TY - GEN
T1 - An algorithm for risk-informed analysis of advanced nuclear reactors with a case study of pipe failure rate estimation
AU - Beal, John
AU - Sakurahara, Tatsuya
AU - Reihani, Seyed
AU - Kee, Ernie
AU - Mohaghegh, Zahra
N1 - Publisher Copyright:
©ESREL2020-PSAM15 Organizers.Published by Research Publishing, Singapore.
PY - 2020
Y1 - 2020
N2 - For advanced nuclear reactors, Probabilistic Risk/Safety Assessment (PRA/PSA) is one of the primary inputs to the risk-informed decision-making by the regulatory agency and the nuclear industry. The risk-informed analysis for advanced reactors is, however, challenging since (i) design-specific operating experience is quite limited or even unavailable and (ii) for the systems and phenomena that do not exist in the operating reactors, no consensus model that has been validated or peer reviewed and widely adopted by the community is available. To address these challenges, the authors' group initiated a line of research to enhance PRA theories and methodologies for risk-informed analysis of advanced reactors. In their previous work, an Integrated Probabilistic Physics-of-Failure (I-PPoF) methodological framework was developed, where explicit models of physical degradation and maintenance performance were coupled using a renewal process model. I-PPoF helped address the first challenge (i.e., the lack of operating experience) by quantifying the PRA model based on modeling of the underlying phenomena associated with PRA inputs, instead of using a solely data-driven approach (as commonly done in PRA for operating reactors). This paper develops an algorithm to operationalize the I-PPoF methodological framework equipped with Probabilistic Validation (PV) to address the second challenge (i.e., validation) associated with risk-informed analysis of advanced reactors. This algorithm helps evaluate whether and how the structure and quantification of existing models, such as consensus models from conventional reactors and up-todate models in academic literature, need to be updated for advanced reactors. The algorithm uses epistemic uncertainty as a measure of credibility, while sensitivity analyses are included to identify the most influential contributors to the output uncertainty, helping gradual and efficient improvements of realism and relevancy of the models that are needed for the I-PPoF methodological framework. Although the proposed algorithm is applicable for risk-informed analysis of diverse advanced reactors, this paper demonstrates its applicability to a case study of pipe failure rate estimation for advanced water-cooled reactor.
AB - For advanced nuclear reactors, Probabilistic Risk/Safety Assessment (PRA/PSA) is one of the primary inputs to the risk-informed decision-making by the regulatory agency and the nuclear industry. The risk-informed analysis for advanced reactors is, however, challenging since (i) design-specific operating experience is quite limited or even unavailable and (ii) for the systems and phenomena that do not exist in the operating reactors, no consensus model that has been validated or peer reviewed and widely adopted by the community is available. To address these challenges, the authors' group initiated a line of research to enhance PRA theories and methodologies for risk-informed analysis of advanced reactors. In their previous work, an Integrated Probabilistic Physics-of-Failure (I-PPoF) methodological framework was developed, where explicit models of physical degradation and maintenance performance were coupled using a renewal process model. I-PPoF helped address the first challenge (i.e., the lack of operating experience) by quantifying the PRA model based on modeling of the underlying phenomena associated with PRA inputs, instead of using a solely data-driven approach (as commonly done in PRA for operating reactors). This paper develops an algorithm to operationalize the I-PPoF methodological framework equipped with Probabilistic Validation (PV) to address the second challenge (i.e., validation) associated with risk-informed analysis of advanced reactors. This algorithm helps evaluate whether and how the structure and quantification of existing models, such as consensus models from conventional reactors and up-todate models in academic literature, need to be updated for advanced reactors. The algorithm uses epistemic uncertainty as a measure of credibility, while sensitivity analyses are included to identify the most influential contributors to the output uncertainty, helping gradual and efficient improvements of realism and relevancy of the models that are needed for the I-PPoF methodological framework. Although the proposed algorithm is applicable for risk-informed analysis of diverse advanced reactors, this paper demonstrates its applicability to a case study of pipe failure rate estimation for advanced water-cooled reactor.
KW - Advanced nuclear reactors
KW - Importance measure
KW - Maintenance
KW - Physical degradation
KW - Pipe failure rate
KW - Probabilistic risk/safety assessment
KW - Renewal process
KW - Risk-informed analysis
KW - Sensitivity analysis
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M3 - Conference contribution
AN - SCOPUS:85110334622
T3 - 30th European Safety and Reliability Conference, ESREL 2020 and 15th Probabilistic Safety Assessment and Management Conference, PSAM 2020
SP - 4170
EP - 4177
BT - 30th European Safety and Reliability Conference, ESREL 2020 and 15th Probabilistic Safety Assessment and Management Conference, PSAM 2020
A2 - Baraldi, Piero
A2 - Di Maio, Francesco
A2 - Zio, Enrico
PB - Research Publishing Services
T2 - 30th European Safety and Reliability Conference, ESREL 2020 and 15th Probabilistic Safety Assessment and Management Conference, PSAM 2020
Y2 - 1 November 2020 through 5 November 2020
ER -