Robust vulnerability analysis of nuclear facilities subject to external hazards

Abstract

Natural hazards have the potential to trigger complex chains of events in technological installations leading to disastrous effects for the surrounding population and environment. The threat of climate change of worsening extreme weather events exacerbates the need for new models and novel methodologies able to capture the complexity of the natural-technological interaction in intuitive frameworks suitable for an interdisciplinary field such as that of risk analysis. This study proposes a novel approach for the quantification of risk exposure of nuclear facilities subject to extreme natural events. A Bayesian Network model, initially developed for the quantification of the risk of exposure from spent nuclear material stored in facilities subject to flooding hazards, is adapted and enhanced to include in the analysis the quantification of the uncertainty affecting the output due to the imprecision of data available and the aleatory nature of the variables involved. The model is applied to the analysis of the nuclear power station of Sizewell B in East Anglia (UK), through the use of a novel computational tool. The network proposed models the direct effect of extreme weather conditions on the facility along several time scenarios considering climate change predictions as well as the indirect effects of external hazards on the internal subsystems and the occurrence of human error. The main novelty of the study consists of the fully computational integration of Bayesian Networks with advanced Structural Reliability Methods, which allows to adequately represent both aleatory and epistemic aspects of the uncertainty affecting the input through the use of probabilistic models, intervals, imprecise random variables as well as probability bounds. The uncertainty affecting the output is quantified in order to attest the significance of the results and provide a complete and effective tool for riskinformed decision making.

Vulnerability of Hydropower Installations to Climate Change: Preliminary Study

“…Similarly, the methodology adopted integrates the use of Credal Networks (CNs), which can be regarded as a generalization of BNs able to include imprecise probabilities in the framework, with cutting-edge and robust SRMs. The choice of this particular methodology is justified by its large potential in the representation of the interaction between weather events and technological installations, as proved in former studies [19] [20]. Indeed the approach allows to embody the aleatory character of natural events as well as the epistemic uncertainty associated (in particular in the case of climate projections), through the use of probabilistic models, intervals or imprecise random variables.…”

Pseudo Credal Networks for Inference With Probability Intervals

“…In this way, the use of the information contained inside the CPTs as inputs in the joint distribution and, in combination with the Bayes' theorem, concede the capability of computing the posterior probability of any single or complex event modelled in the network. This process is known as belief updating or probabilistic inference and constitutes the warhorse of the predictive and diagnostic analyses as well as the what-if scenarios that can be produced with the Bayesian networks [16]. Further information about this matter will be treated later on section 2.3.…”

Bayesian networks with imprecise datasets: Application to oscillating water column

“…The type of information that can be adopted in this method involves real probability values (discrete nodes) or Gaussian distribution functions. The latter works with crisp value probabilities (Tolo et al 2016b). However, this characteristic turns into the main drawback of this technique when the data comes in the way of continuous distributions.…”

“…The integral shown in Eq. 6 is equivalent to that of a reliability problem (Tolo et al 2016b). Solving a structural reliability problem, i.e.…”