Addressing Cyber Hazards in Nuclear Power Plants with Systems Theoretic-Informed Fault Trees.
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This report documents the scoping study of developing generic simplified fuel damage risk models for quantitative analysis from inadvertent reactivity insertion events during shutdown (SD) in light water pressurized and boiling water reactors. In the past, nuclear fuel reactivity accidents have been analyzed both mainly deterministically and probabilistically for at-power and SD operations of nuclear power plants (NPPs). Since then, many NPPs had power up-rates and longer refueling intervals, which resulted in fuel configurations that may potentially respond differently (in an undesirable way) to reactivity accidents. Also, as shown in a recent event, several inadvertent operator actions caused potential nuclear fuel reactivity insertion accident during SD operations. The set inadvertent operator actions are likely to be plant- and operation-state specific and could lead to accident sequences. This study is an outcome of the concern which arose after the inadvertent withdrawal of control rods at Dresden Unit 3 in 2008 due to operator actions in the plant inadvertently three control rods were withdrawn from the reactor without knowledge of the main control room operator. The purpose of this Standardized Plant Analysis Risk (SPAR) Model development project is to develop simplified SPAR Models that can be used by staff analysts to perform risk analyses of operating events and/or conditions occurring during SD operation. These types of accident scenarios are dominated by the operator actions, (e.g., misalignment of valves, failure to follow procedures and errors of commissions). Human error probabilities specific to this model were assessed using the methodology developed for SPAR model human error evaluations. The event trees, fault trees, basic event data and data sources for the model are provided in the report. The end state is defined as the reactor becomes critical. The scoping study includes a brief literature search/review of historical events, developments of a small set of comprehensive event trees and fault trees and recommendation for future work.
The requirement for ultradependability of computer systems in future avionics and space applications necessitates a top-down, integrated systems engineering approach for design, implementation, testing, and operation. The functional analyses of hardware and software systems must be combined by models that are flexible enough to represent their interactions and behavior. The information contained in these models must be accessible throughout all phases of the system life cycle in order to maintain consistency and accuracy in design and operational decisions. One approach being taken by researchers at Ames Research Center is the creation of an object-oriented environment that integrates information about system components required in the reliability evaluation with behavioral information useful for diagnostic algorithms. Procedures have been developed at Ames that perform reliability evaluations during design and failure diagnoses during system operation. These procedures utilize information from a central source, structured as object-oriented fault trees. Fault trees were selected because they are a flexible model widely used in aerospace applications and because they give a concise, structured representation of system behavior. The utility of this integrated environment for aerospace applications in light of our experiences during its development and use is described. The techniques for reliability evaluation and failure diagnosis are discussed, and current extensions of the environment and areas requiring further development are summarized.
As IMRT treatment processes advance and mature, so must the quality assurance processes being used to validate their delivery. In some respects, treatment delivery advancements (e.g. VMAT) have out-paced QA advancements. The purpose of this session is to describe new processes that are being implemented to bring IMRT QA up-to-date with the treatment delivery advances. It would explore emerging IMRT QA paradigms, including requirements-based IMRT QA which necessitates definition of delivery errors (e.g. patient dose error, leaf positioning error) and development of processes to ensure reliable error detection. Engineeringbased QA approaches, including use of IMRT treatment delivery process trees, fault tree analysis and failure modes effects analysis would be described. Approaches to detect errors such as (1) during treatment delivery validation using exit fluence detectors (e.g. EPIDs); (2) analysis of treatment delivery via use of machine parameter log files; (3) dose recalculation using (3a) treatment planning system; (3b) record-and-verify; or (3c) entrance and exit fluence measurement parameters would be explained. The relative advantages and disadvantages of each method would be discussed. Schemes for error classification and root cause analysis would be described – steps which are essential for future error prevention. For each QA method, testing procedures and results would be presented indicating the types of errors that can be detected, those that cannot be detected, and the reliability of the error detection method (for example determined via ROC analysis). For speakers, we are seeking to engage non-commercially biased experts. Those listed below are a sub-sample of possible qualified individuals.
Seismic isolation is a mature technology that has been implemented in many civil structures, including buildings, bridges, liquid natural gas tanks, and off shore oil platforms, both in the United States and other countries, to mitigate the damaging effects of earthquakes. Seismic isolation has also been used in nuclear structures in France and South Africa, but not yet in the United States: neither in the Department of Energy facilities nor in commercial nuclear power plants (NPPs). This is primarily due to a lack of guidelines, codes and standards for the analysis, design and construction specific to seismically isolated nuclear structures. However, seismic isolation of nuclear structures has seen increased research interest in the recent years and the recently published national consensus standard, America Society of Civil Engineers (ASCE) Standard 4-16 'Seismic analysis of safety related nuclear structures', now incorporates language and commentary (Chapter 12) for seismically isolating surface or near-surface-mounted nuclear facilities, including NPPs. Seismic isolation substantially reduces horizontal seismic loads (demands) on structures, systems, and components. Reduction in demand results in four potential benefits: (1) economic: reduction in capital cost, (2) increased safety: reduction in the mean annual frequency of unacceptable performance, (3) insurance: protection against increases in the known seismic hazard after construction by minimizing the effort to re-qualify and re-certify structures, systems and components, and (4) recertification: the opportunity to certify an existing NPP design for a region of higher seismic hazard. Item (2) above, an increase in safety (reduction of seismic risk), was explored in Huang et al., wherein it was demonstrated that the implementation of seismic isolation reduced seismic risk in nuclear power plant structures. Studies that assess item (1) have not been performed although the use of isolation may lead to large reductions in the capital cost of safety-related nuclear facilities. Funding provided by Nuclear Safety Research and Development in the Department of Energy (DOE) allowed the authors to develop a framework for assessing the economic benefits and reductions in seismic risk afforded by the use of seismic (base) isolation. The framework includes probabilistic risk assessment and estimation of overnight capital costs (OCC's) of a sample generic DOE nuclear facility (GDNF). The project is documented in detail in Bolisetti et al. and Yu et al., and key results are presented here. The GDNF considered in this study is based on a facility constructed at the Idaho National Laboratory. The GDNF is a two-story reinforced concrete building with an embedded basemat and is founded on soft rock at the boundary of Site Class B and Site Class C (B/C boundary) as characterized by ASCE/SEI Standard 7. The GDNF is assumed to handle radiological materials, and its structure, systems and components (SSC's) are designed to effectively confine these materials in the event of an internal failure. The GDNF is populated with components that are generic to safety-related nuclear structures. A single system, which is intended to confine a loss of materials-at-risk (MAR) to the interior of the structure, is chosen for this study. Accordingly, a hypothetical event tree and fault tree are assumed for risk assessment. The isolation system for the GDNF is designed per Chapter 12 of ASCE/SEI 4-16 but is not optimized. The isolation system consists of 38 lead rubber bearings installed in one horizontal plane below the basemat. Details of the GDNF, including the SSC's, event tree and fault tree, and isolation system, are presented in the project report and Yu et al.. This study includes seismic risk assessment and cost estimation for four cases: (1) the GDNF is located at a site of low to moderate seismic hazard and is constructed on a conventional (non-isolated) foundation; (2) the GDNF is located at the same site as case 1, but is seismically isolated; (3) the GDNF is located at a site of high seismic hazard and is constructed on a conventional foundation; and (4) the GDNF is located at the same site of high seismic hazard as case 3, but is seismically isolated. In this study, the DOE site at Idaho National Laboratory (INL) is chosen as the low to moderate seismic hazard site for cases 1 and 2, and the Los Alamos National Laboratory (LANL) is chosen as the high seismic hazard site for cases 3 and 4.
Implementation of risk-informed design allows the design team to thoroughly explore the risks of a system while iterating the operations concept, design, and requirements until the system meets mission objects and is achievable within constraints. To arrive at a space system design that is likely to meet all constraints placed upon mass, cost, performance and risk, the system requirements must be understood and traded against each other as early as the conceptual design phase. Depending on the project phase and the goals of the risk analysis, various PRA methodologies could be used to produce quantitative risk estimates to enable such a process. In order to better understand the applicability, advantages, and limitations of various PRA methodologies, a comparative analysis of three bottom-up, component-based PRA approaches was performed. The three methods examined are a traditional static fault tree, a fault tree hybrid, and a dynamic Monte Carlo simulation. Each approach was used to assess a generic reaction control system (RCS) thruster pod and mission. The methods are assessed in terms of the process of modeling a system, the actionable information produced for the design team, and the overall fidelity of the quantitative risk evaluation generated. The paper also discusses the applicability of each methodology to the different phases of system development.
Through continued advancement in computational resources, development that was previously done by trial and error production is now performed through computer simulation. These virtual physical representations have the potential to provide accurate and valid modeling results and are being used in many different technical fields. Risk assessment now has the opportunity to use 3D simulation to improve analysis results and insights, especially for external event analysis. By using simulations, the modeler only has to determine the likelihood of an event without having to also predict the results of that event. The 3D simulation automatically determines not only the outcome of the event, but when those failures occur. How can we effectively incorporate 3D simulation into traditional PRA? Most PRA plant modeling is made up of components with different failure modes, probabilities, and rates. Typically, these components are grouped into various systems and then are modeled together (in different combinations) as a “system” with logic structures to form fault trees. Applicable fault trees are combined through scenarios, typically represented by event tree models. Though this method gives us failure results for a given model, it has limitations when it comes to time-based dependencies or dependencies that are coupled to physical processes which may themselves be space- or time-dependent. Since, failures from a 3D simulation are naturally time related, they should be used in that manner. In our simulation approach, traditional static models are converted into an equivalent state diagram representation with start states, probabilistic driven movements between states and terminal states. As the state model is run repeatedly, it converges to the same results as the PRA model in cases where time-related factors are not important. In cases where timing considerations are important (e.g., when events are dependent upon each other), then the simulation approach will typically provide superior results and insights. We also couple the state model with the dynamic 3D simulation analysis representing events (such as flooding) to determine which (if any) components fail. Not only does the simulation take into account any failed items from the state model, but any failures caused by the simulation are incorporated back into the state model and factored into the overall results. Using this method we incorporate accurate 3D simulation results, eliminate static-based PRA issues, and have time ordered failure information.
Probabilistic risk assessment (PRA) is being used increasingly by the nuclear industry for safety during normal operations as well as for the protection against external hazards. Computation of total risk in an external hazard PRA is dependent on hazard assessment, fragility assessment, and systems analysis. A systems analysis for propagation of component fragilities is conducted using event and fault trees. The event and fault trees for an actual power plant can be fairly large in size, which imposes computational challenges. Hence, certain assumptions are employed for computational efficiency. These assumptions typically represent the conditions imposed during the design basis (DB) scenario. The traditional PRA tools based on these assumptions are also widely applied to perform risk assessment in the context of beyond design basis (BDB) scenarios. However, some of these assumptions may not be valid for certain BDB scenarios. In addition, the probability of dependent failures also increases in BDB scenarios due to common cause failures (CCF) which usually results from design modifications, human errors, etc. In this manuscript, a simple and a relatively more complex illustrative examples are used to show the limitation of these assumptions in the numerical quantification of risk for the case of BDB conditions. Case studies with CCF events across multiple fault trees are also presented to illustrate the effect of these assumptions when traditional approach is used in BDB risk assessment. It is shown that the assumptions are valid for the case of DB conditions but may lead to excessively conservative risk estimates in the case of BDB conditions. Finally, a Bayesian network based top-down algorithm is proposed as an alternative tool for accurate numerical quantification of total risk in systems analysis.
A top-down risk assessment in the early phases of space exploration architecture development can provide understanding and intuition of the potential risks associated with new designs and technologies. In this approach, risk analysts draw from their past experience and the heritage of similar existing systems as a source for reliability data. This top-down approach captures the complex interactions of the risk driving parts of the integrated system without requiring detailed knowledge of the parts themselves, which is often unavailable in the early design stages. Traditional probabilistic risk analysis (PRA) technologies, however, suffer several drawbacks that limit their timely application to complex technology development programs. The most restrictive of these is a dependence on static planning scenarios, expressed through fault and event trees. Fault trees incorporating comprehensive mission scenarios are routinely constructed for complex space systems, and several commercial software products are available for evaluating fault statistics. These static representations cannot capture the dynamic behavior of system failures without substantial modification of the initial tree. Consequently, the development of dynamic models using fault tree analysis has been an active area of research in recent years. This paper discusses the implementation and demonstration of dynamic, modular scenario modeling for integration of subsystem fault evaluation modules using the Space Architecture Failure Evaluation (SAFE) tool. SAFE is a C++ code that was originally developed to support NASA s Space Launch Initiative. It provides a flexible framework for system architecture definition and trade studies. SAFE supports extensible modeling of dynamic, time-dependent risk drivers of the system and functions at the level of fidelity for which design and failure data exists. The approach is scalable, allowing inclusion of additional information as detailed data becomes available. The tool performs a Monte Carlo analysis to provide statistical estimates. Example results of an architecture system reliability study are summarized for an exploration system concept using heritage data from liquid-fueled expendable Saturn V/Apollo launch vehicles.
Nuclear power plants are increasingly adding digital components for plant operation, safety, and security. These digital components fill a gap with legacy equipment where replacement components no longer exist. They also benefit operation of the plant by increasing efficiency in power generation, monitoring of equipment and plant parameters, as well as aiding operator control. However, the addition of digital components and systems also adds cyber risks with previously unanalyzed failure modes and attack vectors are introduced with these new systems. These risks are difficult to identify, analyze, and mitigate due to the increasingly complex nature of the digital components and the integration of these components with additional plant processes and communication networks. The research presented in this paper develops a new method that addresses the cyber risk to inform appropriate levels of protection. EPRI and Sandia are working under a Cooperative Research and Development Agreement to develop an effective method of evaluating the cyber risk in production nuclear power facilities. The Cyber Hazards Analysis Risk Methodology (CHARM) focuses on ensuring adequate controls are in place for appropriate cyber protection of the plant from radiological release or generation risk. Existing plant hazards analyses (e.g., PRA, FTA) do not account for software deficiencies or adversarial intent. This method leverages existing plant analyses and MIT's Systems Theoretic Process Analysis (STPA) to create cyber informed fault trees. These new fault trees will provide the basis for comprehensive cyber risk analysis and help ensure potential gaps in cyber security controls are identified and corrected. (authors)
Analysis of the material protection, control, and accountability (MPC&A) system is necessary to understand the limits and vulnerabilities of the system to internal threats. A self-appraisal helps the facility be prepared to respond to internal threats and reduce the risk of theft or diversion of nuclear material. The material control and accountability (MC&A) system effectiveness tool (MSET) fault tree was developed to depict the failure of the MPC&A system as a result of poor practices and random failures in the MC&A system. It can also be employed as a basis for assessing deliberate threats against a facility. MSET uses fault tree analysis, which is a top-down approach to examining system failure. The analysis starts with identifying a potential undesirable event called a 'top event' and then determining the ways it can occur (e.g., 'Fail To Maintain Nuclear Materials Under The Purview Of The MC&A System'). The analysis proceeds by determining how the top event can be caused by individual or combined lower level faults or failures. These faults, which are the causes of the top event, are 'connected' through logic gates. The MSET model uses AND-gates and OR-gates and propagates the effect of event failure using Boolean algebra. To enable the fault tree analysis calculations, the basic events in the fault tree are populated with probability risk values derived by conversion of questionnaire data to numeric values. The basic events are treated as independent variables. This assumption affects the Boolean algebraic calculations used to calculate results. All the necessary calculations are built into the fault tree codes, but it is often useful to estimate the probabilities manually as a check on code functioning. The probability of failure of a given basic event is the probability that the basic event primary question fails to meet the performance metric for that question. The failure probability is related to how well the facility performs the task identified in that basic event over time (not just one performance or exercise). Fault tree calculations provide a failure probability for the top event in the fault tree. The basic fault tree calculations establish a baseline relative risk value for the system. This probability depicts relative risk, not absolute risk. Subsequent calculations are made to evaluate the change in relative risk that would occur if system performance is improved or degraded. During the development effort of MSET, the fault tree analysis program used was SAPHIRE. SAPHIRE is an acronym for 'Systems Analysis Programs for Hands-on Integrated Reliability Evaluations.' Version 1 of the SAPHIRE code was sponsored by the Nuclear Regulatory Commission in 1987 as an innovative way to draw, edit, and analyze graphical fault trees primarily for safe operation of nuclear power reactors. When the fault tree calculations are performed, the fault tree analysis program will produce several reports that can be used to analyze the MPC&A system. SAPHIRE produces reports showing risk importance factors for all basic events in the operational MC&A system. The risk importance information is used to examine the potential impacts when performance of certain basic events increases or decreases. The initial results produced by the SAPHIRE program are considered relative risk values. None of the results can be interpreted as absolute risk values since the basic event probability values represent estimates of risk associated with the performance of MPC&A tasks throughout the material balance area (MBA). The RRR for a basic event represents the decrease in total system risk that would result from improvement of that one event to a perfect performance level. Improvement of the basic event with the greatest RRR value produces a greater decrease in total system risk than improvement of any other basic event. Basic events with the greatest potential for system risk reduction are assigned performance improvement values, and new fault tree calculations show the improvement in total system risk. The operational impact or cost-effectiveness from implementing the performance improvements can then be evaluated. The improvements being evaluated can be system performance improvements, or they can be potential, or actual, upgrades to the system. The RIR for a basic event represents the increase in total system risk that would result from failure of that one event. Failure of the basic event with the greatest RIR value produces a greater increase in total system risk than failure of any other basic event. Basic events with the greatest potential for system risk increase are assigned failure performance values, and new fault tree calculations show the increase in total system risk. This evaluation shows the importance of preventing performance degradation of the basic events. SAPHIRE identifies combinations of basic events where concurrent failure of the events results in failure of the top event.
Course Objectives: Build PRA modeling and analysis skills – Event tree and fault tree model development – Dependent failures and common cause modeling – Component failure mechanisms, Improve understanding of quantification process, Improve ability to extract key results from a PRA, Greater familiarity with PRA goals and process, and Aleatory (stochastic) versus Epistemic (state of knowledge) uncertainty
A supervisory control system is being developed for multiunit advanced small modular reactors to minimize human interventions during normal and abnormal operations. In the supervisory control system, control action decisions are made based on a probabilistic risk assessment that employs event trees and fault trees. Although traditional probabilistic risk assessment tools are implemented, their scope is extended to normal operations, and the application is reversed to assess the success of non-safety related systems and to enable continued operation of the plant. This extended probabilistic risk assessment approach is called operational performance risk assessment (OPRA). OPRA helps to identify available paths, combine control actions for maintaining plant conditions within operational limits, and to quantify the likelihood of success of these operational trajectories to optimize the selection of alternative actions without activating reactor protection system. In this paper, a case study of OPRA in a supervisory control system is demonstrated for the Advanced Liquid Metal Reactor (ALMR) Power Reactor Inherently Safe Module (PRISM) design, specifically the power conversion system. The scenario investigated involved a condition in which the feedwater control valve that was observed to be drifting to the closed position. Alternative plant configurations that would allow the plant to continue to operate at full or reduced power were identified using OPRA. Dynamic analyses were performed with a thermal-hydraulic model of the ALMR PRISM system using Modelica to evaluate the magnitude of safety margins. Successful recovery paths for the selected scenario were identified and quantified using the supervisory control system. (authors)
An approach to reconfiguration in tree architectures has been developed in which redundant processors are allocated at the leaves. The scheme is called sub-tree oriented fault tolerances (SOFT) and is capable of tolerating both link failures as well as multiple processor failures. In this paper, the SOFT scheme is examined from the perspective of reconfigurability. Specific algorithms are presented for reconfiguration.
Fault discovery in the complex systems consist of model based reasoning, fault tree analysis, rule based inference methods, and other approaches. Model based reasoning builds models for the systems either by mathematic formulations or by experiment model. Fault Tree Analysis shows the possible causes of a system malfunction by enumerating the suspect components and their respective failure modes that may have induced the problem. The rule based inference build the model based on the expert knowledge. Those models and methods have one thing in common; they have presumed some prior-conditions. Complex systems often use fault trees to analyze the faults. Fault diagnosis, when error occurs, is performed by engineers and analysts performing extensive examination of all data gathered during the mission. International Space Station (ISS) control center operates on the data feedback from the system and decisions are made based on threshold values by using fault trees. Since those decision-making tasks are safety critical and must be done promptly, the engineers who manually analyze the data are facing time challenge. To automate this process, this paper present an approach that uses decision trees to discover fault from data in real-time and capture the contents of fault trees as the initial state of the trees.
This study examines three different methodologies for producing loss-of-mission (LOM) and loss-of-crew (LOC) risks estimates for probabilistic risk assessments (PRA) of crewed spacecraft. The three bottom-up, component-based PRA approaches examined are a traditional static fault tree, a dynamic Monte Carlo simulation, and a fault tree hybrid that incorporates some dynamic elements. These approaches were used to model the reaction control system thruster pod of a generic crewed spacecraft and mission, and a comparative analysis of the methods is presented. The methodologies are assessed in terms of the process of modeling a system, the actionable information produced for the design team, and the overall fidelity of the quantitative risk evaluation generated. The system modeling process is compared in terms of the effort required to generate the initial model, update the model in response to design changes, and support mass-versus-risk trade studies. The results are compared by examining the top-level LOM/LOC estimates and the relative risk driver rankings at the failure mode level. The fidelity of each modeling methodology is discussed in terms of its capability to handle real-world system dynamics such as cold-sparing, changes in mission operations due to loss of redundancy, and common cause failure modes. The paper also discusses the applicability of each methodology to different phases of system development and shows that a single methodology may not be suitable for all of the many purposes of a spacecraft PRA. The fault tree hybrid approach is shown to be best suited to the needs of early assessments during conceptual design phases. As the design begins to mature, the level of detail represented in the risk model must go beyond redundancy and nominal mission operations to include dynamic, time- and state-dependent system responses as well as diverse system capabilities. This is best accomplished using the dynamic simulation approach, since these phenomena are not easily captured by static methods. Ultimately, once the design has been finalized and the goal of the PRA is to provide design validation and requirement verification, more traditional, static fault tree approaches may become as appropriate as the simulation method.
1.1. Overview of the Advanced SAPHIRE Material The Advanced SAPHIRE course material is intended to both (1) provide guidance for learning advanced SAPHIRE features during the class and (2) become a stand-alone reference document after finishing the class. Thus, the format for the class material is a combination of the traditional “overhead-type” of presentation information with a structured, reference-type document. Major topics that are covered in the Advanced SAPHIRE class include: Advanced data base concepts such as Model Type (e.g., random, seismic, fire, flood). Definition of house events and how they are used on a sequence-by-sequence basis to manipulate individual fault tree logic using sequence flag sets or fault tree flag sets. ? Basic event templates, compound basic event equation editors, and human error worksheets. A rule-based event tree top event substitution feature (called the Link Event Tree Rule Editor) which allows for top event substitutions. A rule-based cut set post-processing feature (called the Recovery Rules) which allows cut set manipulation. Cut set analysis based upon rule-based end state categories (called End State Analysis). Cut set generation options for both fault trees and event tree sequences. The large event tree methodology and how SAPHIRE can be used to generate sequence cut sets for this method. The transfer of data between SAPHIRE data bases and between other PRA codes (MAR-D).
Object-oriented fault-tree representation unifies evaluation of reliability and diagnosis of faults. Programming/fault tree described more fully in "Object-Oriented Algorithm For Evaluation Of Fault Trees" (ARC-12731). Augmented fault tree object contains more information than fault tree object used in quantitative analysis of reliability. Additional information needed to diagnose faults in system represented by fault tree.