A Petri net model-based resilience analysis of nuclear power plants under the threat of natural hazards
Due to global climate change, nuclear power plants are increasingly exposed to the threats of extreme natural disasters. In this paper, a resilience engineering approach is applied to tackle all aspects of nuclear safety, spanning from design, operation, and maintenance to accident response and recovery, in the case of high-impact low-probability events. Petri net models are developed to simulate the losses caused by extreme events, the health states of relevant systems, mitigation processes, and the recovery and maintenance processes. The method developed is applied to assess the resilience of a single-unit pressurised heavy water reactor under the threat of three possible external events. Possible loss of coolant accidents and station blackout accidents caused by the events are considered. With the aid of the models developed, both the influence of stochastic deterioration and the impact of external events on the resilience of the reactor can be assessed quantitatively. The simulation results show that the method can comprehensively describe the resilience of nuclear power plants against various disruptive events. It is also found that the stochastic deterioration that does not directly affect the operation of nuclear reactors is critical to the resilience of reactors.
Funding
A Resilience Modelling Framework for Improved Nuclear Safety (NuRes)
Engineering and Physical Sciences Research Council
Find out more...History
School
- Aeronautical, Automotive, Chemical and Materials Engineering
Department
- Aeronautical and Automotive Engineering
Published in
Reliability Engineering & System SafetyVolume
230Publisher
Elsevier BVVersion
- VoR (Version of Record)
Rights holder
© The AuthorsPublisher statement
This is an Open Access Article. It is published by Elsevier under the Creative Commons Attribution 4.0 International Licence (CC BY). Full details of this licence are available at: https://creativecommons.org/licenses/by/4.0/Acceptance date
2022-11-11Publication date
2022-11-13Copyright date
2022ISSN
0951-8320Publisher version
Language
- en