Integrated PRA methodology to advance fire risk modeling for nuclear power plants

ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering

Mohaghegh

Kee

2019

Fire is one of the most critical initiating events that can lead to core damage in nuclear power plants (NPPs). To evaluate the potential vulnerability of plants to fire hazards, fire probabilistic risk assessment (PRA) is commonly conducted. Manual fire protection features, performed by the first responders (e.g., fire brigade), play a key role in preventing and mitigating fire-induced damage to the plant systems. In the current fire PRA methodology of NPPs, there are two main gaps in the modeling of manual fire protection features: (i) the quantification of the first responder performance is solely based on empirical data (industry-wide historical fire events), and so the plant-specific design and conditions cannot be explicitly considered; and (ii) interactions of first responders with fire propagation are not fully captured. To address these challenges, the authors develop a model-based approach, grounded on human reliability analysis (HRA) and coupled with the fire dynamics simulator (FDS), to model the first responder performance more realistically and consider the interface between the first responder performance and fire propagation more explicitly. In this paper, the HRA-based approach is implemented in an integrated PRA (I-PRA) methodological framework for fire PRA and applied to a switchgear room fire scenario of an NPP. The proposed model-based approach (a) adds more realism to fire PRA and so to risk assessment in NPPs and (b) provides opportunities for sensitivity and importance measure analyses with respect to design conditions; therefore, contributes to risk management in NPPs.

Section: Integrated Probabilistic Risk Assessment Framework For Fire mentioning

confidence: 83%

“…To address the excessive conservatism associated with areas #2 and #3, the authors of this paper developed the I-PRA framework ( Fig. 1) [17][18][19][20][21][22].…”

Section: Integrated Probabilistic Risk Assessment Framework For Fire mentioning

confidence: 99%

See 1 more Smart Citation

Human Reliability Analysis-Based Method for Manual Fire Suppression Analysis in an Integrated Probabilistic Risk Assessment

ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering

Mohaghegh

Kee

2019

“…The SoTeRiA Laboratory at UIUC was the first to conduct quantitative screening using the Morris EE method and to compute the moment-independent Global IM for Fire PRA of NPPs [27,28,72]. This current research has advanced the previous studies by the SoTeRiA Laboratory at UIUC [27,28,72] [73] conducted the identification, review and screening, characterization, and propagation of uncertainty sources in the existing Fire PRA models. Uncertainty quantification was performed using Monte Carlo simulation.…”

Section: Concluding Remarks: Contributions and Benefitsmentioning

confidence: 95%

Risk Importance Ranking of Fire Data Parameters to Enhance Fire PRA Model Realism

Biersdorf

Bui

et al. 2020

Fire is historically and analytically a significant contributor to nuclear power plant risk. The level of fire risk and the methods, tools, and data for modeling this risk are highly debated by experts. One area of debate is the input data used in fire modeling and how to deal with this data's high rate of uncertainty. This report outlines work performed for determining the key parameters causing this uncertainty and how it propagates into nuclear power plant PRA models. This research used the Integrated Probabilistic Risk Assessment (I-PRA) method and paves the way to correctly assess and reduce data uncertainty in fire modeling.

“…[12][13][14][15][16][17][18][19][20][21][22][23] The I-PRA framework (Figure 1) incorporates the underlying science of accident causation (i.e. physical and social failure mechanisms) into classical PRA (plant-specific PRA module in Figure 1) associated with certain areas of safety concern, for example, internal fire 14,15,17,20,22 and Generic Safety Issue 191 (GSI-191), i.e., a sump blockage issue following a loss-of-coolant accident at an NPP. 12,21,23 I-PRA is a multi-level risk assessment framework that begins with the simulation of dominant failure mechanisms (e.g.…”

Section: Introductionmentioning

confidence: 99%

Global importance measure methodology for integrated probabilistic risk assessment

Proceedings of the Institution of Mechanical Engineers, Part O:

Reihani

Kee

et al. 2019

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).