Network Risk Assessment Toolkit (NetRAT)
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Poster for ECRA poster session.
This research effort is a part of the Light Water Reactor Sustainability (LWRS) Program, which is a research and development (R&D) program sponsored by the Department of Energy. The LWRS Program is performed in close collaboration with industry R&D programs that provide the technical foundations for licensing and managing the long term, safe, and economical operation of current nuclear power plants (NPPs). The LWRS Program serves to help the United States (U.S.) nuclear industry adopt new technologies and engineering solutions that facilitate the continued safe operation of these NPPs and the extension of their current operating licenses. The Outage Risk Management Improvement Pilot Project seeks to improve the management of nuclear power plant outages. This is done through the development of tools to assist in evaluating pending activities against requirements to detect undesired interactions. Significant efforts are expended to manage the nuclear risk of an outage. The utilities conduct pre outage risk assessments, based on a very detailed review of the outage schedule, to identify where combinations of outage work and out of service equipment would result in degraded conditions with respect to nuclear safety or regulatory compliance. Probabilistic risk assessment studies are conducted to quantify incremental core damage frequency as a result of the outage activities and system unavailability. These studies are usually presented to site and fleet management, the site plant operational review committee, and the NPP’s independent Nuclear Safety Review Board for concurrence that the outage is planned safely and that reasonable measures have been taken to reduce the added risk of conducting the outage. During the outage, the plant configuration is monitored continuously to ensure that it conforms to the approved safety plan. Deviations must be assessed and approved by management committees and, in some cases, the plant operational review committee. In virtually all outage meetings and job briefings, the current nuclear safety status of the plant is communicated, including information on the specific equipment necessary to meet the requirements of the nuclear safety plan. In addition, the operations and outage organizations implement several layers of physical and administrative barriers to prevent unintended interaction with the systems and equipment credited for nuclear safety. In spite of all these efforts, nuclear safety challenges still occur too frequently during outages. While some of these are due to failures of equipment credited for safety, the majority occur because of human error. These typically involve some form of interaction between work activities and plant configuration changes. Some of them are very subtle and are extremely challenging to detect in advance. Nevertheless, they are not acceptable and represent clear opportunities to improve nuclear safety during outages. This particular report will provide a demonstration of the capabilities in the development of a predictive software platform for improving configuration control and accessing the plant risks associated with outage work protocols and illustrate how the software assists in strategies to minimize these interactions. In addition to maintaining nuclear safety, there are numerous administrative requirements that must be complied with during an outage. This report will discuss the use of logic modelling to monitor planned work against technical specification requirements for given plant conditions and to integrate these requirements with shutdown risk models, clearance order requirements and other plant condition requirements.
This work plan is part of an effort to complete an updated evaluation of the potential for adverse effects from miscellaneous treatment unit emissions to potential ecological receptors within and surrounding Site 200. It will address potential pathways between air emissions from the Lawrence Livermore National Laboratory’s (LLNL) Decontamination and Waste Treatment Facility (DWTF), single emission stack, and ecological receptors (i.e., plants and wildlife) present onsite and within a one-mile radius from the DWTF stack. This work plan outlines the procedures by which data collected as part of the previous investigations will be evaluated to assess risks to ecological receptors. It presents the problem formulation, initial conceptual site models, methods for characterizing exposure, toxicity, and risks that will be used if a more detailed “baseline” ecological risk assessment (BERA) is required.
HAGEN project has developed theory, algorithms, and capabilities to assist cyber physical system modelers and operators to perform system and device-level vulnerability assessment, risk assessment, impact assessment, and mitigation planning. The project generates hybrid attack graphs for Cyber-Physical System (CPS) resilience experimentation at desired scale and speed. The project will produce composite attack datasets, algorithms, and demonstrable prototypical tools, and a library of high-impact attack sequences for a given CPS of interest. This report provided overall summary of research and development performed between FY22-24.
This project will develop the scientific basis for evaluating risk in high consequence cyber-physical systems that make up critical infrastructure
This manuscript discusses the challenges in detecting and attributing recently observed trends in the Atlantic tropical cyclone (TC) and the epistemic uncertainty we face in assessing future risk. We use synthetic storms downscaled from five CMIP5 models by the Columbia HAZard model (CHAZ), and directly simulated storms from high-resolution climate models. We examine three aspects of recent TC activity: the upward trend and multi-decadal oscillation of the annual frequency, the increase in storm wind intensity, and the decrease in forward speed. Some data sets suggest that these trends and oscillation are forced while others suggest that they can be explained by natural variability. Projections under warming climate scenarios also show a wide range of possibilities, especially for the annual frequencies, which increase or decrease depending on the choice of moisture variable used in the CHAZ model and on the choice of climate model. The uncertainties in the annual frequency lead to epistemic uncertainties in TC risk assessment. Here, we investigate the potential for reduction of these epistemic uncertainties through a statistical practice, namely likelihood analysis. We find that historical observations are more consistent with the simulations with increasing frequency than those with decreasing frequency, but we are not able to rule out the latter. We argue that the most rational way to treat epistemic uncertainty is to consider all outcomes contained in the results. In the context of risk assessment, since the results contain possible outcomes in which TC risk is increasing, this view implies that the risk is increasing.
This report documents the activities performed by the Idaho National Laboratory (INL) during fiscal year (FY) 2020 for the U.S. Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) Program, Risk-Informed System Analysis (RISA) Pathway, Enhanced Resilient Plant (ERP) Systems research. The purpose of the RISA Pathway research and development is to support plant owner-operator decisions with the aim to improve the economics, reliability, and maintain the high levels of safety of current nuclear power plants over periods of extended plant operations. The concept of ERP refers to the combinations of accident-tolerant fuel (ATF), optimal use of diverse and flexible coping strategy (FLEX), enhancements to plant components and systems, and the incorporation of augmented or new passive cooling systems, as well as improved fuel cycle efficiency. The objective of the ERP research effort is to use the RISA methods and toolkit in industry applications, including methods development and early demonstration of technologies, in order to enhance existing reactors’ safety features (both active and passive) and to substantially reduce operating costs through risk-informed approaches to plant design modifications to the plant and their characterization. One main focus of the FY 2020 efforts documented in this report was to extend the analyses conducted in FYs 2018 and 2019 for a pressurized water reactor (PWR) to a boiling water reactor (BWR). The same analysis process, risk analysis approaches, and analysis tools as in the previous work for PWR were used for a generic BWR with near-term ATF cladding (i.e., Iron-Chromium-Aluminum [FeCrAl] cladding and Chromium [Cr]-coated cladding) designs under the postulated station blackout (SBO) and medium loss-of-coolant (MLOCA) accident scenarios. In addition, a FLEX model was developed and incorporated into a generic BWR probabilistic risk assessment (PRA) model using the INL-developed software tool, Systems Analysis Programs for Hands-on Integrated Reliability Evaluations (SAPHIRE), to assess the risk impact from FLEX. The other main focus of the FY 2020 efforts was to advance analysis methods, including developing dynamic approach for FLEX human reliability analysis (HRA) using the INL-developed software tool, Event Modeling Risk Assessment using Linked Diagrams (EMRALD), as well as developing a multicriterion benefit evaluation (MCBE) method for evaluating costs and benefits of safety enhancements in nuclear power plants (NPPs). As a case study, the MCBE method was applied to evaluate the costs and benefits brought by FLEX implementation.
Advancements in nuclear system designs with automated control features provide many benefits, but can lead to complex coupled systems and dynamic failure scenarios. This is especially true for microreactor designs where components are not expected to be replaced during the reactor’s lifetime. Hence, the life of the system, in addition to the safety, needs to be evaluated. Modeling these sequences of time-dependent events requires addressing cyclical processes and changing failure rates in ways that represent the actual system dynamics in contrast to a single sampling for a component’s time to failure. This research presents two distinct analytical methods for several failure distributions that evaluate a final time to failure used for different scenarios where the time to failure must be sampled multiple times. The first method is used when evaluating a component whose failure rate increases due to an outside event after the initial sampling but before the initially sampled time to failure. The second method is used when evaluating multiple identical components or a component that has been replaced with a new identical version before the second sampling. The two methods were implemented in a few representative case studies developed in the dynamic probabilistic risk assessment tool Event Modeling Risk Assessment using Linked Diagrams. Overall, this paper provides guidelines on how these approaches give a more realistic and accurate dynamic probabilistic risk assessment of complex systems.
Techniques and apparatuses are described for a cybersecurity risk management tool to assess cybersecurity risk and prioritize cybersecurity correction plans. The cybersecurity risk management tool categorizes cybersecurity framework security controls into maturity indicator levels, identifies implementation states achieved by an entity with respect to the cybersecurity framework security controls, and determines which of the maturity indicator levels represents the implementation state achieved by the entity with respect to each of the cybersecurity framework security controls. A cost-benefit analysis for modifying from the implementation state achieved by the entity to a next implementation state to be achieved by the entity with respect to the cybersecurity framework security controls is also enabled. The cost-benefit analysis leverages factored weights including aspects indicative of security perspectives, Gaussian distributions, and the maturity indicator levels.
Regulatory agencies are considering alternative approaches to assessing inhalation toxicity that utilizes in vitro studies with human cells and in silico modeling in lieu of additional animal studies. In support of this goal, computational fluid-particle dynamics models were developed to estimate site-specific deposition of inhaled aerosols containing the fungicide, chlorothalonil, in the rat and human for comparisons to prior rat inhalation studies and new human in vitro studies. Under bioassay conditions, the deposition was predicted to be greatest at the front of the rat nose followed by the anterior transitional epithelium and larynx corresponding to regions most sensitive to local contact irritation and cytotoxicity. For humans, simulations of aerosol deposition covering potential occupational or residential exposures (1–50 µm diameter) were conducted using nasal and oral breathing. Aerosols in the 1–5 µm range readily penetrated the deep region of the human lung following both oral and nasal breathing. Under actual use conditions (aerosol formulations >10 µm), the majority of deposited doses were in the upper conducting airways. Beyond the nose or mouth, the greatest deposition in the pharynx, larynx, trachea, and bronchi was predicted for aerosols in the 10–20 µm size range. Only small amounts of aerosols >20 µm penetrated past the pharyngeal region. Using the ICRP clearance model, local retained tissue dose metrics including maximal concentrations and areas under the curve were calculated for each airway region following repeated occupational exposures. These results are directly comparable with benchmark doses from in vitro toxicity studies in human cells leading to estimated human equivalent concentrations that reduce the reliance on animals for risk assessments.
Human and ecological risk assessments utilize concentration ratios and transfer factors to calculate contaminant concentrations in plant and animal tissues in equilibrium with soil or water (concentration ratios) or based on an animal's contaminant intake from various media (transfer factors). These ratios and factors are used to evaluate human dose via food ingestion pathways and to evaluate ecological dose to an organism. In a probabilistic performance assessment, concentration ratio and transfer factor distributions are developed for every element, and these distributions are applied across the spatial and temporal domain of the assessment. As a result, it is necessary to scale the underlying data distributions by applying the central limit theorem to develop appropriate distributions of the mean for the temporal and spatial scale of the model. Herein we discuss the methodologies for identifying and ranking references, and how to develop statistical distributions of concentration ratios and transfer factors for probabilistic performance assessment models. We also discuss factors that could be pertinent to differences when selecting references and developing distributions for human versus ecological risk assessment scenarios. (authors)
Human reliability analysis (HRA) is a method for evaluating human errors in a variety of complex systems such as nuclear power plants, military systems, aircraft, and chemical plants. Most HRA methods currently used by regulatory institutes or utilities are called static HRA and are carried out by simple worksheets or simple calculators. To date, there are many unsolved or intrinsic challenges in static HRA. For example, existing static HRA does not realistically model and evaluate human actions as they would be performed at actual systems. There is no method with HRA to objectively estimate the time required for human actions despite being essential to HRA processes. In addition, many HRA methods still rely on a dataset generated prior to the 1980s, from unrelated industry experience or simply from expert judgment. Accordingly, this study attempted to research how to overcome the challenges of existing HRA via dynamic risk assessment (a.k.a., simulation-based or computation-based risk assessment) techniques. First, this study developed a dynamic HRA method, named as PRocedure-based Investigation Method of EMRALD Risk Assessment – HRA (PRIMERA-HRA). The PRIMERA-HRA mainly concentrates on providing HRA analysts with specific guidelines on how to reasonably model human actions, assign human reliability data and evaluate output of simulation within a dynamic probabilistic risk assessment tool, called as Event Modeling Risk Assessment using Linked Diagrams (EMRALD). Second, this study also developed a module for performance shaping factors (i.e., the key concept in HRA quantification) applicable to dynamic HRA, then implemented it based on PRIMERA-HRA within the EMRALD tool. Third, this study developed an HRA data collection framework to support dynamic HRA, called as Simplified Human Error Experimental Program (SHEEP). Originally, the SHEEP study aimed to support static HRA and its data collection, but recently extended the scope to the new technologies such as dynamic HRA or HRA for advanced reactors. SHEEP focuses on the use of data collected from simplified simulators to complement—but not replace—data collection studies using full-scope simulators and actual operators. To date, many experiments were conducted under the SHEEP framework. Multiple analyses, such as human performance analysis, human error analysis, task complexity analysis, learning effect analysis and time distribution analysis, were also carried out using the collected data. Then, based on the major insights, an approach to inferring full-scope data based on simplified simulator data was proposed. The PRIMERA-HRA and SHEEP research are expected to evaluate human actions more realistically than existing static HRA, provide an opportunity to collect more HRA data with reasonable cost and labor, then contribute to enhance the quality of HRA.
Human reliability analysis (HRA) is a method for evaluating human errors in a variety of complex systems such as nuclear power plants, military systems, aircraft, and chemical plants. Most HRA methods currently used by regulatory institutes or utilities are called static HRA and are carried out by simple worksheets or simple calculators. To date, there are many unsolved or intrinsic challenges in static HRA. For example, existing static HRA does not realistically model and evaluate human actions as they would be performed at actual systems. There is no method with HRA to objectively estimate the time required for human actions despite being essential to HRA processes. In addition, many HRA methods still rely on a dataset generated prior to the 1980s, from unrelated industry experience or simply from expert judgment. Accordingly, this study attempted to research how to overcome the challenges of existing HRA via dynamic risk assessment (a.k.a., simulation-based or computation-based risk assessment) techniques. First, this study developed a dynamic HRA method, named as PRocedure-based Investigation Method of EMRALD Risk Assessment – HRA (PRIMERA-HRA). The PRIMERA-HRA mainly concentrates on providing HRA analysts with specific guidelines on how to reasonably model human actions, assign human reliability data and evaluate output of simulation within a dynamic probabilistic risk assessment tool, called as Event Modeling Risk Assessment using Linked Diagrams (EMRALD). Second, this study also developed a module for performance shaping factors (i.e., the key concept in HRA quantification) applicable to dynamic HRA, then implemented it based on PRIMERA-HRA within the EMRALD tool. Third, this study developed an HRA data collection framework to support dynamic HRA, called as Simplified Human Error Experimental Program (SHEEP). Originally, the SHEEP study aimed to support static HRA and its data collection, but recently extended the scope to the new technologies such as dynamic HRA or HRA for advanced reactors. SHEEP focuses on the use of data collected from simplified simulators to complement—but not replace—data collection studies using full-scope simulators and actual operators. To date, many experiments were conducted under the SHEEP framework. Multiple analyses, such as human performance analysis, human error analysis, task complexity analysis, learning effect analysis and time distribution analysis, were also carried out using the collected data. Then, based on the major insights, an approach to inferring full-scope data based on simplified simulator data was proposed. The PRIMERA-HRA and SHEEP research are expected to evaluate human actions more realistically than existing static HRA, provide an opportunity to collect more HRA data with reasonable cost and labor, then contribute to enhance the quality of HRA.
The Alternative Fuels Risk Assessment Models (A1tRAM) toolkit combines Quantitative Risk Assessment (QRA) with simulations of unignited dispersion, ignited turbulent diffusion flames, and indoor accumulation with delayed ignition of fuels. The models of the physical phenomena need to be validated for each of the fuels in the toolkit. This report shows the validation for methane which is being used as a surrogate for natural gas. For the unignited dispersion model, seven previously published experiments from credible sources were used to validate. The validation looked at gas concentrations with respect to the distance from the release point. Four of these were underexpanded jets (i.e. release velocity equal to or greater than local speed of sound) and the other three subsonic releases. The methane plume model in AltRAM matched both varieties well, with higher accuracy for the underexpanded releases. For the jet flame model, we compared the heat flux and thermal radiation data reported from five separate turbulent jet flame experiments to the quantities calculated by A1tRAM. Four of the five datasets were for underexpanded diffusion jets flames. While the results still match well enough to give a good estimate of what is occurring, the error is higher than what was seen with the plume model. For the underexpanded flames A1tRAM provided reasonable approximations, which would lead to conservative risk assessments. Some modeling errors can be attributed to environmental effects (i.e. wind) since most large scale flame experiments are conducted outdoors. A1tRAM has been shown to be a reasonably accurate tool for calculating the concentration or flame properties of natural gas releases. Improvements could still be made for the plume of subsonic releases and radiative heat fluxes to reduce the conservative nature of these predictions. These models can provide valuable information for the risk assessment of natural gas infrastructure.
The SeaRAY is a deployable power system for maritime sensors, monitoring equipment, communications, unmanned underwater vehicles, and other similar payloads. This project is to design, deliver, and test a prototype low-power WEC that lowers the total cost of ownership and provides robust, new capabilities for customers in the maritime environment. Failure Modes, Effects, and Criticality Analysis (FMECA) is conducted to systematically identify all potential failure modes and their effects on the system, and to analyze the criticality of each risk based on the likelihood of the event and the severity of the impact. Actions may then be recommended to mitigate the criticality of a risk, either by reducing the likelihood of the risk or the severity of its impact. Risk assessment is executed iteratively as an integral part of the design process. By incorporating risk assessment early in the development cycle, mitigation of risk can be achieved cost effectively. The actions recommended to mitigate risk may be subsequently executed, and as the design progresses the risk assessment is reviewed and revised. Review of the risk assessment is integrated into structured design reviews, ensuring that critical risks are comprehended and that the Project will not progress to e.g. fabrication while intolerable risks remain. The risk assessment process results in the population and maintenance of Risk Registers (RRs). Each major system (and as needed, subsystem) will have a distinct RR. This allows each system or subsystem to be assessed individually, rendering the RRs to a manageable size for review.
This is a presentation to be made at the University of Tokyo. The Idaho National Laboratory is creating the next-generation reliability- and risk-assessment methods and tools that support risk-informed decision-making by combining physics-based models with probabilistic quantification approaches. Integrating these two worlds of physics and probability using a simulation framework leads us to predictions based upon an approach called “computational risk assessment” which will serve as the technical basis for the future of reliability and risk approaches. The driving factors for this new approach includes: temporal (timing issues), spatial (location issues), mechanistic (physics issues), and topological (complexity issues). By combining phenomenology directly with stochastic quantification, we are able to perform advanced uncertainty analysis directly on both parameters and models. In addition, new tools allow for the creation of reduced order models that mimic high-fidelity engineering models while still permitting the realization of thousands of simulation iterations in short run-times on current workstation-class computers. While these advanced methods and tools can provide increased realism in our engineering safety and risk approaches, their greater benefit is to provide a risk-informed engineering framework for design and operation. This talk will briefly review the history of risk assessment and risk-informed applications, describe some of the current research and development found at the Idaho National Laboratory, and discuss potential future applications and approaches for advanced methods and tools.