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Title: DEGRADATION SUSCEPTIBILITY METRICS AS THE BASES FOR BAYESIAN RELIABILITY MODELS OF AGING PASSIVE COMPONENTS AND LONG-TERM REACTOR RISK

Abstract

Conventional probabilistic risk assessments (PRAs) are not well-suited to addressing long-term reactor operations. Since passive structures, systems and components are among those for which refurbishment or replacement can be least practical, they might be expected to contribute increasingly to risk in an aging plant. Yet, passives receive limited treatment in PRAs. Furthermore, PRAs produce only snapshots of risk based on the assumption of time-independent component failure rates. This assumption is unlikely to be valid in aging systems. The treatment of aging passive components in PRA does present challenges. First, service data required to quantify component reliability models are sparse, and this problem is exacerbated by the greater data demands of age-dependent reliability models. A compounding factor is that there can be numerous potential degradation mechanisms associated with the materials, design, and operating environment of a given component. This deepens the data problem since the risk-informed management of materials degradation and component aging will demand an understanding of the long-term risk significance of individual degradation mechanisms. In this paper we describe a Bayesian methodology that integrates the metrics of materials degradation susceptibility being developed under the Nuclear Regulatory Commission's Proactive Management of Materials of Degradation Program with available plant service datamore » to estimate age-dependent passive component reliabilities. Integration of these models into conventional PRA will provide a basis for materials degradation management informed by the predicted long-term operational risk.« less

Authors:
; ; ;
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1036931
Report Number(s):
PNNL-SA-78798
TRN: US1201482
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Conference
Resource Relation:
Conference: Proceedings of the ASME 2011 Pressure Vessels & Piping Conference (PVP2011), July 17-21, 2011, Baltimore, Maryland, Paper No. PVP2011-58073
Country of Publication:
United States
Language:
English
Subject:
22 GENERAL STUDIES OF NUCLEAR REACTORS; AGING; DESIGN; MANAGEMENT; METRICS; PRESSURE VESSELS; REACTOR OPERATION; RELIABILITY; RISK ASSESSMENT; Reactor Aging Management; PRA; Passive Components; Bayesian

Citation Formats

Unwin, Stephen D., Lowry, Peter P., Toyooka, Michael Y., and Ford, Benjamin E.. DEGRADATION SUSCEPTIBILITY METRICS AS THE BASES FOR BAYESIAN RELIABILITY MODELS OF AGING PASSIVE COMPONENTS AND LONG-TERM REACTOR RISK. United States: N. p., 2011. Web.
Unwin, Stephen D., Lowry, Peter P., Toyooka, Michael Y., & Ford, Benjamin E.. DEGRADATION SUSCEPTIBILITY METRICS AS THE BASES FOR BAYESIAN RELIABILITY MODELS OF AGING PASSIVE COMPONENTS AND LONG-TERM REACTOR RISK. United States.
Unwin, Stephen D., Lowry, Peter P., Toyooka, Michael Y., and Ford, Benjamin E.. Sun . "DEGRADATION SUSCEPTIBILITY METRICS AS THE BASES FOR BAYESIAN RELIABILITY MODELS OF AGING PASSIVE COMPONENTS AND LONG-TERM REACTOR RISK". United States.
@article{osti_1036931,
title = {DEGRADATION SUSCEPTIBILITY METRICS AS THE BASES FOR BAYESIAN RELIABILITY MODELS OF AGING PASSIVE COMPONENTS AND LONG-TERM REACTOR RISK},
author = {Unwin, Stephen D. and Lowry, Peter P. and Toyooka, Michael Y. and Ford, Benjamin E.},
abstractNote = {Conventional probabilistic risk assessments (PRAs) are not well-suited to addressing long-term reactor operations. Since passive structures, systems and components are among those for which refurbishment or replacement can be least practical, they might be expected to contribute increasingly to risk in an aging plant. Yet, passives receive limited treatment in PRAs. Furthermore, PRAs produce only snapshots of risk based on the assumption of time-independent component failure rates. This assumption is unlikely to be valid in aging systems. The treatment of aging passive components in PRA does present challenges. First, service data required to quantify component reliability models are sparse, and this problem is exacerbated by the greater data demands of age-dependent reliability models. A compounding factor is that there can be numerous potential degradation mechanisms associated with the materials, design, and operating environment of a given component. This deepens the data problem since the risk-informed management of materials degradation and component aging will demand an understanding of the long-term risk significance of individual degradation mechanisms. In this paper we describe a Bayesian methodology that integrates the metrics of materials degradation susceptibility being developed under the Nuclear Regulatory Commission's Proactive Management of Materials of Degradation Program with available plant service data to estimate age-dependent passive component reliabilities. Integration of these models into conventional PRA will provide a basis for materials degradation management informed by the predicted long-term operational risk.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2011},
month = {7}
}

Conference:
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