Safety and risk assessment are key priorities for nuclear power plants. Probabilistic risk assessment (PRA) is a method for quantitative evaluation of accident risk, in particular severe nuclear core damage and the associated release of radioactive materials into the environment. The reliability and certainty of PRA have at times been questioned, especially when real-world observations have indicated that the frequency of nuclear accidents is higher than the probabilities predicted by PRA. This thesis provides a philosophical review of the epistemic uncertainties in PRA, using the Fukushima Daiichi accident of March 2011 as a case study. The thesis provides an overview of the PRA model structure, its key elements, and possible sources of uncertainty, in an attempt to understand the deviation between the real frequency of nuclear core-melt accidents and the probabilities predicted by PRA.The analyses in this thesis address several sources of epistemic uncertainty in PRA. Analyses of the PRA approach reveal the difficulty involved in covering all possible initiating events, all component and system failures, as well as their possible combinations in the risk evaluations. This difficulty represents the source of a characteristic epistemic uncertainty, referred to as completeness uncertainty. Analyses from the case study (the Fukushima Daiichi accident) illustrate this difficulty, as the PRA failed to identify a combined earthquake and tsunami, with the resultant flooding and consequent power failure and total blackout, as an initiating causal event in its logic structure.The analyses further demonstrate how insufficient experience and knowledge, as well as a lack of empirical data, lead to incorrect assumptions, which are used by the model as input parameters to estimate the probabilities of accidents. With limited availability of input data, decision-makers rely upon the subjective judgements and individual experiences of experts, which adds a further source of epistemic uncertainty to the PRA, usually referred to as input parameter uncertainty. As a typical example from the case study, the Fukushima Daiichi accident revealed that the PRA had underestimated the height of a possible tsunami. Consequently, the risk mitigation systems (e.g. the barrier seawalls) built to protect the power plant were inadequate due to incorrect input data.Poor assumptions may also result in improper modeling of failure modes and sequences in the PRA logic structure, which makes room for an additional source of epistemic uncertainty referred to as model uncertainty. For instance, the Fukushima Daiichi accident indicated insufficient backup of the power supply, because the possibility of simultaneous failure of several emergency diesel generators was assumed to be negligibly small. However, that was exactly what happened when 12 out of the 13 generators failed at the same time as a result of flooding.Furthermore, the analyses highlight the difficulty of modeling the human interventions and actions, in particular during the course of unexpected accidents, taking into account the physiological and psychological effects on the cognitive performance of humans, which result in uncertain operator interventions. This represents an additional source of epistemic uncertainty, usually referred to as uncertainty in modeling human interventions. As a result, there may be an increase in the probability of human error, characterized by a delay in making a diagnosis, formulating a response and taking action. Even this statement confirms the complexity of modelling human errors. In the case of the Fukushima Daiichi accident, lack ofvsufficient instructions for dealing with this "unexpected" accident made the coordination of operators' interventions almost impossible.Given the existence of all these sources of epistemic uncertainty, it would be reasonable to expect such a detected deviation between the real frequency of nuclear core-melt accidents and the probabilities predicted by PRA.It is, however, important to highlight that the occurrence of the Fukushima Daiichi accident could lie within the uncertainty distribution that the PRA model predicted prior to the accident. Hence, from the probabilistic point of view, the occurrence of a single unexpected accident should be interpreted with care, especially in political and commercial debates. Despite the limitations that have been highlighted in this thesis, the model still can provide valuable insights for systematic examination of safety systems, risk mitigation approaches, and strategic plans aimed at protecting the nuclear power plants against failures. Nevertheless, the PRA model does have development potentials, which deserves serious attention. The validity of calculated frequencies in PRA is restricted to the parameter under study. This validity can be improved by adding further relevant scenarios to the PRA, improving the screening approaches and collecting more input data through better collaboration between nuclear power plants world-wide. Lessons learned from the Fukushima Daiichi accident have initiated further studies aimed at covering additional scenarios. In subsequent IAEA safety report series, external hazards in multi-unit nuclear power plants have been considered. Such an action shows that PRA is a dynamic approach that needs continuous improvement toward better reliability.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:kth-285685 |
| Date | January 2020 |
| Creators | Rawandi, Omed A. |
| Publisher | KTH, Filosofi |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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