Skip to content
Licensed Unlicensed Requires Authentication Published by De Gruyter March 30, 2021

Dynamic fault tree analysis of auxiliary feedwater system in a pressurized water reactor

Dynamische Fehlerbaumanalyse des Hilfsspeisewassersystems eines Druckwasserreaktors
R. A. Fahmy and R. I. Gomaa
From the journal Kerntechnik

Abstract

The safe and secure designs of any nuclear power plant together with its cost-effective operation without accidents are leading the future of nuclear energy. As a result, the Reliability, Availability, Maintainability, and Safety analysis of NPP systems is the main concern for the nuclear industry. But the ability to assure that the safety-related system, structure, and components could meet the safety functions in different events to prevent the reactor core damage requires new reliability analysis methods and techniques. The Fault Tree Analysis (FTA) is one of the most widely used logic and probabilistic techniques in system reliability assessment nowadays. The Dynamic fault tree technique extends the conventional static fault tree (SFT) by considering the time requirements to model and evaluate the nuclear power plant safety systems. Thus this paper focuses on developing a new Dynamic Fault Tree for the Auxiliary Feed-water System (AFWS) in a pressurized water reactor. The proposed dynamic model achieves a more realistic and accurate representation of the AFWS safety analysis by illustrating the complex failure mechanisms including interrelated dependencies and Common Cause Failure (CCF). A Simulation tool is used to simulate the proposed dynamic fault tree model of the AFWS for the quantitative analysis. The more realistic results are useful to establish reliability cantered maintenance program in which the maintenance requirements are determined based on the achievement of system reliability goals in the most cost-effective manner.

Abstract

Die sichere Auslegung eines jeden Kernkraftwerks (KKW) zusammen mit seinem kostengünstigen Betrieb ohne Unfälle sind wegweisend für die Zukunft der Kernenergie. Daher ist die Analyse der Zuverlässigkeit, Verfügbarkeit, Instandhaltbarkeit und Sicherheit von Kernkraftwerken-Systemen das Hauptanliegen der Nuklearindustrie. Aber die Fähigkeit zu gewährleisten, dass das sicherheitsrelevante System, die Struktur und die Komponenten die Sicherheitsfunktionen bei verschiedenen Ereignissen erfüllen können, um die Beschädigung des Reaktorkerns zu verhindern, erfordert neue Methoden und Techniken der Zuverlässigkeitsanalyse. Die Fehlerbaumanalyse ist heutzutage eine der am meisten verwendeten logischen und probabilistischen Techniken in der Bewertung der Zuverlässigkeit von Systemen. Die dynamische Fehlerbaumtechnik erweitert den konventionellen statischen Fehlerbaum, indem sie die zeitlichen Anforderungen an die Modellierung und Bewertung der KKW-Sicherheitssysteme berücksichtigt. Diese Arbeit konzentriert sich auf die Entwicklung eines neuen dynamischen Fehlerbaums für das Auxiliary Feed-Water System (AFWS) in einem Druckwasserreaktor. Das vorgeschlagene dynamische Modell erreicht eine realistischere und genauere Darstellung der AFWSSicherheitsanalyse, indem es die komplexen Fehlermechanismen einschließlich der wechselseitigen Abhängigkeiten und Common Cause Failure abbildet. Ein Simulationswerkzeug wird verwendet, um das vorgeschlagene dynamische Fehlerbaumtechnik-Modell des AFWS für die quantitative Analyse zu simulieren. Die realistischeren Ergebnisse sind nützlich, um ein zuverlässigkeitsorientiertes Instandhaltungsprogramm zu erstellen, in dem die Instandhaltungsanforderungen auf der Grundlage der Erreichung der Systemzuverlässigkeitsziele auf eine sehr kosteneffektive Weise bestimmt werden.

Acknowledgements

The authors would like to express their gratitude for the advice and support given by Prof. Ferdinando Chiacchio of DIEEI, Department of Electrical, Electronic and Computer Engineering, University of Catania, Italy.

Nomenclature

AFWS

auxiliary feed-water system

CCF

common cause failure

CST

condensate storage tank

DFT

dynamic fault tree

DPRA

dynamic probabilistic risk assessment

ESFAS

engineered safety features actuation system

ET

Event Trees

FDEP

functional dependency gate

FMEA

Failure Mode Effect Analysis

FR

fails to run

FS

fails to start

FTA

fault tree analysis

HFA

Human Factors Analysis

LOCA

loss-of-coolant accident

LOFW

Loss of Main Feedwater

MDP

motor-driven pump

MFWS

Main Feedwater System

NPP

nuclear power plant

PAND

priority AND gate

PSA

Probabilistic Safety Assessments

RAMS

reliability, availability, maintainability and safety

SBO

station block out

SEQ

sequence enforcing gate

SFT

static fault tree

SHyFTA

Stochastic Hybrid Fault Tree Automaton

SPARE

spare gate

TDP

steam turbine-driven pump

References

1 Bernhoft, S.: Program on Technology Innovation: Approach to Transition Nuclear Power Plants to Flexible Power Operations. Electric Power Research Institute, 2014Search in Google Scholar

2 WNA, Renewable Energy and Electricity. World Nuclear Assoication, September 2016, http://www.world-nuclear.org/information-library/energy-and-the-environment/renewable-energy-and-electricity.aspxSearch in Google Scholar

3 OECD/NEA, Fukushima Dai-Ichi Nuclear Power Station Accident: Summary of NEA and Member Response. June, 2013Search in Google Scholar

4 Andreev, L.: The Economics of the Russian Nuclear Power Industry. BELLONA Foundation, Norway, 2011Search in Google Scholar

5 Shi, L.; Enzinna, R.; Yang, S.; Blodgett, S.: Probabilistic Risk Assessments of Digital I&C in Nuclear Power Plant. Proc. 10th International Probabilistic Safety Assessment & Management Conference, PSAM 10, Seattle, Washington, June 7–11, 2010, paper 173Search in Google Scholar

6 IAEA: Development and application of Level-1 probabilistic Safety Assessment for nuclear power plants. IAEA SSG-3. International Atomic Energy Agency, Vienna, 2010Search in Google Scholar

7 Chu, T.-L.; Yue, M.; Martinez-Guridi, G.; Lehner, J.: AGeneric Failure Modes and Effects Analysis (FMEA) Approach for Reliability Modeling of Digital Instrumentation and Control (I&C) Systems. Proc. 10th InternationalProbabilistic Safety Assessment & Management Conference, PSAM 10, Seattle, Washington, June 7–11, paper 82, 2010Search in Google Scholar

8 OECD/NEA: Recommendations on assessing digital system reliability in probabilistic risk assessments of nuclear power plants. NEA/CSNI/R(2009)18, OECD/NEA/CSNI, Paris, 2009Search in Google Scholar

9 IAEA: Living Probabilistic Safety Assessment (LPSA), in: IAEA (Ed.) IAEA-TECDOC-1106, IAEA, Vienna, 1999Search in Google Scholar

10 Hsu, F.; Musicki, Z.: Issues and Insights of Probabilistic Risk Assessment Methodology in Nuclear and Space Applications, In: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (2005)Search in Google Scholar

11 Labeau, P. E.; Smidts, C.; Swaminathan, S.: Dynamic reliability: towards an integrated platform for probabilistic risk assessment. Reliability Engineering and System Safety 68 (2000) 219–254, DOI:10.1016/S0951-8320(00)00017-X10.1016/S0951-8320(00)00017-XSearch in Google Scholar

12 Chiacchio, F.; Aizpurua, J. I.; Compagno, L.; D’Urso, D.: SHyFTOO, an object-oriented Monte Carlo simulation library for the modeling of Stochastic Hybrid Fault Tree Automaton. Expert Systems with Applications 146 (2020) 113139, DOI:10.1016/j.eswa.2019.11313910.1016/j.eswa.2019.113139Search in Google Scholar

13 Xu, H., Dugan, J. B., Meshkat, L.: A: dynamic fault tree model of a propulsion system. Proceedings of the 8th International Conference on Probabilistic Safety Assessment and Management, 2006, http:// hdl.handle.net/2014/39337Search in Google Scholar

14 Durga Rao, K.; Gopika, V.; Sanyasi Rao, V. V. S.; Kushwaha, H. S.; Verma, A. K.; Srividya, A.: Dynamic fault tree analysis using Monte Carlo simulation in probabilistic safety assessment. Reliability Engineering and System Safety94 (2009) 872–883, DOI:10.1016/j.ress.2008.09.00710.1016/j.ress.2008.09.007Search in Google Scholar

15 Ge, D.; Chen, S.; Wang, Z.; Chen, Z.: Dynamic Fault Tree Analysis for NPP Emergency Diesel Generator System. Transactions of the American Nuclear Society 118 (2018), Philadelphia, Pennsylvania, June 17–21, 2018Search in Google Scholar

16 Dugan, J. B.: Galileo: A Tool for Dynamic Fault Tree Analysis. In: Haverkort, B.R.; Bohnenkamp, H.C.; Smith, C.U.: Computer Performance Evaluation Modeling Techniques and Tools, Lecture Notes in Computer Science, vol 1786. Springer, Berlin, 2000, DOI:10.1007/3-540-46429-8_2410.1007/3-540-46429-8_24Search in Google Scholar

17 Chiacchio, F.; Compagno, L.; D’Urso, D.; MannoG.; Trapani, N.: An open-source application to model and solve dynamic fault tree of real industrial systems. 5th International Conference on Software, Knowledge Information, Industrial Management and Applications (SKIMA) Proceedings, Benevento, 2011, pp. 1–8, DOI:10.1109/SKIMA.2011.617452110.1109/SKIMA.2011.6174521Search in Google Scholar

18 Manno, G.; Chiacchio, F.; D’Urso, D.; Trapani, N.; Compagno, L.: RAATSS, an extensible Matlab® toolbox for the evaluation of repairable dynamic fault trees.Proceedings PSAM’11 &ESREL 2012, Jun 2012, HelsinkiSearch in Google Scholar

19 Chiacchio, F.; D’Urso, D.; Compagno, L.; Pennisi, M.; Pappalardo, F.; Manno, G.: SHyFTA a Stochastic Hybrid Fault Tree Automaton for the modeling and simulation of dynamic reliability problems. Expert Systems with Applications 47 (2016) 42–57, DOI:10.1016/j.eswa.2015.10.04610.1016/j.eswa.2015.10.046Search in Google Scholar

20 Durga Rao, K.; Sanyasi Rao, V. V. S.; Verma, A. K.; Srividya, A.: Dynamic Fault Tree Analysis: Simulation Approach.In: Simulation Methodsfor Reliability and Availability of Complex Systems, Springer, 2010, Chapter 2Search in Google Scholar

21 Liu, Y.; Wu Y.; Kalbarczyk, Z.: Smart Maintenance via Dynamic Fault Tree Analysis: ACase Study on Singapore MRT System, 2017 47th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN), Denver, CO, 2017, pp. 511–51810.1109/DSN.2017.50Search in Google Scholar

22 Poloski, J. P.; Grant,G. M.; Gentillon, C. D.; Galyean,W. A.; Knudsen, J. X.: Reliability Study: Auxiliary/Emergency Feedwater System, 1987–1995. NUREG/CR-5500, Vol. 1, U.S. Nuclear Regulatory Commission, 1998Search in Google Scholar

23 Mosleh, A.; et al.: Procedures Guidelines in Modeling Common Cause Failures in Probabilistic Risk Assessment (NUREG/CR-5485)" 1998.Search in Google Scholar

Received: 2020-09-17
Published Online: 2021-03-30
Published in Print: 2021-04-30

© 2021 Walter de Gruyter GmbH, Berlin/Boston

Scroll Up Arrow