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Title: A Mechanistic Reliability Assessment of RVACS and Metal Fuel Inherent Reactivity Feedbacks

Abstract

GE Hitachi Nuclear Energy (GEH) and Argonne National Laboratory (Argonne) participated in a two year collaboration to modernize and update the probabilistic risk assessment (PRA) for the PRISM sodium fast reactor. At a high level, the primary outcome of the project was the development of a next-generation PRA that is intended to enable risk-informed prioritization of safety- and reliability-focused research and development. A central Argonne task during this project was a reliability assessment of passive safety systems, which included the Reactor Vessel Auxiliary Cooling System (RVACS) and the inherent reactivity feedbacks of the metal fuel core. Both systems were examined utilizing a methodology derived from the Reliability Method for Passive Safety Functions (RMPS), with an emphasis on developing success criteria based on mechanistic system modeling while also maintaining consistency with the Fuel Damage Categories (FDCs) of the mechanistic source term assessment. This paper provides an overview of the reliability analyses of both systems, including highlights of the FMEAs, the construction of best-estimate models, uncertain parameter screening and propagation, and the quantification of system failure probability. In particular, special focus is given to the methodologies to perform the analysis of uncertainty propagation and the determination of the likelihood of violating FDCmore » limits. Additionally, important lessons learned are also reviewed, such as optimal sampling methodologies for the discovery of low likelihood failure events and strategies for the combined treatment of aleatory and epistemic uncertainties.« less

Authors:
; ; ;
Publication Date:
Research Org.:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org.:
USDOE Office of Nuclear Energy
OSTI Identifier:
1373116
DOE Contract Number:
AC02-06CH11357
Resource Type:
Conference
Resource Relation:
Conference: 2017 International Topical Meeting on Probabilistic Safety Assessment and Analysis, 09/24/17 - 09/28/17, Pittsburgh, PA, USA
Country of Publication:
United States
Language:
English

Citation Formats

Grabaskas, David, Brunett, Acacia J., Passerini, Stefano, and Grelle, Austin. A Mechanistic Reliability Assessment of RVACS and Metal Fuel Inherent Reactivity Feedbacks. United States: N. p., 2017. Web.
Grabaskas, David, Brunett, Acacia J., Passerini, Stefano, & Grelle, Austin. A Mechanistic Reliability Assessment of RVACS and Metal Fuel Inherent Reactivity Feedbacks. United States.
Grabaskas, David, Brunett, Acacia J., Passerini, Stefano, and Grelle, Austin. Sun . "A Mechanistic Reliability Assessment of RVACS and Metal Fuel Inherent Reactivity Feedbacks". United States. doi:. https://www.osti.gov/servlets/purl/1373116.
@article{osti_1373116,
title = {A Mechanistic Reliability Assessment of RVACS and Metal Fuel Inherent Reactivity Feedbacks},
author = {Grabaskas, David and Brunett, Acacia J. and Passerini, Stefano and Grelle, Austin},
abstractNote = {GE Hitachi Nuclear Energy (GEH) and Argonne National Laboratory (Argonne) participated in a two year collaboration to modernize and update the probabilistic risk assessment (PRA) for the PRISM sodium fast reactor. At a high level, the primary outcome of the project was the development of a next-generation PRA that is intended to enable risk-informed prioritization of safety- and reliability-focused research and development. A central Argonne task during this project was a reliability assessment of passive safety systems, which included the Reactor Vessel Auxiliary Cooling System (RVACS) and the inherent reactivity feedbacks of the metal fuel core. Both systems were examined utilizing a methodology derived from the Reliability Method for Passive Safety Functions (RMPS), with an emphasis on developing success criteria based on mechanistic system modeling while also maintaining consistency with the Fuel Damage Categories (FDCs) of the mechanistic source term assessment. This paper provides an overview of the reliability analyses of both systems, including highlights of the FMEAs, the construction of best-estimate models, uncertain parameter screening and propagation, and the quantification of system failure probability. In particular, special focus is given to the methodologies to perform the analysis of uncertainty propagation and the determination of the likelihood of violating FDC limits. Additionally, important lessons learned are also reviewed, such as optimal sampling methodologies for the discovery of low likelihood failure events and strategies for the combined treatment of aleatory and epistemic uncertainties.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Sun Sep 24 00:00:00 EDT 2017},
month = {Sun Sep 24 00:00:00 EDT 2017}
}

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