Event Sequence Based Fault Tree Analysis to Evaluate Minimal Combination of Event Sequences Leading to the Reactor Core Damage

Abstract

Probabilistic safety assessment has been widely used to evaluate nuclear power plant safety systems. It couples fault tree (FT) analysis and event tree (ET) analysis. The FT analysis is to develop failure scenarios, to quantify the failure probability, and to identify critical components. The ET analysis is to develop event sequences (ESs) leading to reactor core damage and quantify core damage frequency. Currently, there is no approach in probabilistic safety assessment that can be used to identify minimal combinations of safety system failures leading to reactor core damage. This study proposes an event sequence based fault tree analysis, which integrates the FT model with the ET model. The ET model is to identify the ESs leading to the reactor core damage. Meanwhile, the FT model is to identify minimal combinations of those ESs found in the ET model. The motivation of this study is how to identify critical safety systems in nuclear power plants to mitigate disturbances caused by a group of postulated initiating events to avoid core damage. To confirm the feasibility and the applicability of the proposed approach, the performance of the AP1000 passive core cooling system is evaluated. It is found that if an in-containment refueling water storage tank, an automatic depressurization system – full, an accumulator, a core make-up tank, and a passive residual heat removal work properly, the reactor core will remain intact. These results confirmed that the proposed approach could be applicable to identify minimal combinations of safety systems to keep the reactor core intact.

Keywords

• Fault tree analysis
• Event tree analysis
• Event sequence
• AP1000
• Core cooling system

References

1. [1] Contini, S., Fabbri, L., Matuzas, V., “A novel method to apply importance and sensitivity analysis to multiple fault-trees”, Journal of Loss Prevention in the Process Industries, 23: 574–584, (2010).
2. [2] Khakzad, N., Khan, F., Amyotte, P., “Risk-based design of process systems using discrete-time Bayesian networks”, Reliability Engineering & System Safety, 109: 5–17, (2013).
3. [3] Purba J. H., Tjahyani, D. T. S., Susila, I. P., Widodo, S., Ekariansyah, A. S., “Fuzzy probability and α-cut based-fault tree analysis approach to evaluate the reliability and safety of complex engineering systems”, Quality and Reliability Engineering International, 38: 2356–2371, (2022).
4. [4] Kabir, S., “An overview of fault tree analysis and its application in model based dependability analysis”, Expert Systems with Applications, 77: 114–135, (2017).
5. [5] Walker, M., Papadopoulos, Y., “Qualitative temporal analysis: Towards a full implementation of the Fault Tree Handbook”, Control Engineering Practice, 17: 1115–1125, (2009).
6. [6] Li, S., Lou, J., Zong, X., Ma, S.,“Application of Fault Tree Analysis to the DCS Reliability of Nuclear Power Plants”, IMCEC 2022 - IEEE 5th Advanced Information Management, Communicates, Electronic and Automation Control Conference, 1863–1868, (2022).
7. [7] Purba J. H., Tjahyani D. T. S., Widodo S, Ekariansyah A.S., “Fuzzy probability based event tree analysis for calculating core damage frequency in nuclear power plant probabilistic safety assessment”, Progress in Nuclear Energy, 125: 103376, (2020).
8. [8] Li, J., “Fault-Event Trees Based Probabilistic Safety Analysis of a Boiling Water Nuclear Reactor’s Core Meltdown and Minor Damage Frequencies”, Safety, 6, (2020).

9. [9] Jeong, K. S., Lee, K. W., Jeong, S. Y., Lim, H. K., “Estimation on probability of radiological hazards for nuclear facilities decommissioning based on fuzzy and event tree method”, Annals of Nuclear Energy, 38: 2606–2611, (2011).
10. [10] Purba, J. H., “A fuzzy probability algorithm for evaluating the AP1000 long term cooling system to mitigate large break LOCA”, Atom Indonesia, 41: 113–121, (2015).
11. [11] Queral, C., Montero-Mayorga, J.,“Risk reduction due to modification of normal residual heat removal system of AP1000 reactor to meet European Utility Requirements”, Annals of Nuclear Energy, 91: 65–78, (2016).
12. [12] Hung, Z. Y., Ferng, Y. M., Hsu, W. S., Pei, B. S., Chen, Y. S.,“Analysis of AP1000 containment passive cooling system during a loss-of-coolant accident”, Annals of Nuclear Energy, 85: 717–724, (2014).
13. [13] Ekariansyah, A. S., Widodo, S., “Performance analysis of AP1000 passive systems during direct vessel injection (DVI) line break”, Atom Indonesia, 42: 79–88, (2016).
14. [14] Quan, L.Y., Jian, C. H., Li, D. L., Yann, S., Shen, Y. Z., Kai, Y., Fang, F. F., “Analytical studies of long-term IRWST injection core cooling under small break LOCA in passive safety PWR”, Annals of Nuclear Energy, 88: 218–236, (2016).
15. [15] Li, X., Li, N., Wang, Z. Y., Fu, X., Lu, D., Yang, Y., “Phenomena identification and ranking table for passive residual heat removal system in IRWST”, Annals of Nuclear Energy, 94: 80–86, (2016).
16. [16] Hashim, M., Hidekazu, Y., Takeshi, M., Ming, Y.,“Application case study of AP1000 automatic depressurization system (ADS) for reliability evaluation by GO-FLOW methodology”, Nuclear Engineering and Design, 278: 209–221, (2014).
17. [17] Zou, J., Li, Q., Tong, L. L., Cao, X. W., “Assessment of passive residual heat removal system cooling capacity”, Progress in Nuclear Energy, 70: 159–166, (2014).
18. [18] Westinghouse, “UK AP1000 Probabilistic Risk Assessment UKP-GW-GL-022”, (2007).