Risk Assessment of Spent Nuclear Fuel Facilities Considering Climate Change

Natural hazards have the capability to affect technological installations, triggering multiple failures and putting the population and the surrounding environment at risk. Global climate change introduces an additional and not negligible element of uncertainty to the vulnerability quantification, threatening to intensify (both in terms of frequency and severity) the occurrence of extreme climate events. Sea level extremes and extreme coastal high waters are expected to change in the future as a result of both changes in atmospheric storminess and mean sea level rise, as well as extreme precipitation events. These trends clearly suggest a parallel increase in the risks affecting technological installations and the subsequent need for mitigation measures to enhance the reliability of existing systems and to improve the design standards of new facilities. In spite of this situation, the scientific research in this field lacks robust and reliable tools for this kind of assessment, often relying on the adoption of oversimplified models or strong assumptions, which affect the credibility of the results. The main purpose of this study is to provide a novel and general model for the evaluation of the risk of exposure of spent nuclear fuel stored in a facility subject to flood hazard, investigating the potential and limitations of Bayesian networks (BNs) in this field. The network aims to model the interaction between extreme weather conditions and the technological installation, as well as the propagation of failures within the system itself, taking into account the dependencies among the different components and the occurrence of human error. A real-world application concerning the nuclear power station of Sizewell B in East Anglia, in the United Kingdom, is extensively described, together with the models and data set used. Results are presented for three different time scenarios in which climate change projections have been adopted to estimate future risk

Robust vulnerability analysis of nuclear facilities subject to external hazards

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 risk-informed decision making

Vulnerability of hydropower installations to climate change : preliminary study

The climate trends observed worldwide over the past few decades appear to corroborate the concerns of the scientific community about the many threats posed by global warming. Future changes of the current climate are expected to occur on different scales all around the globe, hence modifying the environmental background on the basis of which technological installations have been designed and operated. This can potentially threat the safety of the installations as well as their. The development of suitable tools aiming to predict the impact of climate change on technological installations is then essential in the wider context of climate change mitigation. Hydropower installations play often a crucial role not only as a long-term renewable resource of energy but also for flood control and water supply in the case of droughts. All these aspects highlight the increasing importance of such installations as well as their growing vulnerability to natural hazards. It is hence essential to enlarge the current understanding of the interaction mechanisms between such installations and the changing surrounding environment in order to take adequate measures for climate change adaptation and ensure the future safety and productivity of hydropower production. The current study aims to provide a novel model for the evaluation of the impact of climate change on the safety of hydropower stations. The approach adopted allows to include in the model the uncertainty inevitably associated with the input variables and to propagate such uncertainty within the analysis. The model proposed is finally applied to a realistic case-study in order to highlight its potential and limitations

A computational tool for Bayesian networks enhanced with reliability methods

A computational framework for the reduction and computation of Bayesian Networks enhanced with structural reliability methods is presented. During the last decades, the inner flexibility of the Bayesian Network method, its intuitive graphical structure and the strong mathematical background have attracted increasing interest in a large variety of applications involving joint probability of complex events and dependencies. Furthermore, the fast growing availability of computational power on the one side and the implementation of robust inference algorithms on the other, have additionally promoted the success of this method. Inference in Bayesian Networks is limited to only discrete variables (with the only exception of Gaussian distributions) in case of exact algorithms, whereas approximate approach allows to handle continuous distributions but can either result computationally inefficient or have unknown rates of convergence. This work provides a valid alternative to the traditional approach without renouncing to the reliability and robustness of exact inference computation. The methodology adopted is based on the combination of Bayesian Networks with structural reliability methods and allows to integrate random and interval variables within the Bayesian Network framework in the so called Enhanced Bayesian Networks. In the following, the computational algorithms developed are described and a simple structural application is proposed in order to fully show the capability of the tool developed

MCNPX simulations of the response of the extended-range rem meter WENDI-2

Proton therapy uses proton beams with energies typically between 50 and 230 MeV to treat cancerous tumors very efficiently, while protecting as much as possible surrounding healthy tissues from radiation damage. Protons interacting with matter inevitably induce secondary radiation from which all people inside the proton therapy center have to be protected. The ambient dose equivalent H*(10) in such a facility is mainly due to neutrons, which can have energies up to 230 MeV. Although various dose monitoring systems sensitive to high energy neutrons have already been developed, the response function of these detectors is often insufficiently characterized, and so are the calibration factors appropriate for the specific neutron spectra encountered inside a proton therapy facility. In this work, the Monte Carlo code MCNPX 2.5.0 has been used to study the response function of the extended-range rem-meter WENDI-2 from thermal energies up to 5 GeV. A good match has been obtained with equivalent simulation results found in literature. As a first step towards the characterization of the WENDI-2 response in continuous neutron fields, MCNPX simulations have also been carried out for the case-study of a bunker around an 18 MeV H-cyclotron, which involves neutron fields from thermal energies up to 18 MeV

Uncertainty quantification methods for neural networks pattern recognition

On-line monitoring techniques have attracted increasing attention as a promising strategy for improving safety, maintaining availability and reducing the cost of operation and maintenance. In particular, pattern recognition tools such as artificial neural networks are today largely adopted for sensor validation, plant component monitoring, system control, and fault-diagnostics based on the data acquired during operation. However, classic artificial neural networks do not provide an error context for the model response, whose robustness remains thus difficult to estimate. Indeed, experimental data generally exhibit a time/space-varying behaviour and are hence characterized by an intrinsic level of uncertainty that unavoidably affects the performance of the tools adopted and undermines the accuracy of the analysis. For this reason, the propagation of the uncertainty and the quantification of the so called margins of uncertainty in output are crucial in making risk-informed decision. The current study presents a comparison between two different approaches for the quantification of uncertainty in artificial neural networks. The first technique presented is based on the error estimation by a series association scheme, the second approach couples Bayesian model selection technique and model averaging into a unified framework. The efficiency of these two approaches are analysed in terms of their computational cost and predictive performance, through their application to a nuclear power plant fault diagnosis system

An open toolbox for the reduction, inference computation and sensitivity analysis of Credal Networks

Bayesian Networks are a flexible and intuitive tool associated with a robust mathematical background. They have attracted increasing interest in a large variety of applications in different fields. In spite of this, inference in traditional Bayesian Networks is generally limited to only discrete variables or to probabilistic distributions (adopting approximate inference algorithms) that cannot fully capture the epistemic imprecision of the data available. In order to overcome these limitations, Credal Networks have been proposed to integrate Bayesian Networks with imprecise probabilities which, adopting non-probabilistic or hybrid models, allow to fully represent the information available and its uncertainty. Here, a novel computational tool, implemented in the general purpose software OpenCossan, is proposed. The tool provides the reduction of Credal Networks through the use of structural reliability methods, in order to limit the cost associated with the inference computation without impoverishing the quality of the information initially introduced. Novel algorithms for the inference computation of networks involving probability bounds are provided. In addition, a novel sensitivity approach is proposed and implemented into the Toolbox in order to identify the maximum tolerable uncertainty associated with the inputs

Uncertainty on shallow landslide hazard assessment: from field data to hazard mapping

Shallow landsliding that involve Hillslope Deposits (HD), the surficial soil that cover the bedrock, is an important process of erosion, transport and deposition of sediment along hillslopes. Despite Shallow landslides generally mobilize relatively small volume of material, they represent the most hazardous factor in mountain regions due to their high velocity and the common absence of warning signs. Moreover, increasing urbanization and likely climate change make shallow landslides a source of widespread risk, therefore the interest of scientific community about this process grown in the last three decades. One of the main aims of research projects involved on this topic, is to perform robust shallow landslides hazard assessment for wide areas (regional assessment), in order to support sustainable spatial planning. Currently, three main methodologies may be implemented to assess regional shallow landslides hazard: expert evaluation, probabilistic (or data mining) methods and physical models based methods. The aim of this work is evaluate the uncertainty of shallow landslides hazard assessment based on physical models taking into account spatial variables such as: geotechnical and hydrogeologic parameters as well as hillslope morphometry. To achieve this goal a wide dataset of geotechnical properties (shear strength, permeability, depth and unit weight) of HD was gathered by integrating field survey, in situ and laboratory tests. This spatial database was collected from a study area of about 350 km2 including different bedrock lithotypes and geomorphological features. The uncertainty associated to each step of the hazard assessment process (e.g. field data collection, regionalization of site specific information and numerical modelling of hillslope stability) was carefully characterized. The most appropriate probability density function (PDF) was chosen for each numerical variable and we assessed the uncertainty propagation on HD strength parameters obtained by empirical relations with geotechnical index properties. Site specific information was regionalized at map scale by (hard and fuzzy) clustering analysis taking into account spatial variables such as: geology, geomorphology and hillslope morphometric variables (longitudinal and transverse curvature, flow accumulation and slope), the latter derived by a DEM with 10 m cell size. In order to map shallow landslide hazard, Monte Carlo simulation was performed for some common physically based models available in literature (eg. SINMAP, SHALSTAB, TRIGRS). Furthermore, a new approach based on the use of Bayesian Network was proposed and validated. Different models, such as Intervals, Convex Models and Fuzzy Sets, were adopted for the modelling of input parameters. Finally, an accuracy assessment was carried out on the resulting maps and the propagation of uncertainty of input parameters into the final shallow landslide hazard estimation was estimated. The outcomes of the analysis are compared and discussed in term of discrepancy among map pixel values and related estimated error. The novelty of the proposed method is on estimation of the confidence of the shallow landslides hazard mapping at regional level. This allows i) to discriminate regions where hazard assessment is robust from areas where more data are necessary to increase the confidence level and ii) to assess the reliability of the procedure used for hazard assessment

COSSAN SOFTWARE: A MULTIDISCIPLINARY AND COLLABORATIVE SOFTWARE FOR UNCERTAINTY QUANTIFICATION

Computer-Aided modelling and simulation is now widely recognised as the third 'leg' of scientific method, alongside theory and experimentation. Many phenomena can be studied only by using computational processes such as complex simulations or analysis of experimental data. In addition, in many engineering fields computational approaches and virtual prototypes are used to support and drive the design of new components, structures and systems. A general purpose software for uncertainty quantification and risk assessment, named COSSAN, is under continuous development. This is a multi-disciplinary software that satisfies industry requirements regarding numerical efficiency and analysis of detailed models that can be used to solve a wide range of industrial and scientific problems. The main aim of the COSSAN software is to allow the inclusion of non-deterministic analyses as a practice standard routing in scientific computing. This paper presents two recent toolboxes added to the OPENCOSSAN: Credal Networks and Interval Predictive model

Dynamic analysis of a pumped-storage hydropower plant with random power load

This paper analyzes the dynamic response of a pumped-storage hydropower plant in generating mode. Considering the elastic water column effects in the penstock, a linearized reduced order dynamic model of the pumped-storage hydropower plant is used in this paper. As the power load is always random, a set of random generator electric power output is introduced to research the dynamic behaviors of the pumped-storage hydropower plant. Then, the influences of the PI gains on the dynamic characteristics of the pumped-storage hydropower plant with the random power load are analyzed. In addition, the effects of initial power load and PI parameters on the stability of the pumped-storage hydropower plant are studied in depth. All of the above results will provide theoretical guidance for the study and analysis of the pumped-storage hydropower plant