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Title: RISK MONITORING CAPABILITES FROM DYNAMIC PRA DATA

Abstract

Risk Monitors (RMs) are software tools that are employed in nuclear power plants to monitor the safety properties of the plant itself during operation. These safety properties are generated using classical PRA methods which are typically based on static Boolean structures such as Fault-Trees (FTs) and Event-Trees (ETs). The outcomes of these methods are Core Damage Frequency (CDF) and risk importance values of plant components (e.g., valves and pumps). Examples of Risk Importance Measures (RIMs) commonly used are: Risk Achievement Worth (RAW), Risk Reduction Worth (RRW), Birnbaum (B) and Fussell-Vesely (FV) [1,2]. In a RM these values are updated in real time once plant components status is updated (e.g., under maintenance, off-line, testing). In addition, RMs can be used to optimize maintenance schedules so those safety features of the plant are below plant standard values. This paper presents an approach to create temporal profiles of RIMs (i.e., RM capabilities) given a temporal change of the plant configuration. Instead of employing data generated by ETs and FTs, the proposed approach will employ data generated by Dynamic PRA methods. In contrast to classical PRA methods, Dynamic PRA methods couple stochastic methods with safety analysis codes to determine risk associate to complex systemsmore » such as nuclear plants. Compared to classical PRA methods, they can evaluate with higher resolution the safety impact of timing and sequencing of events on the accident progression. We will describe how classical RIMs can be generated by Dynamic PRA data and then we will show how the temporal profiles of the RIMs can be generated given a temporal profile of the system configuration.« less

Authors:
ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]
  1. Idaho National Laboratory
Publication Date:
Research Org.:
Idaho National Lab. (INL), Idaho Falls, ID (United States)
Sponsoring Org.:
USDOE Office of Nuclear Energy (NE)
OSTI Identifier:
1478763
Report Number(s):
INL/CON-17-42296-Rev000
DOE Contract Number:  
AC07-05ID14517
Resource Type:
Conference
Resource Relation:
Conference: ANS Winter Meeting, Washington DC, 10/29/2017 - 11/02/2017
Country of Publication:
United States
Language:
English
Subject:
97 - MATHEMATICS AND COMPUTING; PRA; risk monitor

Citation Formats

Diego, Mandelli,, Andrea, Alfonsi,, and L, Smith, Curtis. RISK MONITORING CAPABILITES FROM DYNAMIC PRA DATA. United States: N. p., 2017. Web.
Diego, Mandelli,, Andrea, Alfonsi,, & L, Smith, Curtis. RISK MONITORING CAPABILITES FROM DYNAMIC PRA DATA. United States.
Diego, Mandelli,, Andrea, Alfonsi,, and L, Smith, Curtis. Wed . "RISK MONITORING CAPABILITES FROM DYNAMIC PRA DATA". United States. https://www.osti.gov/servlets/purl/1478763.
@article{osti_1478763,
title = {RISK MONITORING CAPABILITES FROM DYNAMIC PRA DATA},
author = {Diego, Mandelli, and Andrea, Alfonsi, and L, Smith, Curtis},
abstractNote = {Risk Monitors (RMs) are software tools that are employed in nuclear power plants to monitor the safety properties of the plant itself during operation. These safety properties are generated using classical PRA methods which are typically based on static Boolean structures such as Fault-Trees (FTs) and Event-Trees (ETs). The outcomes of these methods are Core Damage Frequency (CDF) and risk importance values of plant components (e.g., valves and pumps). Examples of Risk Importance Measures (RIMs) commonly used are: Risk Achievement Worth (RAW), Risk Reduction Worth (RRW), Birnbaum (B) and Fussell-Vesely (FV) [1,2]. In a RM these values are updated in real time once plant components status is updated (e.g., under maintenance, off-line, testing). In addition, RMs can be used to optimize maintenance schedules so those safety features of the plant are below plant standard values. This paper presents an approach to create temporal profiles of RIMs (i.e., RM capabilities) given a temporal change of the plant configuration. Instead of employing data generated by ETs and FTs, the proposed approach will employ data generated by Dynamic PRA methods. In contrast to classical PRA methods, Dynamic PRA methods couple stochastic methods with safety analysis codes to determine risk associate to complex systems such as nuclear plants. Compared to classical PRA methods, they can evaluate with higher resolution the safety impact of timing and sequencing of events on the accident progression. We will describe how classical RIMs can be generated by Dynamic PRA data and then we will show how the temporal profiles of the RIMs can be generated given a temporal profile of the system configuration.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2017},
month = {11}
}

Conference:
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