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

Abstract

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...