Nearly half of the U.S. nuclear power plants (NPPs) are in the process of transitioning, or have already transitioned, to a risk-informed, performance-based fire protection program. For this transition, Fire Probabilistic Risk Assessment (Fire PRA) is used as a foundation for fire risk evaluation. To increase realism in Fire PRA by reducing conservative bias, the authors have developed an Integrated Probabilistic Risk Assessment (I-PRA) methodological framework that does not require major changes to the existing plant Probabilistic Risk Assessments (PRAs). The underlying failure mechanism models associated with fire events are developed in a separate module, which can be interfaced and connected to the existing plant PRA. This paper explains the areas of methodological advancements in I-PRA, comparing them with the existing Fire PRA of NPPs. This comparison is further demonstrated in a realistic case study that applies the I-PRA framework to a critical fire-induced scenario at an NPP. The core damage frequency (CDF) for the selected scenario, computed by the I-PRA framework, is compared with the results of the Full Compartment Burn screening method and the existing Fire PRA methodology. Using the I-PRA framework, the CDF for the selected scenario has decreased by a factor of 20 compared with the Full Compartment Burn screening approach and by a factor of 2 compared to the existing Fire PRA methodology based on NUREG/CR-6850 and the subsequent NUREGs that have updated the data and methods for individual steps.

An integrated probabilistic risk assessment framework combines spatio-temporal probabilistic simulations of underlying failure mechanisms with classical probabilistic risk assessment logic consisting of event trees and fault trees. The importance measure methods commonly used in classical probabilistic risk assessment, for example, Fussell–Vesely importance measure and Risk Achievement Worth, generate the ranking of components at the basic event level utilizing the one-at-a-time and local methods. These classical importance measure methods, however, are not sufficient for integrated probabilistic risk assessment where, in addition to the component-level risk importance ranking, the ranking of risk-contributing factors (e.g. physical design parameters) associated with the underlying failure mechanisms is desired. In this research, the global importance measure method is suggested and implemented for the integrated probabilistic risk assessment framework. The global importance measure can account for four key aspects of integrated probabilistic risk assessment, including (a) ranking of input parameters at the failure mechanism level based on the contribution to the system risk metrics, (b) uncertainty of input parameters, (c) non-linearity and interactions among input parameters inside the model, and (d) uncertainty associated with the system risk estimates; therefore, it enhances the accuracy of risk importance ranking when the risk model has a high level of non-linearity, interactions, and uncertainties. This article shows (a) qualitative justifications for the selection of the global importance measure method for the integrated probabilistic risk assessment framework and (b) quantitative proof of concept using three case studies, including two illustrative fault tree examples and one practical application of the integrated probabilistic risk assessment framework for Generic Safety Issue 191 at nuclear power plants (a sump blockage issue following a loss-of-coolant accident).