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At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Identification of the accident-related critical digital assets based on probabilistic safety analysis results - 225

Digital I and C systems have been developed and installed in nuclear power plants, and due to installation of the digital I and C systems, cyber security concerns are increasing in nuclear industry. However, there are too many critical digital assets (CDAs) to be inspected in digitalized NPPs. In order to reduce the inefficiency of regulation in nuclear facilities, the critical digital assets that are directly related to an accident are elicited by using the probabilistic safety assessment results. Target initial events are selected, and their heading are analyzed through event tree analysis. Among the headings, the headings that can be proceeded directly to the core damage by the cyber-attack are finally selected as the target of deriving the minimum cut-sets. Based on success criteria of each heading, we analyze the fault trees and derive the minimum set-cuts. In terms of original PSA, the value of probability for the cut-sets is important but the probability is not important in terms of cyber security of NPPs. The important factors is the number of basic events consisting of the minimal cut-sets that is proportional to vulnerability. Finally, the process for identifying accident-related CDAs is suggested based on PSA results. The results of this study are expected to be used to derive the linkage between cyber-attack and accident, and to develop the final core digital asset identification methodology and effective regulatory method. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2017

Development of cyber security test scenario for non-safety display system - 226

In order to test the cyber security of NPP related to safety, the results of probabilistic safety assessment were used. The fault tree (FT) is used to reflect the impact of the cyber-attack on the operator. The cyber security test should be performed focusing on the scenario derived from the FT. The concept of a security case has been introduced. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2017

Operational performance risk assessment in support of a supervisory control system - 251

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)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2017

Nuclear power plant risk-informed surveillance frequency control program implementation at the Duke Catawba nuclear plant with a focus on instrumentation and control systems - 333

This paper summarizes an updated process for risk-informed surveillance frequency control program (SFCP) implementation at the Catawba Nuclear Power Plant in the past year. This program includes selection and prioritization of specific target surveillance test interval extensions; and development, review, and implementation of surveillance test risk informed documented evaluation (STRIDE) packages designed to support extension of conventional surveillance requirement test intervals, in accordance with 'Risk-Informed Technical Specifications Initiative 5b, Risk-Informed Method for Control of Surveillance Frequencies, Industry Guideline,' NEI 04-10, Revision 1 . The scope of work associated with STRIDE development includes probabilistic risk assessment (PRA) case studies, deterministic assessment (DA) evaluations, and, where required, instrument drift evaluation (IDE). The STRIDE implementation efforts have also included support of independent decision-making panel (IDP) meetings at the implementing power stations and IDP member training. The purpose of this paper is to provide a presentation of a refined process for STRIDE development with a focus on instrumentation and control systems based on author experience, which includes support for the development of multiple STRIDE packages for the Catawba Nuclear Plant. This paper outlines a framework for practical implementation of an SFCP within the context of an integrated risk-informed performance-based regulation application program with emphasis on instrumentation and control systems. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2017

Identification of NPP transients using artificial intelligence - 335

If the accidents happen in nuclear power plants (NPPs), the NPP operators identify the transients through observing the behaviors of the major parameters. However, the integrity of the instrumentation signals is not ensured under the accident circumstances and the major parameters essential in identifying the transients can rapidly change although the instruments generate accurate signals. Thus, to successfully control the accidents by taking necessary measures relatively faster, it is very important to provide the operators with the information that can identify the transients and accidents accurately. In this study, the transients of severe accidents in NPPs are identified and classified using artificial intelligence methodologies. Several transients are identified using support vector classification (SVC) and probabilistic neural network (PNN) as artificial intelligence methodologies. The used data for the transients are the simulation data using the modular accident analysis program (MAAP) code for the optimized power reactors (OPR1000s). The time-integrated values of chosen simulated sensor signals are used as the input variables for identifying the transients. As a result of this study, the proposed models identified the transients quite precisely. Therefore, the quite accurate identification of the transients indicates the excellence of these artificial intelligence methods and they will produce useful supporting information for the operators even under extreme transient circumstances in NPPs. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2016

Qualitative Evaluation of Human Reliability Analysis Quality

On August, 2001, the Korean government issued a severe accident policy statement. In this policy statement, a safety goal was proposed in a form of quantitative health objective(QHO) such that an additive risk of early fatality and cancer fatality caused by accident or operation of nuclear power plant(NPPs) should not exceed 0.1% of the sum of other accidents and cancer fatality risks resulting from all other causes, respectively. In accordance with the policy statement, probabilistic safety analysis(PSA) for all Korean NPPs was conducted to show each plant satisfies QHO. Given the increasing importance of PSA to assess the level of safety of NPPs, it is crucial that the quality of the PSA results should be assured. Compared to well-established PSA method used for domestic NPPs, there is consensus that human reliability analysis (HRA) has lack of consistency due to the different assumptions and different characteristics of the methods, which could possibly result in low quality of the PSA. To resolve the issues with lack of consistency of the HRA methods, there have been efforts to develop a standardized HRA method in Korea. However, quality of existing Korean HRA should be evaluated before developing a standardized method, since the results can be used for deriving requirements and give feedback and insights for developing the standardized method. In this study, literature review was performed to select an adequate method for evaluating the quality of the HRA, and the quality of the HRA is evaluated. Finally, results and insights of this study are shown in the conclusion. (authors)

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2016

Development of Real-Time Quantitative Safety Evaluation Method for Nuclear Power Plants

Human operators involved with large process control systems such as nuclear power plants (NPPs) perform significant roles in the operation of systems. In this reason, deficiencies in human factors such as poor HSI (Human System Interface) design, procedure, and training, are significant contributing factors to NPPs incidents and accidents. In this regard, many kinds of studies including human reliability analysis (HRA), operation support system, automation, have been conducted in order to reduce human errors. Among these fields of studies, automation has its own specialty since it mainly focuses on eliminating the human error-inducing tasks to reduce human error, while other topics are primarily focusing on reducing human error by aiding human operators during operation. It is commonly believed that replacing human operators to the automated system would guarantee greater efficiency, lower workloads, and fewer human errors. However, these beliefs are not always true in large and complex systems such as NPP, since excessive automation can generate new roles for human operators, change human operators' activities in unexpected ways, and inducing 'out-of-the-loop' (OOTL) problem. Especially, although human error probability drastically increases during abnormal situations in NPP due to overload of information, high workload, and short time available for diagnosis, automation is not actively adopted since conventional machine learning techniques are neither capable nor reliable to handle overwhelmingly complex abnormal situations in NPP. Recently, newly developed 'deep learning' techniques have been actively applied to many fields, and some of these studies proved that the artificial intelligences (AI) based on deep learning techniques show better performance than that of human being. In 2015, deep Q-network (DQN) which is one of the deep learning techniques was developed and applied to train AI that automatically plays various Atari 2800 games, and this AI surpassed the human-level playing in many kind of games. Also in 2016, 'AlphaGo', which was developed by 'Google Deepmind' based on deep learning technique to play the game of Go, was defeated Se-dol Lee who is the World Go champion with score of 4:1. In the point of view that the game of Go is considered as one of the challenging area in machine learning field, it is verified that these new machine learning techniques can perform well, even better than the human being in high-complexity tasks. Previously, most of AI applications in nuclear field were focusing on supporting human operators' tasks such as diagnosis and decision making by rapid classification and prediction, based on fixed training data sets. However, it is expected that AI can be applied to solve more complicated problems (i.e. automation during abnormal states in NPP) by adopting advanced machine learning methodologies. In order to apply the automation system in NPP, firstly it is needed to evaluate the status of the plant quantitatively in real-time, since the information about which state is better (i.e. safe) or worse (i.e. unsafe) is essential for training properly working automation AI. Currently, there are several methodologies for NPP safety evaluation such as probabilistic safety assessment (PSA), safety performance indicator (SPI). However, existing methodologies are not feasible for development of automated system since they are not capable for the cases which are not included in the underlying model, not able to consider delicate changes in the NPP, and hard to evaluate in real-time. Therefore, development of new NPP safety evaluation methodology which can cover previously mentioned requirements is needed to conduct the study on NPP automation further. In this study, the concept of early warning score (EWS) in medical field was adopted to develop the methodology which can evaluate the safety level of the NPP quantitatively and in real-time. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2016

Conditional Tree Reduction in the ADAPT Framework

Probabilistic Risk Assessment (PRA) uses fault tree/event tree analysis to evaluate the environmental impacts of nuclear power plants due to internal or external initiating events. In a fault tree, basic failure events are assembled using primarily And/Or logic to determine the combinations of failures that lead to failure of the system as a whole. An event tree is forward-facing, and begins with a single initiating event. From there the analysis considers what event may occur next, and branches out among the possible configurations of occurrence and non-occurrence of that event. This branching continues until end states are reached. End states may include a safe and stable configuration of the plant, or any number of differing failure states. Both fault and event trees require basic event probabilities as inputs to provide insight on the likelihood of different outcomes. PRA has been applied to nuclear power plants as an analysis tool since the 1975 Reactor Safety Study. In a traditional PRA, the order of events is prescribed by the analyst and each event is typically a binary aleatory uncertainty: occurrence or non-occurrence. Discrete dynamic event tree (DDET) analysis eliminates this subjectivity in ordering of events by using the output of a dynamic system model (simulator) to inform the branching. Branching conditions are triggered by the existence of a relevant plant state in the code, and therefore only occur as physically appropriate. The DDET approach also allows consideration of both epistemic and aleatory uncertainties on a phenomenologically and stochastically consistent platform. A DDET may be orders of magnitude larger than a traditional event tree for the same initiating event, as more of the uncertainty space is likely to be explored and the tree does not require manual assembly. Because of the size, manual inspection of the entire DDET is often infeasible. This problem may be approached from the front by reducing the number of sampled branching values or by online pruning of the tree, thus reducing the total number of branches. Utilizing post-processing techniques to draw actionable insights via clustering has also been proposed. One post-processing tool to reduce the DDET according to user-defined rules has been recently implemented in the Analysis of Dynamic Accident Progression Trees (ADAPT) DDET driver code. The tool creates a 'slice' of the DDET comprised of the set of branches that meet a set of user-input rules, for immediate visualization or for further calculations conditional on the rules being met. This extension is expected to ease analysis of the results of large and complex DDETs, leading to greater applicability of DDETs in PRA. It should be emphasized that this tool is a visualization aide and does not impact the theoretical basis of the DDET approach. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2016

Extension of the ADAPT Framework for Multiple Simulators

Probabilistic Risk Assessment (PRA) uses fault tree/event tree analysis to evaluate the environmental impacts of nuclear power plants due to internal or external initiating events. In a fault tree, basic failure events are assembled using primarily And/Or logic to determine the combinations of failures that lead to failure of the system as a whole. An event tree is forward-facing, and begins with a single initiating event. From there the analysis considers what event may occur next, and branches out among the possible configurations of occurrence and non-occurrence of that event. This branching continues until end states are reached. End states may include a safe and stable configuration of the plant, or any number of differing failure states. Both fault and event trees require basic event probabilities as inputs to provide insight on the likelihood of different outcomes. PRA has been applied to nuclear power plants as an analysis tool since the 1975 Reactor Safety Study. In a traditional PRA, the order of events is prescribed by the analyst and each event is typically a binary aleatory uncertainty: occurrence or non-occurrence. Discrete dynamic event tree (DDET) analysis eliminates this subjectivity in ordering of events by using the output of a dynamic system model (simulator) to inform the branching. Branching conditions are triggered by the existence of a relevant plant state in the code, and therefore only occur as physically appropriate. The DDET approach also allows consideration of both epistemic and aleatory uncertainties on a phenomenologically and stochastically consistent platform. One limitation of the DDET driver codes developed to date (namely ADAPT, ADS, and MCDET ) is that the tree is driven by a single simulator. The goal of this work is a general multi-simulator driver. This limits the parameter space that may be explored in a single DDET to that which may be covered by a single simulator. In this work, the Analysis of Dynamic Accident Progression Trees (ADAPT) code is extended to support multiple arbitrary simulators. This capability will increase the depth and breadth of phenomena that may be analyzed in a single DDET. For example, a large-scale system code may pass the analysis to a more detailed code if certain conditions are met, in order to capture relevant phenomena that would be lost if only the system code were to be used. In addition, the time required to cover a given uncertainty space may be reduced by dynamically identifying and running only the situations that are appropriate given the plant conditions in each simulator. The use of multiple simulators is expected to further reduce subjectivity in PRA by enabling high fidelity modeling of reality. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2016

Comparison of Surrogate Models with Physical Models for Dynamic Probabilistic Risk Analysis Using the RAVEN Code

Dynamic probabilistic risk analysis (DPRA) has emerged in the nuclear industry as an effective tool to identify points of weakness in nuclear reactors and create a comprehensive approach to quantifying risk. While DPRA allows for detailed analysis, it usually requires thousands of runs due to the necessity of exploring low probability, high consequence events, which can be computationally prohibitively difficult to assess. Risk Analysis Virtual Environment (RAVEN) is a software package being developed under the Nuclear Energy Advanced Modeling and Simulation program at the Idaho National Laboratory. It is able to manage complex control logic to drive a RELAP-7 simulation and to handle sampling strategies including Monte Carlo, Latin Hypercube, Grid and Adaptive samplers. By performing a thorough investigation of the possibility space of an accident scenario with RELAP-7, RAVEN is able to identify consequences of accident scenarios that have not been encountered. The most computational resource intensive step of a RAVEN analysis is running the RELAP-7 code modeling the physical system behavior. Using surrogate models to represent the outcomes of more complex simulations rather than the full model can greatly reduce the computation effort required. In situations where the uncertainty distributions of a model are unknown, or when there are multiple sets of uncertainty distributions, DPRA generally requires rerunning the physical model for each set of uncertainty distributions used. By taking an unbiased sample of the physical model and creating a surrogate model from the results, the surrogate model can be sampled under a different set of uncertainty distributions to provide equivalent results. This paper presents an example of this approach. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2016

Transitions From Stochastic to Point Kinetics Models In Fissile Solutions

Fissile solutions form an integral part of the fuel cycle, being used to store and process both fresh fuel and waste. Fissile solutions are also found within Aqueous Homogeneous Reactors (AHRs) including research reactors such as CRAC and SILENE and also potential future systems which could be used for the production of medical isotopes. In the case of fissile solutions in the fuel cycle, avoiding criticality is a priority but accidents have occurred in which a significant number of fissions occurred. In the case of AHRs, a sustained neutron population and fission rate is desired. In both cases the ability to simulate transients where changes in reactivity cause the neutron population to increase are important. A popular method for the simulation of such transients is the point kinetic method which allows the capture of the major features of a transient at a fraction of the computational runtime and code complexity of more comprehensive modelling technologies. A limitation of the point kinetics method and other mainstream methods of predicting neutron behaviour is that they assume the neutron population is large enough that the mean behaviour will be observed. When the neutron population is low, however, the deviation around the mean behaviour is expected to be relatively large as there is an aleatoric uncertainty arising from the uncertain fate of individual neutrons. Such limitations are of particular importance when the reactivity of a system is rapidly increasing while the neutron population is low, which is often true in accident scenarios involving fissile solutions. This provides the motivation for the coupling of point kinetics and a probabilistic models of fissile solutions. This summary aims to explore some of the behaviours resulting from a low neutron population and rising reactivity using a coupled probabilistic and point kinetics model of a fissile solution. As the effects of a low neutron population are the primary concern of this summary, the point kinetics and thermal hydraulic models used will be relatively simple but these models should be sufficient to capture the most important behaviours of the system in the first minute or so of a transient. This work is an extension of the work found in [1]. In that work the methodology was applied to fast pulses produced by systems such as Godiva and its results were found to compare favourably to experimental and analytical results, such as the burst wait time, in [2]. This work focuses on the application of this methodology to a fissile solution but the method could be applied to a number of criticality safety or reactor operation scenarios through suitable modification of the point kinetics model and would produce a more accurate prediction of the peak power obtained when a system first becomes critical compared to the use of a standard point kinetics model. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2016

Development of Advanced Numerical Tools for the Nonlinear Seismic Analysis of Nuclear Systems

In the framework of Probabilistic Risk Assessment (PRA) of nuclear facilities, a rigorous dynamic characterization of all structural and non-structural components is of paramount concern. The common features of nuclear facilities lies in the combination of three characteristics: 1. low aspect ratio; 2. large mass; and 3. significant embedment in soil. These characteristics make the interaction between the structural system and the surrounding soil a key aspect to consider for seismic risk evaluations. The methodologies and numerical tools currently in use for simulating the seismic response of a nuclear facility with significant Soil Structure Interaction (SSI) date back to the 1970's and rely on a number of simplifying idealizations, mainly related to the characterization of the incident seismic waveforms, the assumption of linear-elastic behavior of the superstructures, and idealization of equivalent linear behavior of the supporting soils. Given this background and taking advantage of the substantial progress in computational mechanics in the last decades, the ongoing research aims at developing advanced numerical tools for the nonlinear, time history simulation of nuclear facilities. This translates into the availability of a robust nonlinear time-domain simulation tool that can accurately model the nonlinearities of coupled soil-structure systems, and whose development includes robust verification and validation. The work described herein is supported by the U.S. Department of Energy (DOE) and focuses on delivering the next-generation, high performance simulation tool in support of DOE needs and mission. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2016

Pruning of Discrete Dynamic Event Trees Using Density Peaks and Dynamic Time Warping

Discrete Dynamic Event Trees (DDETs) are used in probabilistic risk assessment (PRA) to determine the risk associated with complex systems, such as nuclear power plants, where accident scenarios need to be realistically modeled. One of the most challenging aspects of the DDET method is that a large number of branches are produced for each initiating event. This can result in data that is difficult to organize and time consuming to analyze. As the number of uncertain parameters increases, so does the complexity of the event tree. An event tree often has nodes that do not contribute to the overall results and predictive capabilities; therefore, we suggest a pre-pruning algorithm that can work to remove these nodes, and consequently, reduce the complexity of the tree. Sandia National Laboratories (SNL) is currently working on a two year dynamic cyber-risk informed project to assess cyber failure in a nuclear power plant. This will be achieved by coupling SNL's dynamic scheduler known as Analysis of Dynamic Accident Progression Trees (ADAPT) with MELCOR, a reactor accident simulator, and a System for Circuit Evaluation and Prediction of Transient Radiation Effects (SCEPTRE), a cyber Emulytics code. This paper will discuss the preliminary results of a feasibility study for using density peaks (DP) clustering with dynamic time warping (DTW) to prune branches that are formed from large DDETs, like the ones that will be produced throughout SNL's project. Papers from the project that are submitted to this conference discuss the following: - The MELCOR model for a Pressurized Water Reactor that uses a digital instrumentation and control system. - Proposed generic cyber exploits for the above reactor. - Modifications to ADAPT to extend the framework for multiple simulators, incorporate importance measures into DDET analysis, as well as sort and reduce DDETs based on inputs to or outputs from the tree. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2016

Dynamic Importance Measures in the ADAPT Framework

Probabilistic Risk Assessment (PRA) uses fault tree/event tree analysis to evaluate the environmental impacts of nuclear power plants due to internal or external initiating events. In a fault tree, basic failure events are assembled using primarily And/Or logic to determine the combinations of failures that lead to failure of the system as a whole. An event tree is forward-facing, and begins with a single initiating event. From there the analysis considers what event may occur next, and branches out among the possible configurations of occurrence and non-occurrence of that event. This branching continues until user-defined end states are reached. End states may include a safe and stable configuration of the plant, or any number of differing failure states. Both fault and event trees require basic event probabilities as inputs to provide insight on the likelihood of different outcomes. PRA has been applied to nuclear power plants as an analysis tool since the 1975 Reactor Safety Study. In a traditional PRA, the order of events is prescribed by the analyst and each event is typically a binary aleatory uncertainty: occurrence or non-occurrence. Discrete dynamic event tree (DDET) analysis eliminates this subjectivity in ordering of events by using the output of a dynamic system model (simulator) to inform the branching. Branching conditions are triggered by the existence of a relevant plant state in the code, and therefore only occur as physically appropriate. The DDET approach also allows consideration of both epistemic and aleatory uncertainties on a phenomenologically and stochastically consistent platform. One current limitation of DDETs is the assessment of results, which is not as well developed as in traditional PRA. A concept that is used in PRA to assess the significance of a basic event is the concept of importance measures (IMs). In traditional PRA, IMs only consider the likelihood of occurrence of non-occurrence of a basic event. The application of importance measures to DDETs must take into consideration not only the occurrence and non-occurrence of an event, but also uncertain timing or severity. This work describes three general dynamic importance measures (DIMs) as implemented in the Analysis of Dynamic Accident Progression Trees (ADAPT) DDET driver code. The measures account for the change in progression of the DDET resulting from the different values of each uncertain variable under investigation. The DIMs are applied to a sample DDET to demonstrate the type of insights that may be gained from each one. These measures will facilitate comparison of the impact of dynamic branching events on a consequence of interest, which also allow DDETs to be used in prioritizing plant investments. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2016

Tuning and Verification of an Internal Flooding Model for Dynamic PRA

In probabilistic risk assessment (PRA), the likelihoods and consequences of basic events are combined to determine possible system level outcomes and the likelihood of each outcome given an initial event. Both the consequences and likelihoods of basic events may be informed by the use of mathematical models or experimental data regarding the phenomena of interest. In either case, the impact of uncertainties on the results need to be evaluated. If a model is used to predict the evolution of an initiating event, uncertainty quantification can be computationally challenging for complex phenomena with many epistemic (e.g., model) uncertainties. In this work, a reduced order model is introduced for the flooding analysis of a multi-room building at a nuclear power plant under uncertainties which can require long run times using high fidelity computer models. The reduced order model focuses on macro-level transfer of water between rooms and tracks the water level within each room. In order to verify the model's fidelity, its results for the phenomena involved in a scenario of interest are compared to those of a computational fluid dynamics (CFD) code, which are taken to represent reality. This work does not focus on the overall fidelity of the flooding model, as the goal of the project for which it was developed is to develop a methodology rather than to analyze a particular case. The focus instead is on the tunability and verifiability of the reduced order model for relevant physical measures. In analyzing a particular case, the model may be required to incorporate more realistic description of phenomena; however, the procedure described in this work may be used to verify its performance as well. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2016

Rich-Text based PSA Model Transformation and Verification in RiskA

Nuclear Energy is one of the most possible alternative to fossil energy in large-scale. Nuclear safety is the lifeline of nuclear energy development, and has been an important part of national security, especially after the accident of Fukushima. On the basis of computer codes, reliability engineering and Probabilistic Risk Assessment (PSA) is effective methods to improve the safety of nuclear power plants. After many years of development, the methodologies, applications and relevant soft programs have been quite sufficient in the area of nuclear safety. However, it is still difficult for the current PSA technologies and software to meet the needs of dealing with the problems of quantitatively public risk assessment in the digital society. Especially, there are so many software that adopted different modeling and analyzing methods, which brings difficulties to the comparison, evaluation or verification for different software and models. By using its own academic advantages, the advanced nuclear energy research team, i.e., FDS Team has invested a few hundreds person-years of independent research and development of a reliability and probabilistic safety assessment software system RiskA. The system consists of three main functions: device reliability optimization management, probabilistic safety assessment and instantaneous risk monitoring, and the comprehensive public risk assessment in digital society. RiskA establishes an integrated risk evaluation system for the device reliability, nuclear power plant (NPP) risk monitoring and public risk assessment. Currently, RiskA has successfully been applied in more than 600 organizations and institutes from a variety of fields in 39 countries, involving nuclear, aerospace, defense and so on. For example, RiskA has been used in the safety analysis of International Thermonuclear Experimental Reactor (ITER), FDS series fusion reactor, and the Strategic Priority Research Program of Chinese Academy of Sciences 'the Future Advanced Nuclear Fission Energy - Accelerator Driven Sub-critical (ADS) transmutation system'. Also, RiskA has been used in public risk communication and risk assessment. Among them, Third Qinshan nuclear power plant Risk Monitor (TQRM), which is the China's first domestic nuclear safety analysis software used in the actual online operation, has been highly apprised by renowned experts in the field. Meanwhile, it has been identified as the first domestic nuclear power plant risk monitoring system with completely independent intellectual property rights, and has reached the international advanced level. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2016

Identification of Significant NPP Cyber-Attack Scenarios based on Risk Information

For the last decades, analog instrumentation and control (I and C) systems have been replaced with digital systems in nuclear power plants (NPPs). While there are various advantages of the digital technology for the plant safety, new threats have been introduced such as cyber security. In fact, there have been cyber-attacks on nuclear facilities adopting digital systems. The cyber-attack on the Iran nuclear facility is a typical example which shows the possibility of a cyber-attack on safety critical infrastructures with intra-network. Cyber security has been introduced as one of the significant issues for NPP safety and recently its importance has been more highlighted, but the research in this field is not mature yet. Moreover, there is not much information about cyber security of NPPs. In this work, as a fundamental research for risk evaluation of cyber-attacks, a method to identify significant cyber-attack scenarios on NPPs was proposed based on the probabilistic risk assessment (PRA) method which is widely used for evaluating the safety of NPPs. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2016

A Simplified Multi-Path Event Tree for the Integral, Inherently-Safe Light Water Reactor (I{sup 2}S-LWR)

The Integral, Inherently-Safe Light Water Reactor (I{sup 2}S-LWR) concept aims to raise the bar for nuclear power plant safety while maintaining the economic advantages of large, gigawatt-scale plants.1 To obtain this goal, the project safety team seeks to eliminate initiating events where possible, maximize passivity of safety systems, and optimize a balance of redundancy, diversity, and simplicity of safety systems.2 Achievement of these objectives, particularly the last set, requires detailed, quantitative risk information from the earliest stages of development. Therefore, despite limited design information, we have constructed a preliminary probabilistic risk assessment (PRA) for I{sup 2}S-LWR. Safety implementation for I{sup 2}S-LWR, then, requires an iterative process involving system design, thermal-hydraulic (T/H) and neutronic deterministic analyses, and risk-informing PRA studies. Due to this parallelism, it is vital those team members without PRA expertise quickly and correctly understand preliminary PRA results. To this end, we have developed a method to condense the entire PRA into a single, multi-path event tree (MPET). (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗