Physics-Based Stress Corrosion Cracking Component Reliability Model cast in an R7-Compatible Cumulative Damage Framework

Reduction in safety margin can be expected as passive structures and components undergo degradation with time. Limitations in the traditional probabilistic risk assessment (PRA) methodology constrain its value as an effective tool to address the impact of aging effects on risk and for quantifying the impact of aging management strategies in maintaining safety margins. A methodology has been developed to address multiple aging mechanisms involving large numbers of components (with possibly statistically dependent failures) within the PRA framework in a computationally feasible manner when the sequencing of events is conditioned on the physical conditions predicted in a simulation environment, such as the New Generation System Code (NGSC) concept. Both epistemic and aleatory uncertainties can be accounted for within the same phenomenological framework and maintenance can be accounted for in a coherent fashion. The framework accommodates the prospective impacts of various intervention strategies such as testing, maintenance, and refurbishment. The methodology is illustrated with several examples.

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“…The impact of thermal-hydraulic data on the transition rates is mainly through and ̇. This impact is quantified through [13]…”

Section: State Transition Scc Modelmentioning

confidence: 99%

Methodology Development for Passive Component Reliability Modeling in a Multi-Physics Simulation Environment

Aldemir

Denning

Çatalyürek

et al. 2015

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“…Multistate, physics-based models of passive component reliability are currently being developed for integration into the RELAP7/RISMC modeling environment [13][14][15]. A simplified version of a model has been chosen as the basis for a demonstration analysis of alternative uncertainty classifications.…”

Section: Demonstration Problemmentioning

confidence: 99%

“…A model of primary water stress corrosion cracking of reactor coolant system Alloy 82/182 dissimilar metal welds is selected for analysis [13]. This is a potentially risk-significant degradation mechanism in Class 1 piping because of its relevance to loss of coolant accidents.…”

Section: Physical Mechanisms and Modelsmentioning

confidence: 99%

“…A modified version of the Battelle model [13] is adopted. The rupture pressure, P f , is estimated as…”

Section: Rupture Pressurementioning

confidence: 99%

“…This demonstration model is a simplified form of the component model developed for RISMC (which also addresses leaks before break and intervention actions [13]), but it provides a basis for assessing the impact of alternative uncertainty classifications.…”

Section: Rupture Pressurementioning

confidence: 99%

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Robustness of RISMC Insights under Alternative Aleatory/Epistemic Uncertainty Classifications: Draft Report under the Risk-Informed Safety Margin Characterization (RISMC) Pathway of the DOE Light Water Reactor Sustainability Program

Unwin¹,

Eslinger²,

Johnson³

2012

Self Cite

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The Risk-Informed Safety Margin Characterization (RISMC) pathway is a set of activities defined under the U.S. Department of Energy (DOE) Light Water Reactor Sustainability Program. The overarching objective of RISMC is to support plant life-extension decision-making by providing a state-of-knowledge characterization of safety margins in key systems, structures, and components (SSCs). A technical challenge at the core of this effort is to establish the conceptual and technical feasibility of analyzing safety margin in a risk-informed way, which, unlike conventionally defined deterministic margin analysis, would be founded on probabilistic characterizations of uncertainty in SSC performance.In the context of probabilistic risk assessment (PRA) technology, there has arisen a general consensus about the distinctive roles of two types of uncertainty: aleatory and epistemic, where the former represents irreducible, random variability inherent in a system, whereas the latter represents a state of knowledge uncertainty on the part of the analyst about the system which is, in principle, reducible through further research. While there is often some ambiguity about how any one contributing uncertainty in an analysis should be classified, there has nevertheless emerged a broad consensus on the meanings of these uncertainty types in the PRA setting. However, while RISMC methodology shares some features with conventional PRA, it will nevertheless be a distinctive methodology set. Therefore, the paradigms for classification of uncertainty in the PRA setting may not fully port to the RISMC environment. Yet the notion of risk-informed margin is based on the characterization of uncertainty, and it is therefore critical to establish a common understanding of uncertainty in the RISMC setting.The RISMC framework contrasts sharply with the PRA structure in that the underlying models are not inherently aleatory. Rather, they are largely deterministic physical/engineering models. However, there are uncertainties associated with appropriate quantification of many of the model input parameters. The current RISMC paradigm for uncertainty quantification is to adopt the criteria by which epistemic and aleatory uncertainties are distinguished in PRAs (irreducibility, whether the source is random variability, etc.) as the basis for classifying input parameter uncertainties. However, since the underlying structure of RISMC is deterministic and not aleatory, and (almost) all input parameters are purely deterministic, judging whether a given input uncertainty should be viewed as aleatory or deterministic presents more of a challenge. Note that this ambiguity is a well-recognized issue, even in the context of conventional PRA. However, a viewpoint sometimes expressed is that if this ambiguity does not affect the insights from a study relevant to decision-making, then it is unimportant. Our intent in this report is to assess the robustness of study insights to alternative categorizations of uncertainty -addressing the question "does it ma...

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Safety Benefits Assessment for Accident Tolerant Fuels in Consideration of Steam Generator Tube Degradation Using Dynamic Event Tree Analysis

et al. 2023

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Physics-Based Stress Corrosion Cracking Component Reliability Model cast in an R7-Compatible Cumulative Damage Framework

Cited by 6 publications

References 8 publications

Methodology Development for Passive Component Reliability Modeling in a Multi-Physics Simulation Environment

Methodology Development for Passive Component Reliability Modeling in a Multi-Physics Simulation Environment

Robustness of RISMC Insights under Alternative Aleatory/Epistemic Uncertainty Classifications: Draft Report under the Risk-Informed Safety Margin Characterization (RISMC) Pathway of the DOE Light Water Reactor Sustainability Program

Safety Benefits Assessment for Accident Tolerant Fuels in Consideration of Steam Generator Tube Degradation Using Dynamic Event Tree Analysis

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