PSAM 16 - Session T02 Overview

PSAM 16 Conference Session T02 Overview

Session Chair: Anders Olsson (anders.olsson@vysusgroup.com)

Paper 1 ER221

Lead Author: Erik Sparre Co-author(s): Carl Eriksson, carl.eriksson@riskpilot.se Mattias Håkansson, mattias.hakansson@riskpilot.se Jacob Larsson, jacob.larsson@riskpilot.se Gunnar Johanson, gunnar.johanson@riskpilot.se

Project DIOR (Deeper Investigation of Repairability of Failures)
The overall purpose of the DIOR project is to better understand failure data used in PRA models. A deeper knowledge about repairability, timing of failures, which failures (causes, coupling mechanisms) occur early/late etcetera gives the analyst valuable insights when developing state-of-the-art PSA models. Such knowledge can be relevant when introducing repair into PRA models, how to assign failure data for longer time windows, definition of CCF (concerning for example failure modes and repair possibilities). Information about single failures in Nordic Nuclear Power Plants is gathered in the so-called TUD-database. This database has primarily been used to calculate failure probabilities and failure rates for components, but since the database also contains information about repair times, and other timing information, it is possible to calculate measures such as mean time to repair (MTTR). Information about CCFs is collected within the ICDE-project and compiled in databases. Therefore, events from the TUD and ICDE databases have been analyzed for a selected number of components (centrifugal pumps, diesel generators and batteries). Data has been evaluated regarding severity of failures, repair times, waiting times etcetera. In the presentation at the PSAM 16, results and conclusions from the DIOR project will be presented.

Project DIOR (Deeper Investigation of Repairability of Failures)

The overall purpose of the DIOR project is to better understand failure data used in PRA models. A deeper knowledge about repairability, timing of failures, which failures (causes, coupling mechanisms) occur early/late etcetera gives the analyst valuable insights when developing state-of-the-art PSA models. Such knowledge can be relevant when introducing repair into PRA models, how to assign failure data for longer time windows, definition of CCF (concerning for example failure modes and repair possibilities). Information about single failures in Nordic Nuclear Power Plants is gathered in the so-called TUD-database. This database has primarily been used to calculate failure probabilities and failure rates for components, but since the database also contains information about repair times, and other timing information, it is possible to calculate measures such as mean time to repair (MTTR). Information about CCFs is collected within the ICDE-project and compiled in databases. Therefore, events from the TUD and ICDE databases have been analyzed for a selected number of components (centrifugal pumps, diesel generators and batteries). Data has been evaluated regarding severity of failures, repair times, waiting times etcetera. In the presentation at the PSAM 16, results and conclusions from the DIOR project will be presented.

Paper ER221 | Download the paper file. | Download the presentation PowerPoint file.

A PSAM Profile is not yet available for this author.

Paper 2 HA48

Lead Author: Hayat Chatri Co-author(s): Gunnar Johanson; gunnar.johanson@afconsult.com Jan Stiller; Jan.Stiller@grs.de Jeffery Wood; Jeffery.Wood@nrc.gov

Recent Insights from the International Common Cause Failure Data Exchange (ICDE) Project
Abstract: CCF events can significantly impact the availability of safety systems of nuclear power plants. For this reason, the ICDE Project was initiated by several countries in 1994. Since 1997 it has been operated within the OECD NEA framework and the project has successfully operated over seven consecutive terms (the current term being 2019-2022). The ICDE Project allows multiple countries to collaborate and exchange common-cause failure (CCF) data to enhance the quality of risk analyses, which include CCF modelling. Because CCF events are typically rare, most countries do not experience enough CCF events to perform meaningful analyses. Information combined from several countries, however, have yielded sufficient data for more rigorous analyses. The ICDE project has meanwhile published eleven reports on collection and analysis of CCF events of specific component types (centrifugal pumps, emergency diesel generators, motor operated valves, safety and relief valves, check valves, circuit breakers, level measurement, control rod drive assemblies, heat exchangers) and five topical reports on a number of different topics including intersystem common cause failure events while three additional topical reports are under preparation. The ICDE project has changed the view of CCFs a great deal. Many insights would not have been possible to identify without a deep plant data collection and combining information from many sources. For instance, determination of the fact that the most common cause of complete CCFs seems to be human action as a part of operation or design, rather than manufacturing deficiencies, would not have been possible without deep plant data collection and combining of information from many sources. This paper presents recent activities and lessons learnt from the data collection and the results of topical analyses on Pre-initiator human failure events (HFEs). In addition, the objectives and scopes of the ongoing analyses are presented. References [1.] ICDE General Coding Guidelines [NEA/CSNI/R(2004)4], January 2004. [2.] Collection and analysis of common-cause failure of centrifugal pumps [NEA/CSNI/R(99)2], September 1999. Replaced with [NEA/CSNI/R(2013)2], [3.] Collection and analysis of common-cause failure of emergency diesel generators [NEA/CSNI/R(2000)20], May 2000. Replaced with [NEA/CSNI/R(2018)5], [4.] Collection and analysis of common-cause failure of motor-operated valves [NEA/CSNI/R(2001)10], February 2001. Replaced with NEA/CSNI/R(20YY)XX ICDE project: Common-Cause Failures of Motor Operated Valves [5.] Collection and analysis of common-cause failure of safety valves and relief valves [NEA/CSNI/R(2002)19]. Published October 2002. Replaced with NEA/CSNI/R(2020)17 ICDE project: Common-Cause Failures of Safety and Relief Valves [6.] Collection and analysis of common-cause failure of check valves [NEA/CSNI/R(2003)15], February 2003. [7.] Collection and analysis of common-cause failure of batteries [NEA/CSNI/R(2003)19], September 2003. [8.] Proceedings of ICDE Workshop on the qualitative and quantitative use of ICDE Data [NEA/CSNI/R(2001)8], November 2002. [9.] Collection and analysis of common-cause failure of switching devices and circuit breakers [NEA/CSNI/R(2008)01], October 2007. [10.] Collection and analysis of common-cause failure of level measurement components [NEA/CSNI/R(2008)8], July 2008. [11.] Collection and analysis of common-cause failure of centrifugal pumps [NEA/CSNI/R(2013)2], June 2013. [12.] Collection and analysis of common-cause failure of control rod drive assemblies [NEA/CSNI/R(2013)4], June 2013. [13.] Collection and analysis of common-cause failure of heat exchangers [NEA/CSNI/R(2015)11], August 2015. [14.] ICDE Workshop - Collection and Analysis of Common-Cause Failures due to External Factors [NEA/CSNI/R(2015)17], October 2015. Replaced with [NEA/CSNI/R(2020)19 Final Manuscript ICDE Topical Report: Operational Experience on Common-Cause Failures due to External Environmental Factors [15.] ICDE Workshop - Collection and Analysis of Emergency Diesel Generator Common-Cause Failures Impacting Entire Exposed Population, 2015. NEA/CSNI/R(2017)8, August 2017. [16.] Lessons Learnt from Common-Cause Failure of Emergency Diesel Generators in Nuclear Power Plants – A Report from the International Common-Cause Failure Data Exchange (ICDE) Project [NEA/CSNI/R(2018)5], September 2018. [17.] ICDE Project Report: Summary of Phase VII of the International Common-Cause Data Exchange Project NEA/CSNI/R(2019)3, June 2019. [18.] ICDE Topical report: Collection and Analysis of Common-Cause Failures due to Plant Modifications NEA/CSNI/R(2019)4 ; 2019. [19.] ICDE Topical report: Provision against Common-Cause Failures by Improving Testing NEA/CSNI/R(2019)5; 2019. [20.] ICDE Topical report: Collection and Analysis of Multi-Unit Common-Cause Failure Events NEA/CSNI/R(2019)6; 2019. [21.] NEA/CSNI/R(2020)1 Final Manuscript ICDE Topical report. ICDE Topical report: Collection and Analysis of Intersystem Common Cause Failure Events. [22.] Draft. ICDE Topical report: Collection and Analysis of Common Cause Pre Initiator Human Failure Events.

Recent Insights from the International Common Cause Failure Data Exchange (ICDE) Project

Abstract: CCF events can significantly impact the availability of safety systems of nuclear power plants. For this reason, the ICDE Project was initiated by several countries in 1994. Since 1997 it has been operated within the OECD NEA framework and the project has successfully operated over seven consecutive terms (the current term being 2019-2022). The ICDE Project allows multiple countries to collaborate and exchange common-cause failure (CCF) data to enhance the quality of risk analyses, which include CCF modelling. Because CCF events are typically rare, most countries do not experience enough CCF events to perform meaningful analyses. Information combined from several countries, however, have yielded sufficient data for more rigorous analyses. The ICDE project has meanwhile published eleven reports on collection and analysis of CCF events of specific component types (centrifugal pumps, emergency diesel generators, motor operated valves, safety and relief valves, check valves, circuit breakers, level measurement, control rod drive assemblies, heat exchangers) and five topical reports on a number of different topics including intersystem common cause failure events while three additional topical reports are under preparation. The ICDE project has changed the view of CCFs a great deal. Many insights would not have been possible to identify without a deep plant data collection and combining information from many sources. For instance, determination of the fact that the most common cause of complete CCFs seems to be human action as a part of operation or design, rather than manufacturing deficiencies, would not have been possible without deep plant data collection and combining of information from many sources. This paper presents recent activities and lessons learnt from the data collection and the results of topical analyses on Pre-initiator human failure events (HFEs). In addition, the objectives and scopes of the ongoing analyses are presented. References [1.] ICDE General Coding Guidelines [NEA/CSNI/R(2004)4], January 2004. [2.] Collection and analysis of common-cause failure of centrifugal pumps [NEA/CSNI/R(99)2], September 1999. Replaced with [NEA/CSNI/R(2013)2], [3.] Collection and analysis of common-cause failure of emergency diesel generators [NEA/CSNI/R(2000)20], May 2000. Replaced with [NEA/CSNI/R(2018)5], [4.] Collection and analysis of common-cause failure of motor-operated valves [NEA/CSNI/R(2001)10], February 2001. Replaced with NEA/CSNI/R(20YY)XX ICDE project: Common-Cause Failures of Motor Operated Valves [5.] Collection and analysis of common-cause failure of safety valves and relief valves [NEA/CSNI/R(2002)19]. Published October 2002. Replaced with NEA/CSNI/R(2020)17 ICDE project: Common-Cause Failures of Safety and Relief Valves [6.] Collection and analysis of common-cause failure of check valves [NEA/CSNI/R(2003)15], February 2003. [7.] Collection and analysis of common-cause failure of batteries [NEA/CSNI/R(2003)19], September 2003. [8.] Proceedings of ICDE Workshop on the qualitative and quantitative use of ICDE Data [NEA/CSNI/R(2001)8], November 2002. [9.] Collection and analysis of common-cause failure of switching devices and circuit breakers [NEA/CSNI/R(2008)01], October 2007. [10.] Collection and analysis of common-cause failure of level measurement components [NEA/CSNI/R(2008)8], July 2008. [11.] Collection and analysis of common-cause failure of centrifugal pumps [NEA/CSNI/R(2013)2], June 2013. [12.] Collection and analysis of common-cause failure of control rod drive assemblies [NEA/CSNI/R(2013)4], June 2013. [13.] Collection and analysis of common-cause failure of heat exchangers [NEA/CSNI/R(2015)11], August 2015. [14.] ICDE Workshop - Collection and Analysis of Common-Cause Failures due to External Factors [NEA/CSNI/R(2015)17], October 2015. Replaced with [NEA/CSNI/R(2020)19 Final Manuscript ICDE Topical Report: Operational Experience on Common-Cause Failures due to External Environmental Factors [15.] ICDE Workshop - Collection and Analysis of Emergency Diesel Generator Common-Cause Failures Impacting Entire Exposed Population, 2015. NEA/CSNI/R(2017)8, August 2017. [16.] Lessons Learnt from Common-Cause Failure of Emergency Diesel Generators in Nuclear Power Plants – A Report from the International Common-Cause Failure Data Exchange (ICDE) Project [NEA/CSNI/R(2018)5], September 2018. [17.] ICDE Project Report: Summary of Phase VII of the International Common-Cause Data Exchange Project NEA/CSNI/R(2019)3, June 2019. [18.] ICDE Topical report: Collection and Analysis of Common-Cause Failures due to Plant Modifications NEA/CSNI/R(2019)4 ; 2019. [19.] ICDE Topical report: Provision against Common-Cause Failures by Improving Testing NEA/CSNI/R(2019)5; 2019. [20.] ICDE Topical report: Collection and Analysis of Multi-Unit Common-Cause Failure Events NEA/CSNI/R(2019)6; 2019. [21.] NEA/CSNI/R(2020)1 Final Manuscript ICDE Topical report. ICDE Topical report: Collection and Analysis of Intersystem Common Cause Failure Events. [22.] Draft. ICDE Topical report: Collection and Analysis of Common Cause Pre Initiator Human Failure Events.

Paper HA48 | Download the paper file. | Download the presentation pdf file.

Name: Hayat Chatri (hayat.chatri@cnsc-ccsn.gc.ca)

Bio: Hayat Chatri holds a M.Sc. in Nuclear physics from Montreal University, Canada. Hayat is a technical specialist at the Probabilistic Safety Assessment (PSA) and Reliability Division at the Canadian Nuclear Safety Commission (CNSC). Since joining the CNSC in 2008, she has been involved in several projects and activities related to the licensing and the regulatory reviews of reliability analyses and PSA of Nuclear Power Plants. She has also participated in several international activities related to PSA and she represented the CNSC in various international PSA fora (NEA, IAEA, USNRC etc). Prior to joining the CNSC, she worked for Atomic Energy Canada Limited (AECL) as PSA specialist. She began her career at a consulting firm of Hydro-Québec, where she was involved in a variety of reliability and nuclear safety analyses projects.

Country: CAN
Company: Canadian Nuclear Safety Commission
Job Title: Technical Specialist

Paper 3 MA19

Lead Author: Marco Arndt Co-author(s): Philipp Mell, M.Sc.; E-mail: philipp.mell@ima.uni-stuttgart.de Dr.-Ing. Martin Dazer; E-mail: martin.dazer@ima.uni-stuttgart.de Prof. Dr.-Ing. Bernd Bertsche; E-mail: bernd.bertsche@ima.uni-stuttgart.de

Generic effects of deviations from test design orthogonality on test power and regression modelling of Central-Composite Designs
In the context of design of experiments (DoE), for many cases the quantitative dependency of a nonlinear target parameter on a few factors is to be determined for the related parameter prediction. For these cases, from the group of response surface designs, test plans are used following the structure of Central-Composite Design (CCD). Based on full-factorial test plans, they feature additional test runs in the center of the design space (center run) as well as along the main axes (star run), which yield the required information for a quadratic model while still being highly efficient. The leverage value α predefines the relative directional distance beyond the center run for the star runs. The individual value determination of α as well as the specific arrangement of the test runs in the design matrix follow a generic mathematical approach to match required DoE properties. Here the most essential respective property is orthogonality. It is sufficiently required in order to consider uncorrelated and independent coefficients separately and to establish regression models, guaranteeing the narrowest possible confidence intervals for parameter prediction. It can be complied and determined analytically based on α and the relative amount of individual run types. However, in the current state of research it remains unclear to what extent renewed adjustments in the amount and arrangement of test runs or further deviations from orthogonality have a practicable effect on design efficiency, test power and precision in regression coefficient estimation. This paper presents a parameter study regarding generic orthogonality deviations in CCDs. For this purpose, various orthogonality deviations are mathematically identified, quantified and performed. Subsequently, potentials and deviations in the effect detection are calculated. Finally, tendencies and first recommendations for design adaptations are presented under consideration of parameter prediction and design efficiency. This includes the categorical exclusion of possible orthogonality deviations as well as the quantification of tolerance limits for minor orthogonality deviations.

Generic effects of deviations from test design orthogonality on test power and regression modelling of Central-Composite Designs

In the context of design of experiments (DoE), for many cases the quantitative dependency of a nonlinear target parameter on a few factors is to be determined for the related parameter prediction. For these cases, from the group of response surface designs, test plans are used following the structure of Central-Composite Design (CCD). Based on full-factorial test plans, they feature additional test runs in the center of the design space (center run) as well as along the main axes (star run), which yield the required information for a quadratic model while still being highly efficient. The leverage value α predefines the relative directional distance beyond the center run for the star runs. The individual value determination of α as well as the specific arrangement of the test runs in the design matrix follow a generic mathematical approach to match required DoE properties. Here the most essential respective property is orthogonality. It is sufficiently required in order to consider uncorrelated and independent coefficients separately and to establish regression models, guaranteeing the narrowest possible confidence intervals for parameter prediction. It can be complied and determined analytically based on α and the relative amount of individual run types. However, in the current state of research it remains unclear to what extent renewed adjustments in the amount and arrangement of test runs or further deviations from orthogonality have a practicable effect on design efficiency, test power and precision in regression coefficient estimation. This paper presents a parameter study regarding generic orthogonality deviations in CCDs. For this purpose, various orthogonality deviations are mathematically identified, quantified and performed. Subsequently, potentials and deviations in the effect detection are calculated. Finally, tendencies and first recommendations for design adaptations are presented under consideration of parameter prediction and design efficiency. This includes the categorical exclusion of possible orthogonality deviations as well as the quantification of tolerance limits for minor orthogonality deviations.

Paper MA19 | Download the paper file. | Download the presentation pdf file. Download the presentation PowerPoint file.

Name: Marco Arndt (marco.arndt@ima.uni-stuttgart.de)

Bio: Marco Arndt studied Mechanical Engineering at the University of Stuttgart in Germany and received his M.Sc. in 2020. Since 2021, he has been a researcher in the Reliability Engineering Department at the Institute of Machine Components at the University of Stuttgart and pursues his PhD studies. In his research, he investigates experimental designs and testing strategies with a focus on efficiency improvement to develop highly efficient and adaptable experimental designs.

Country: DEU
Company: PhD candidate at the University of Stuttgart, Germany
Job Title: Research Assistant in Reliability Engineering

Paper 4 SJ53

Lead Author: Jan Stiller

Modelling and Quantification of Correlated Failures of Multiple Components due to Asymmetries of the Electrical Power Supply System of Nuclear Power Plants in PSA
Failures of multiple redundant trains of the electrical power supply system of nuclear power plants (NPPs) have recently gained increasing attention by the nuclear community. This was triggered by events at different NPPs where single causes led to such failures. For example, at the Byron NPP (USA), asymmetries in the power supply system arose from a single failure of an insulator in the switchyard of the plant. The asymmetry failed to cause the reactor protection system (RPS) to initiate the isolation of the emergency bus bars and the operation of the emergency diesel generators. At the Forsmark NPP (Sweden), an open phase condition which was not detected by the RPS was caused by the failure of one pole of a breaker. In both events, the electrical consumers remained connected with the fault and were exposed to an asymmetric voltage supply, leading to unavailability and destruction of safety related electrical equipment. Similar events occurred in other plants as well, e.g., at Vandellòs, Unit 2 (Spain) in 2006, at Dungeness, Unit B (United Kingdom) in 2007 and at Bruce, Unit A-1 (Canada) in 2012. To consider such events in PSA, the failures of electrical components due to asymmetries in the electrical power supply system have to be adequately modelled and quantified. A comprehensive analysis of international operating experience regarding asymmetries in the electrical power supply has shown that resulting component failures cannot be modelled as independent events but are significantly correlated. Similar components with comparable loads tend to fail simultaneously. This is very important to safety since redundant components are likely to be affected by such correlated failures. To grasp this effect, different modelling approaches have been considered. Estimation algorithms for the respective model parameters have been developed and applied utilizing the international operating experience. The paper will present the analysis of the operating experience, the modelling approaches and first quantification results.

Modelling and Quantification of Correlated Failures of Multiple Components due to Asymmetries of the Electrical Power Supply System of Nuclear Power Plants in PSA

Failures of multiple redundant trains of the electrical power supply system of nuclear power plants (NPPs) have recently gained increasing attention by the nuclear community. This was triggered by events at different NPPs where single causes led to such failures. For example, at the Byron NPP (USA), asymmetries in the power supply system arose from a single failure of an insulator in the switchyard of the plant. The asymmetry failed to cause the reactor protection system (RPS) to initiate the isolation of the emergency bus bars and the operation of the emergency diesel generators. At the Forsmark NPP (Sweden), an open phase condition which was not detected by the RPS was caused by the failure of one pole of a breaker. In both events, the electrical consumers remained connected with the fault and were exposed to an asymmetric voltage supply, leading to unavailability and destruction of safety related electrical equipment. Similar events occurred in other plants as well, e.g., at Vandellòs, Unit 2 (Spain) in 2006, at Dungeness, Unit B (United Kingdom) in 2007 and at Bruce, Unit A-1 (Canada) in 2012. To consider such events in PSA, the failures of electrical components due to asymmetries in the electrical power supply system have to be adequately modelled and quantified. A comprehensive analysis of international operating experience regarding asymmetries in the electrical power supply has shown that resulting component failures cannot be modelled as independent events but are significantly correlated. Similar components with comparable loads tend to fail simultaneously. This is very important to safety since redundant components are likely to be affected by such correlated failures. To grasp this effect, different modelling approaches have been considered. Estimation algorithms for the respective model parameters have been developed and applied utilizing the international operating experience. The paper will present the analysis of the operating experience, the modelling approaches and first quantification results.

Paper SJ53 | Download the paper file. | Download the presentation PowerPoint file.

Name: Jan Stiller (sjc@grs.de)

Bio: Jan Stiller studied Physics and has a PhD in Theoretical physics. After different postoc positions with a focus an Bayesian statistical methods and machine learning he joined GRS in 2003, where he currently is the chief expert equipment reliability. Main topics of his work are common-cause failure modelling and quantification, quantification of the reliability of software based digital I&C, organizational factors and (safety) management.

Country: DEU
Company: Gesellschaft für Anlagen- und Reaktorsicherheit (GRS) gGmbH
Job Title: Chief expert equipment reliability