Natural disaster risk analysis for critical infrastructure systems: An approach based on statistical learning theory

Probabilistic risk analysis has historically been developed for situations in which measured data about the overall reliability of a system are limited and expert knowledge is the best source of information available. There continue to be a number of important problem areas characterized by a lack of hard data. However, in other important problem areas the emergence of information technology has transformed the situation from one characterized by little data to one characterized by data overabundance. Natural disaster risk assessments for events impacting large-scale, critical infrastructure systems such as electric power distribution systems, transportation systems, water supply systems, and natural gas supply systems are important examples of problems characterized by data overabundance. There are often substantial amounts of information collected and archived about the behavior of these systems over time. Yet it can be difficult to effectively utilize these large data sets for risk assessment. Using this information for estimating the probability or consequences of system failure requires a different approach and analysis paradigm than risk analysis for data-poor systems does. Statistical learning theory, a diverse set of methods designed to draw inferences from large, complex data sets, can provide a basis for risk analysis for data-rich systems. This paper provides an overview of statistical learning theory methods and discusses their potential for greater use in risk analysis.     

Probabilistic safety assessment support for the maintenance rule at Duke Power Company

The Nuclear Regulatory Commission (NRC) published the Maintenance Rule on July 10, 1991 with an implementation date of July 10, 1996 [1]. Maintenance rule implementation at the Duke Power Company has used probabilistic safety assessment (PSA) insights to help focus the monitoring of structures, systems and components (SSC) performance and to ensure that maintenance is effectively performed. This paper describes how the probabilistic risk assessment (PRA)¹ group at the Duke Power Company provides support for the maintenance rule by performing the following tasks: (1) providing a member of the expert panel; (2) determining the risk-significant SSCs; (3) establishing SSC performance criteria for availability and reliability; (4) evaluating past performance and its impact on core damage risk as part of the periodic assessment; (5) providing input to the PRA matrix; (6) providing risk analyses of combinations of SSCs out of service; (7) providing support for the SENTINEL program; and (8) providing support for PSA training. These tasks are not simply tied to the initial implementation of the rule. The maintenance rule must be kept consistent with the current design and operation of the plant. This will require that the PRA models and the many PSA calculations performed to support the maintenance rule are kept up-to-date. Therefore, support of the maintenance rule will be one of the primary roles of the PSA group for the remainder of the life of the plant.     

A fuzzy reasoning and fuzzy-analytical hierarchy process based approach to the process of railway risk information: A railway risk management system

Risk management is becoming increasingly important for railway companies in order to safeguard their passengers and employees while improving safety and reducing maintenance costs. However, in many circumstances, the application of probabilistic risk analysis tools may not give satisfactory results because the risk data are incomplete or there is a high level of uncertainty involved in the risk data. This article presents the development of a risk management system for railway risk analysis using fuzzy reasoning approach and fuzzy analytical hierarchy decision making process. In the system, fuzzy reasoning approach (FRA) is employed to estimate the risk level of each hazardous event in terms of failure frequency, consequence severity and consequence probability. This allows imprecision or approximate information in the risk analysis process. Fuzzy analytical hierarchy process (fuzzy-AHP) technique is then incorporated into the risk model to use its advantage in determining the relative importance of the risk contributions so that the risk assessment can be progressed from hazardous event level to hazard group level and finally to railway system level. This risk assessment system can evaluate both qualitative and quantitative risk data and information associated with a railway system effectively and efficiently, which will provide railway risk analysts, managers and engineers with a method and tool to improve their safety management of railway systems and set safety standards. A case study on risk assessment of shunting at Hammersmith depot is used to illustrate the application of the proposed risk assessment system.     

核电厂风险管理活动中的PRA质量要求

基于美国核管理委员会(NRC)推行的在核电厂运用的概率安全评价(PRA)技术,介绍PRA质量的含义、NRC在应用PRA过程中提出的分阶段提高PRA质量的方法以及相应的管理导则。结合国内现状,给出提高PRA质量的可接受方法。     

PRA-Based SMA: the First Tool toward a Risk-Informed Approach to the Seismic Design of the IRIS

International Reactor Innovative and Secure (IRIS) is an advanced, modular, medium-power PWR with an integral primary system layout. As part of the “safety-by-design_” philosophy that inspired the project from the very beginning, a risk-informed approach to its design phase is being adopted and a probabilistic risk assessment (PRA) is being used as an active tool in pursuing an advanced level of safety. Within this framework, a preliminary PRA-based seismic margin assessment (SMA) has been conducted to assess the ability of the IRIS standard design to respond to seismic events. A high confidence of low probability of failure at the core damage sequence level and then at the entire plant level is the primary result of the SMA model; in the end, it will have to ensure that IRIS can withstand the review-level earthquake of 0.5 g which is consistent with the upper bin level of the NUREG/CR-4334.1) In this preliminary phase of its development, in which the core of the quantitative data is critically extracted from the SMA of other PWR designs, the IRIS SMA model can be seen as a first step toward the development of an extensive seismic PRA model.     

Probabilistic simulation of cable performance and water based protection in cable tunnel fires

Nuclear power plants contain a significant amount of fire load in form of electrical cables. The performance of the cables is interesting both from the fire development and system failure viewpoints. In this work, cable tunnel fires are studied using numerical simulations, focusing on the fire spreading along power cables and the efficiency of the water suppression in preventing the cable failures. Probabilistic simulations are performed using Monte Carlo technique and the Fire Dynamics Simulator (FDS) as the deterministic fire model. The primary fire load, i.e. the power cables are modelled using the one-dimensional pyrolysis model, for which the material parameters are estimated from the experimental data. Two different water suppression systems are studied. The simulation results indicate that using either suppression system decreased the heat release rate in the tunnel to less than 10% of the non-suppressed case. Without water suppression, the cables of the second sub-system were damaged in almost all fires, but when either of the studied water suppression systems was used, the probability of the cable failures was decreased to less than 1%. This result indicates that in current scenario, the probability of losing both sub-systems is determined directly by the suppression system unavailability.     

A feasibility assessment of the use of nanofluids to enhance the in-vessel retention capability in light-water reactors

Nanofluids, colloidal dispersions of nanoparticles, exhibit a substantially higher critical heat flux (CHF) compared to water. As such, they could be used to enhance the in-vessel retention (IVR) capability in the severe accident management strategy implemented by certain light-water reactors. It is envisioned that, at normal operating conditions, the nanofluid would be stored in dedicated storage tanks, which, upon actuation, would discharge into the reactor cavity through injection lines. The design of the injection system was explored with risk-informed analyses and computational fluid dynamics. It was determined that the system has a reasonably low failure probability, and that, once injected, the nanofluid would be delivered effectively to the reactor vessel surface within seconds. It was also shown analytically that the increase in decay power removal through the vessel using a nanofluid is about 40%, which could be exploited to provide a higher IVR safety margin or, for a given margin, to enable IVR at higher core power. Finally, the colloidal stability of a candidate alumina-based nanofluid in an IVR environment was experimentally investigated, and it was found that this nanofluid would be stable against dilution, exposure to gamma radiation, and mixing with boric acid and lithium hydroxide, but not tri-sodium phosphate.     

一种低轨卫星网络的概率风险评估方法研究

可靠性分析是卫星星座设计过程中不可或缺的一环。针对低轨卫星网络的高可靠性要求,利用概率风险评估的分析方法,结合网络风险及故障树理论,提出了卫星故障树杠铃风险模型。在此模型基础上分析＂铱＂星系统网络,利用卫星工具箱获取每颗卫星对地覆盖人口数,以此为参数计算得到对网络影响较大的节点和链路,对于今后评估卫星网络风险具有一定的启发意义。     

Key attributes of the SAPHIRE risk and reliability analysis software for risk-informed probabilistic applications

The Idaho National Laboratory is a primary developer of probabilistic risk and reliability analysis (PRRA) tools, dating back over 35 years. Evolving from mainframe-based software, the current state-of-the-practice has led to the creation of the SAPHIRE software. Currently, agencies such as the Nuclear Regulatory Commission, the National Aeronautics and Aerospace Agency, the Department of Energy, and the Department of Defense use version 7 of the SAPHIRE software for many of their risk-informed activities. In order to better understand and appreciate the power of software as part of risk-informed applications, we need to recall that our current analysis methods and solution methods have built upon pioneering work done 30–40 years ago. We contrast this work with the current capabilities in the SAPHIRE analysis package. As part of this discussion, we provide information for both the typical features and special analysis capabilities, which are available. We also present the application and results typically found with state-of-the-practice PRRA models. By providing both a high-level and detailed look at the SAPHIRE software, we give a snapshot in time for the current use of software tools in a risk-informed decision arena.     

The impact of assessing lower head integrity on boiling water reactor probabilistic risk assessments

Over the last several decades, much effort has been directed at estimating the likelihood of a large early release of radioactivity during a nuclear accident. This effort has culminated in the Individual Plant Examinations (IPEs) for the over 100 US nuclear power plants and the NUREG 1150 study. The large early release of radioactivity requires core damage with loss of primary containment integrity during the accident. Given a successful reactor scram, early containment failure coupled with a large release of radioactivity will only occur if the reactor core vessel is breached by core debris. Most IPE/PRA studies performed to date have not considered the possibility of quenching core debris in the lower plenum. Consequently, lower head failure is presumed to closely follow the onset of core damage. Therefore, these assessments did not address the role that in-vessel debris retention plays in preserving primary containment integrity, nor do they propose a criterion for evaluating the integrity of the vessel lower head given that core damage has occurred. Yet preserving the vessel lower head integrity is a necessary condition for satisfying the plant design and licensing basis. Therefore, a more complete treatment of the risk associated with nuclear plant operation includes an evaluation of the ability to retain the core debris in-vessel. This paper presents a performance requirement for vessel integrity to be used in probabilistic risk assessments; evaluates the impact the core damage progression and lower plenum quenching models have on the likelihood of terminating the damage progression in-vessel; documents the significant reduction in BWR containment failure probability that can occur when appropriate core damage and lower head quenching models are used; reviews the implications of core debris quenching in the lower head on BWR PRA modeling; argues why crediting the capability to maintain vessel integrity is necessary from a safety point of view. These results and conclusions are derived from consideration of a BWR 4 plant with a 251 inch vessel. However, the concepts are generally applicable and results specific to other BWR designs can be developed using the methodology presented in this paper.