This paper explores the viability of using counterfactual reasoning for impact analyses when understanding and responding to "beyond-design-basis" nuclear power plant accidents. Currently, when a severe nuclear power plant accident occurs, plant operators rely on Severe Accident Management Guidelines. However, the current guidelines are limited in scope and depth: for certain types of accidents, plant operators would have to work to mitigate the damage with limited experience and guidance for the particular situation. We aim to fill the need for comprehensive accident support by using a dynamic Bayesian network to aid in the diagnosis of a nuclear reactor's state and to analyze the impact of possible response measures. The dynamic Bayesian network, DBN, offers an expressive representation of the components and relationships that make up a complex causal system. For this reason, and for its tractable reasoning, the DBN supports a functional model for the intricate operations of nuclear power plants. In this domain, it is also pertinent that a Bayesian network can be composed of both probabilistic and knowledge-based components. Though probabilities can be calculated from simulated models, the structure of the network, as well as the value of some parameters, must be assigned by human experts. Since dynamic Bayesian network-based systems are capable of running better-than-real-time situation analyses, they can support both current event and alternate scenario impact analyses.
The HyRAM software toolkit provides a basis for conducting quantitative risk assessment and consequence modeling for hydrogen infrastructure and transportation systems. HyRAM is designed to facilitate the use of state-of-the-art science and engineering models to conduct robust, repeatable assessments of hydrogen safety, hazards, and risk. HyRAM is envisioned as a unifying platform combining validated, analytical models of hydrogen behavior, a stan- dardized, transparent QRA approach, and engineering models and generic data for hydrogen installations. HyRAM is being developed at Sandia National Laboratories for the U. S. De- partment of Energy to increase access to technical data about hydrogen safety and to enable the use of that data to support development and revision of national and international codes and standards. This document provides a description of the methodology and models contained in the HyRAM version 1.1. HyRAM 1.1 includes generic probabilities for hydrogen equipment fail- ures, probabilistic models for the impact of heat flux on humans and structures, and computa- tionally and experimentally validated analytical and first order models of hydrogen release and flame physics. HyRAM 1.1 integrates deterministic and probabilistic models for quantifying accident scenarios, predicting physical effects, and characterizing hydrogen hazards (thermal effects from jet fires, overpressure effects from deflagrations), and assessing impact on people and structures. HyRAM is a prototype software in active development and thus the models and data may change. This report will be updated at appropriate developmental intervals.
This paper presents a risk assessment of a Liquefied Natural Gas (LNG)/diesel hybrid locomotive to identify and rank failures that could result in the release of LNG or Gaseous Natural Gas (GNG) to the surrounding environment. The Federal Railroad Administration (FRA) will analyze industry safety assessments of the proposed rail vehicles and the goal of this risk analysis is to identify and prioritize hazard scenarios so the FRA can ensure that they are properly addressed. For operational activities, a Failure Modes and Effects Analysis (FMEA) was performed to identify high risk failure modes. A modified hazard and operability study (HAZOP) methodology was used to analyze hazard scenarios for the maintenance activities for the LNG and Compressed Natural Gas (CNG) dual-fuel locomotives and the LNG tender car. Because refueling operations are highly dependent on human interactions, a human factors assessment was also performed on a sample refueling procedure to identify areas of improvement and identify best practices for analyzing future procedures. The FMEA resulted in the identification of 87 total failure modes for the operational phase, three of which were deemed to have a High risk priority, all involving the cryogenic storage tank. The HAZOP for the LNG tender resulted in the identification of eight credible hazard scenarios and the HAZOP for the locomotive in the maintenance mode identified 27 credible hazard scenarios. The high and medium risk failure modes and hazard scenarios should be prioritized for further analysis.
Hydrogen Risk Assessment Models (HyRAM) is a prototype software toolkit that integrates data and methods relevant to assessing the safety of hydrogen fueling and storage infrastructure. The HyRAM toolkit integrates deterministic and probabilistic models for quantifying accident scenarios, predicting physical effects, and characterizing the impact of hydrogen hazards, including thermal effects from jet fires and thermal pressure effects from deflagration. HyRAM version 1.0 incorporates generic probabilities for equipment failures for nine types of components, and probabilistic models for the impact of heat flux on humans and structures, with computationally and experimentally validated models of various aspects of gaseous hydrogen release and flame physics. This document provides an example of how to use HyRAM to conduct analysis of a fueling facility. This document will guide users through the software and how to enter and edit certain inputs that are specific to the user-defined facility. Description of the methodology and models contained in HyRAM is provided in [1]. This User’s Guide is intended to capture the main features of HyRAM version 1.0 (any HyRAM version numbered as 1.0.X.XXX). This user guide was created with HyRAM 1.0.1.798. Due to ongoing software development activities, newer versions of HyRAM may have differences from this guide.
The HySafe research priorities workshop is held on the even years between the International Conference on Hydrogen Safety (ICHS) which is held on the odd years. The research priorities workshop is intended to identify the state-of-the-art in understanding of the physical behavior of hydrogen and hydrogen systems with a focus on safety. Typical issues addressed include behavior of unintended hydrogen releases, transient combustion phenomena, effectiveness of mitigation measures, and hydrogen effects in materials. In the workshop critical knowledge gaps are identified. Areas of research and coordinated actions for the near and medium term are derived and prioritized from these knowledge gaps. The stimulated research helps pave the way for the rapid and safe deployment of hydrogen technologies on a global scale. To support the idea of delivering globally accepted research priorities for hydrogen safety the workshop is organized as an internationally open meeting. In attendance are stakeholders from the academic community (universities, national laboratories), funding agencies, and industry. The industry participation is critically important to ensure that the research priorities align with the current needs of the industry responsible for the deployment of hydrogen technologies. This report presents the results of the HySafe Research Priorities Workshop held in Washing ton, D.C. on November 10-11, 2014. At the workshop the participants presented updates (since the previous workshop organized two years before in Berlin, Germany) of their research and development work on hydrogen safety. Following the workshop, participants were asked to provide feedback on high-priority topics for each of the research areas discussed and to rank research area categories and individual research topics within these categories.
When a severe nuclear power plant accident occurs, plant operators rely on Severe Accident Management Guidelines (SAMGs). However, current SAMGs are limited in scope and depth. The plant operators must work to mitigate the accident with limited experience and guidance for the situation. The SMART (Safely Managing Accidental Reactor Transients) procedures framework aims to fill the need for detailed guidance by creating a comprehensive probabilistic model, using a Dynamic Bayesian Network, to aid in the diagnosis of the reactor's state. In this paper, we explore the viability of the proposed SMART proceedures approach by building a prototype Bayesian network that allows tor the diagnosis of two types of accidents based on a comprehensive data set. We use Kullback-Leibler (K-L) divergence to gauge the relative importance of each of the plant's parameters. We compare accuracy and F-score measures across four different Bayesian networks: a baseline network that ignores observation variables, a network that ignores data from the observation variable with the highest K-L score, a network that ignores data from the variable with the lowest K-L score, and finally a network that includes all observation variable data. We conclude with an interpretation of these results for SMART procedures.
The HyRAM software toolkit provides a basis for conducting quantitative risk assessment and consequence modeling for hydrogen infrastructure and transportation systems. HyRAM is designed to facilitate the use of state-of-the-art science and engineering models to conduct robust, repeatable assessments of hydrogen safety, hazards, and risk. HyRAM is envisioned as a unifying platform combining validated, analytical models of hydrogen behavior, a standardized, transparent QRA approach, and engineering models and generic data for hydrogen installations. HyRAM is being developed at Sandia National Laboratories for the U. S. Department of Energy to increase access to technical data about hydrogen safety and to enable the use of that data to support development and revision of national and international codes and standards. This document provides a description of the methodology and models contained in the HyRAM version 1.0. HyRAM 1.0 includes generic probabilities for hydrogen equipment failures, probabilistic models for the impact of heat flux on humans and structures, and computationally and experimentally validated analytical and first order models of hydrogen release and flame physics. HyRAM 1.0 integrates deterministic and probabilistic models for quantifying accident scenarios, predicting physical effects, and characterizing hydrogen hazards (thermal effects from jet fires, overpressure effects from deflagrations), and assessing impact on people and structures. HyRAM is a prototype software in active development and thus the models and data may change. This report will be updated at appropriate developmental intervals.
Accident management is an important component to maintaining risk at acceptable levels for all complex systems, such as nuclear power plants. With the introduction of passive, or inherently safe, reactor designs the focus has shifted from management by operators to allowing the system's design to take advantage of natural phenomena to manage the accident. Inherently and passively safe designs are laudable, but nonetheless extreme boundary conditions can interfere with the design attributes which facilitate inherent safety, thus resulting in unanticipated and undesirable end states. This report examines an inherently safe and small sodium fast reactor experiencing a variety of beyond design basis events with the intent of exploring the utility of a Dynamic Bayesian Network to infer the state of the reactor to inform the operator's corrective actions. These inferences also serve to identify the instruments most critical to informing an operator's actions as candidates for hardening against radiation and other extreme environmental conditions that may exist in an accident. This reduction in uncertainty serves to inform ongoing discussions of how small sodium reactors would be licensed and may serve to reduce regulatory risk and cost for such reactors.
This article documents an exploratory study for collecting and using human performance data to inform human error probability (HEP) estimates for a new human reliability analysis (HRA) method, the IntegrateD Human Event Analysis System (IDHEAS). The method was based on cognitive models and mechanisms underlying human behaviour and employs a framework of 14 crew failure modes (CFMs) to represent human failures typical for human performance in nuclear power plant (NPP) internal, at-power events [1]. A decision tree (DT) was constructed for each CFM to assess the probability of the CFM occurring in different contexts. Data needs for IDHEAS quantification are discussed. Then, the data collection framework and process is described and how the collected data were used to inform HEP estimation is illustrated with two examples. Next, five major technical challenges are identified for leveraging human performance data for IDHEAS quantification. These challenges reflect the data needs specific to IDHEAS. More importantly, they also represent the general issues with current human performance data and can provide insight for a path forward to support HRA data collection, use, and exchange for HRA method development, implementation, and validation.
HyRAM is a prototype software toolkit that integrates data and methods relevant to assessing the safety of hydrogen fueling and storage infrastructure. The HyRAM toolkit integrates deterministic and probabilistic models for quantifying accident scenarios, predicting physical effects, and characterizing the impact of hydrogen hazards, including thermal effects from jet fires and thermal pressure effects from deflagration.
Building a hydrogen infrastructure system is critical to supporting the development of alternate- fuel vehicles. This report provides a methodology for implementing a performance-based design of an outdoor hydrogen refueling station that does not meet specific prescriptive requirements in NFPA 2, The Hydrogen Technologies Code . Performance-based designs are a code-compliant alternative to meeting prescriptive requirements. Compliance is demonstrated by comparing a prescriptive-based fueling station design with a performance-based design approach using Quantitative Risk Assessment (QRA) methods and hydrogen risk assessment tools. This template utilizes the Sandia-developed QRA tool, Hydrogen Risk Analysis Models (HyRAM), which combines reduced-order deterministic models that characterize hydrogen release and flame behavior with probabilistic risk models to quantify risk values. Each project is unique and this template is not intended to account for site-specific characteristics. Instead, example content and a methodology are provided for a representative hydrogen refueling site which can be built upon for new hydrogen applications.
Accident management is an important component to maintaining risk at acceptable levels for all complex systems, such as nuclear power plants. With the introduction of self-correcting, or inherently safe, reactor designs the focus has shifted from management by operators to allowing the system's design to manage the accident. Inherently and passively safe designs are laudable, but nonetheless extreme boundary conditions can interfere with the design attributes which facilitate inherent safety, thus resulting in unanticipated and undesirable end states. This report examines an inherently safe and small sodium fast reactor experiencing a beyond design basis seismic event with the intent of exploring two issues: (1) can human intervention either improve or worsen the potential end states and (2) can a Bayesian Network be constructed to infer the state of the reactor to inform.
Disposal overpacks are proposed as an element of the engineered barrier system for direct disposal of spent nuclear fuel in dual-purpose canisters (DPCs). DPCs are currently licensed for storage and transport, but not disposal. In the DPC disposal system, overpacks would provide long-term containment, and conversely, they would keep groundwater from flooding DPCs. Without flooding, DPCs can never achieve nuclear criticality because they are under-moderated.
Accident management is an important component to maintaining risk at acceptable levels for all complex systems, such as nuclear power plants. With the introduction of self - correcting, or inherently safe, reactor designs the focus has shifted from management by operators to allowing the syste m's design to manage the accident. While inherently and passively safe designs are laudable, extreme boundary conditions can interfere with the design attributes which facilitate inherent safety , thus resulting in unanticipated and undesirable end states. This report examines an inherently safe and small sodium fast reactor experiencing a beyond design basis seismic event with the intend of exploring two issues : (1) can human intervention either improve or worsen the potential end states and (2) can a Bayes ian Network be constructed to infer the state of the reactor to inform (1). ACKNOWLEDGEMENTS The author s would like to acknowledge the U.S. Department of E nergy's Office of Nuclear Energy for funding this research through Work Package SR - 14SN100303 under the Advanced Reactor Concepts program. The authors also acknowledge the PRA teams at A rgonne N ational L aborator y , O ak R idge N ational L aborator y , and I daho N ational L aborator y for their continue d contributions to the advanced reactor PRA mission area.
A series of test cases designed to verify the correct implementation of several features of the CPLOAS_2 program are documented. CPLOAS_2 is used to calculate the probability of loss of assured safety (PLOAS) for a weak link (WL)/strong link (SL) system. CPLOAS_2 takes physical properties (e.g., temperature, pressure, etc.) of a WL/SL system and uses these properties and definitions of link failure properties in probabilistic calculations to determine PLOAS. The features being tested include (i) six aleatory distribution forms, (ii) five numerical procedures for the determination of PLOAS (i.e., one quadrature procedure, two simple random sampling procedures, and two importance sampling procedures), and (iii) time and environmental margin calculations. All tests were performed with CPLOAS_2 version 2.10.
In the past several years, several international agencies have begun to collect data on human performance in nuclear power plant simulators [1]. This data provides a valuable opportunity to improve human reliability analysis (HRA), but there improvements will not be realized without implementation of Bayesian methods. Bayesian methods are widely used in to incorporate sparse data into models in many parts of probabilistic risk assessment (PRA), but Bayesian methods have not been adopted by the HRA community. In this article, we provide a Bayesian methodology to formally use simulator data to refine the human error probabilities (HEPs) assigned by existing HRA methods. We demonstrate the methodology with a case study, wherein we use simulator data from the Halden Reactor Project to update the probability assignments from the SPAR-H method. The case study demonstrates the ability to use performance data, even sparse data, to improve existing HRA methods. Furthermore, this paper also serves as a demonstration of the value of Bayesian methods to improve the technical basis of HRA.
There has been increasing interest in using Quantitative Risk Assessment [QRA] to help improve the safety of hydrogen infrastructure and applications. Hydrogen infrastructure for transportation (e.g. fueling fuel cell vehicles) or stationary (e.g. back-up power) applications is a relatively new area for application of QRA vs. traditional industrial production and use, and as a result there are few tools designed to enable QRA for this emerging sector. There are few existing QRA tools containing models that have been developed and validated for use in small-scale hydrogen applications. However, in the past several years, there has been significant progress in developing and validating deterministic physical and engineering models for hydrogen dispersion, ignition, and flame behavior. In parallel, there has been progress in developing defensible probabilistic models for the occurrence of events such as hydrogen release and ignition. While models and data are available, using this information is difficult due to a lack of readily available tools for integrating deterministic and probabilistic components into a single analysis framework. This paper discusses the first steps in building an integrated toolkit for performing QRA on hydrogen transportation technologies and suggests directions for extending the toolkit.
Developing a big picture understanding of a severe accident is extremely challenging. Operating crews and emergency response teams are faced with rapidly evolving circumstances, uncertain information, distributed expertise, and a large number of conflicting goals and priorities. Severe accident management guidance (SAMGs) provides support for collecting information and assessing the state of a nuclear power plant during severe accidents. However, SAMGs developers cannot anticipate every possible accident scenario. Advanced Probabilistic Risk Assessment (PRA) methods can be used to explore an extensive space of possible accident sequences and consequences. Using this advanced PRA to develop a decision support system can provide expanded support for diagnosis and response. In this paper, we present an approach that uses dynamic PRA to develop risk-informed "Smart SAMGs". Bayesian Networks form the basis of the faster-than-real-time decision support system. The approach leverages best-available information from plant physics simulation codes (e.g., MELCOR). Discrete Dynamic Event Trees (DDETs) are used to provide comprehensive coverage of the potential accident scenario space. This paper presents a methodology to develop Smart procedures and provides an example model created for diagnosing the status of the ECCS valves in a generic iPWR design.
The Quantitative Risk Assessment (QRA) Toolkit Introduction Workshop was held at Energetics on June 11-12. The workshop was co-hosted by Sandia National Laboratories (Sandia) and HySafe, the International Association for Hydrogen Safety. The objective of the workshop was twofold: (1) Present a hydrogen-specific methodology and toolkit (currently under development) for conducting QRA to support the development of codes and standards and safety assessments of hydrogen-fueled vehicles and fueling stations, and (2) Obtain feedback on the needs of early-stage users (hydrogen as well as potential leveraging for Compressed Natural Gas [CNG], and Liquefied Natural Gas [LNG]) and set priorities for %E2%80%9CVersion 1%E2%80%9D of the toolkit in the context of the commercial evolution of hydrogen fuel cell electric vehicles (FCEV). The workshop consisted of an introduction and three technical sessions: Risk Informed Development and Approach; CNG/LNG Applications; and Introduction of a Hydrogen Specific QRA Toolkit.
Probabilistic Risk Assessment (PRA) is a fundamental part of safety/quality assurance for nuclear power and nuclear weapons. Traditional PRA very effectively models complex hardware system risks using binary probabilistic models. However, traditional PRA models are not flexible enough to accommodate non-binary soft-causal factors, such as digital instrumentation&control, passive components, aging, common cause failure, and human errors. Bayesian Networks offer the opportunity to incorporate these risks into the PRA framework. This report describes the results of an early career LDRD project titled %E2%80%9CUse of Limited Data to Construct Bayesian Networks for Probabilistic Risk Assessment%E2%80%9D. The goal of the work was to establish the capability to develop Bayesian Networks from sparse data, and to demonstrate this capability by producing a data-informed Bayesian Network for use in Human Reliability Analysis (HRA) as part of nuclear power plant Probabilistic Risk Assessment (PRA). This report summarizes the research goal and major products of the research.
11th International Probabilistic Safety Assessment and Management Conference and the Annual European Safety and Reliability Conference 2012, PSAM11 ESREL 2012
Many of the Performance Shaping Factors (PSFs) used in Human Reliability Analysis (HRA) methods are not directly measurable or observable. Methods like SPAR-H require the analyst to assign values for all of the PSFs, regardless of the PSF observability; this introduces subjectivity into the human error probability (HEP) calculation. One method to reduce the subjectivity of HRA estimates is to formally incorporate information about the probability of the PSFs into the methodology for calculating the HEP. This can be accomplished by encoding prior information in a Bayesian Network (BN) and updating the network using available observations. We translated an existing HRA methodology, SPAR-H, into a Bayesian Network to demonstrate the usefulness of the BN framework. We focus on the ability to incorporate prior information about PSF probabilities into the HRA process. This paper discusses how we produced the model by combining information from two sources, and how the BN model can be used to estimate HEPs despite missing observations. Use of the prior information allows HRA analysts to use partial information to estimate HEPs, and to rely on the prior information (from data or cognitive literature) when they are unable to gather information about the state of a particular PSF. The SPAR-H BN model is a starting point for future research activities to create a more robust HRA BN model using data from multiple sources.