was developed on a daily basis (i.e., every day in a year has the same probability of a discharge event occurring). A more accurate depiction of the NPS pollution control problem was achieved by assuming the occurrence of a runoff event. A second fault tree was presented for the discharge of excessive nitrogen to the stream during a runoff event. The quantitative assessment of the new fault tree showed more clearly the impact of BMPs on reducing the likelihood of nitrogen discharge. A 0.15 decrease in the probability of nitrogen discharge during a runoff event was calculated for the Owl Run watershed from 1987 to 1993 due to the effects of BMPs installed during that time period. A 0.20 decrease was calculated for an Owl Run subwatershed for the same time period. This subwatershed isolated two major dairy operations and the effects of the BMPs installed for those dairies. Despite the success of the fault tree in mirroring changes within the watershed, the amount of data and time required to perform the quantitative assessment may limit its use in the NPS pollution control field. The basic nature of the fault tree technique also limits its usefulness in the field. One such limitation is that degrees of events cannot be expressed. For example, a BMP is either present or not present on a fault tree. There can be no indication of how effective the BMP is in preventing NPS pollution without substantially increasing the level of detail displayed by the tree. Another limitation is that the ultimate result of the fault tree calculations is a probability of occurrence. This value is not as easily understood as the output of NPS pollution computer models, for example, where the output has specific meaning and units (e.g., milligrams of nitrogen per liter of runoff). The qualitative fault tree, however, has the advantage over computer models when it comes to understanding the concepts behind the technique and being able to see the cause and effect relationships at work in the watershed. Laypersons can understand the fault tree more easily than the complex computer code and intricate equations of models. / Master of Science

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The current method of determining component failure rates for probabilistic risk assessment (PRA) in the nuclear industry is to take the total number of failures divided by the time over which the failures occurred. The method proposed in this study is the reliability growth method and involves taking into account the fact that the amount of failures per additional year of operation generally decreases yearly because the operational staff becomes familiar with the equipment. The reliability growth method will result in lower component failure rates which when used in PRA studies could result in a lower core melt frequency value. The component failure rate would be expected to be higher in the early stages and should gradually decrease as time goes on. This study will compare the final core melt frequency of the Trojan Nuclear Power Plant using both methods. The Nuclear Power Reactor Data System (NPRDS) data base from the Institute of Nuclear Power Operations (INPO) was used in this study. The components which were examined for the reliability growth method are motor operated valves, service water pump/motors and emergency diesel generator air chargers. These data were screened to ensure that only true failures were reported. A comparison was made of the overall core melt frequency between the conventional failure rate method and reliability growth method for the motor operated valves. The overall core melt frequency was decreased by 1.8 % when using the reliability growth method compared to the conventional method. / Graduation date: 1993

Electrical cabinet fires occur frequently in commercial and industrial facilities. However, the severity of these fire events varies widely, making it difficult to estimate the fire growth and size with certainty. This study aims to identify the significant parameters that affect electrical cabinet fires, which are quantified as the heat release rate (HRR), and properly categorize them. With this knowledge, optimal parameter-response relationships can be developed to predict the electrical cabinet fire behavior. Statistical analysis conducted in this study on historical fire incident data revealed that the fires in Nuclear Power Plants (NPP) were primarily associated with electrical cabinets. The database used in this research was an electronic version of the publicly available Updated Fire Event Database developed by Electric Power Research Institute, including 2,111 fire events. 540 of these events were labeled as being challenging fires with 74.2% of these challenging fire events being due to eleven selected fire types. Electrical cabinets were found to represent a majority (40.7%) of all the challenging fire events. Although historically conducted electrical cabinet fire experiments sought to explore the influence of parameters on HRR, the parameters were not systematically varied to statistically quantify which parameters were most important/relevant. Research in this study used statistical analysis on a series of simulation results on electrical cabinet fires from the computational fluid dynamics code Fire Dynamic Simulator (FDS). Simulation matrices were developed and evaluated using fractional factorial Design of Experiments (DOE) to screen the importance of different parameters on the electric cabinet HRR. Based on statistical analysis of the results, the combustible material surface area was found to be the most significant parameter followed by cabinet volume, combustible configuration, burning duration, and combustible material heat release rate per unit area. Material ignition temperature was found to not be statistically significant. The last phase of this research assessed the robustness of the electrical cabinet parameters on the predicted HRR with more detailed simulations. Two investigations were undertaken. To identify the nonlinear effects of parameters on the electrical cabinet fire HRR, a Response Surface Methodology (RSM) based Central Composite Design (CCD) was used to create a simulation matrix that would allow statistical analysis of important parameters as well as their effects on the fire heat release rate while keeping the combustible configuration inside the cabinet constant. A series of simulations were conducted to explore the impact of combustible configuration and ignition source location while keeping all other variables consistent. The analysis revealed that all variables had a statistically significant effect on peak HRR. For the average HRR, both the ventilation area into the cabinet and the ignition source HRR were found to be statistically insignificant. For both output variables, the cabinet volume, material heat release rate per unit area, and material surface area were the most significant parameters. Combustible configuration and ignition source location were also found to be statistically significant. / Master of Science / Electrical cabinet fires are a significant concern for industries, commercial electric plants, telecommunication buildings, and nuclear power plant (NPP) facilities. These cabinets typically represent a metallic enclosure of varying sizes. Additionally, several different electronic components of heterogenous composition and configuration are included within this cabinet. As a result, the fires within the cabinet can propagate to several other nearby components, resulting in large fires that are difficult to suppress. Thus, it becomes necessary to understand the fire behavior of electrical cabinets and the factors influencing fire propagation. Knowing the factors influencing the electrical cabinet fires will enable facilities to have better fire resilience and further prevent multiple components and structures from being damaged by these fires. Statistical analysis of historic fire events validated that the most frequently challenging fires in NPP involve electrical cabinets.Therefore, aA detailed study was conducted to investigate what parameters most significantly affect the size of the electrical cabinet fire, which is quantified as the heat release rate (HRR). The parameters in the study included cabinet volume, ventilation area, combustible fuel detail (ignition temperature, heat release rate per unit area (HRRPUA), burning duration), fuel configuration inside the cabinet, and size of the ignition source. To determine which of these factors significantly impacted the electrical cabinet HRR, a computational fluid dynamics code Fire Dynamic Simulator (FDS), was used to predict the fire growth of electrical cabinet fires. After employing a rigorous statistical analysis of the FDS results, the combustible material surface area was found to be the most significant parameter, followed by cabinet volume, combustible configuration, burning duration, and flammable material HRRPUA. The last phase of the research sought to explore the significance of the parameters while developing a nonlinear expression to predict the fire HRR based on cabinet parameters. Given the wide range of electrical cabinet parameters, especially combustible configuration, two studies were conducted where the configuration was fixed or varying with respect to other parameters. For fixed combustible configuration, simulations were conducted with FDS systematically varying the other parameters so their importance could be ranked. Simulations were also performed with all parameters fixed except the combustible configuration and ignition source location. The analysis revealed that all variables had a statistically significant impact on peak HRR. For the average HRR, both the ventilation area into the cabinet and the ignition source HRR were found to be statistically insignificant. For both output variables, the cabinet volume, material heat release rate per unit area, and material surface area were found to be the most significant parameters. Combustible configuration and ignition source location were also found to be statistically significant.

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In an effort to apply Cumulative Risk Assessment (CRA) as developed by the U.S. EPA, the present study investigates the cumulative risks to Eastern oysters due to multiple stressors such as salinity, temperature and oxygen and carbon dioxide. I also compared the effectiveness of the Hazard Quotient Method (HQ) in CRA. Ambient conditions in the James River, VA were obtained from the Virginia DEQ database and respiratory responses were estimated using values from the literature. The multiple environmental stresses are evaluated using a probabilistic analysis that combines the environmental conditions. It was concluded that salinity was the most influential stressor in the model. Other risks were identified contributing to the vulnerability of the oysters. Crystal Ball simulations yielded that the oxygen uptake of oysters reduced by more than 29%. The HQ method was found to be inappropriate in analyzing cumulative risks for CRA. Oyster populations are dramatically declining in the James River and the Chesapeake Bay. Hence, effective oyster restoration activities are underway to rebuild oyster populations in the James River and throughout the Bay area.

Common cause failures (CCF) are a significant contributor to risk in complex technological systems, such as nuclear power plants. Many probabilistic parametric models have been developed to quantify the systems subject to the CCF. Existing models include the beta factor model, the multiple Greek letter model, the basic parameter model, the alpha factor model and the binomial failure rate model. These models are often only capable of providing a point estimate, when there are limited CCF data available. Some recent studies have proposed a Bayesian approach to quantify the uncertainties in CCF modeling, but they are limited in addressing the uncertainty in the common failure factors only. This thesis presents a multivariate Poisson model for CCF modeling, which combines the modeling of individual and common cause failures into one process. The key idea of the approach is that failures in a common cause component group of n components are decomposed into superposition of k (>n) independent Poisson processes. Empirical Bayes method is utilized for simultaneously estimating the independent and common cause failure rates which are mutually exclusive. In addition, the conventional CCF parameters can be evaluated using the outcomes of the new approach. Moreover, the uncertainties in the CCF modeling can also be addressed in an integrated manner. The failure rate is estimated as the mean value of the posterior density function while the variance of the posterior represents the variation of the estimate. A MATLAB program of the Monte Carlo simulation was developed to check the behavior of the proposed multivariate Poisson (MVP) model. Superiority over the traditional CCF models has been illustrated. Furthermore, due to the rarity of the CCF data observed at one nuclear power plant, data of the target plant alone are insufficient to produce reliable estimates of the failure rates. Data mapping has been developed to make use of the data from source plants of different sizes. In this thesis, data mapping is combined with EB approach to partially assimilate information from source plants and also respect the data of the target plant. Two case studies are presented using different database. The results are compared to the empirical values provided by the United States Nuclear Regulatory Commission (USNRC).