Reducing digital risks and improving reliability in nuclear power integrated energy systems
Not Available
21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
Engineering topics
Youngblood, Robert
Publications and source records attributed to Youngblood, Robert.
Advanced Reactor Safeguards & Security 2024 Program Roadmap
The Advanced Reactor Safeguards and Security (ARSS) program was established to provide research support addressing near term challenges that advanced nuclear reactor vendors face in meeting domestic Material Control and Accounting (MC&A), Physical Protection System (PPS), and Cybersecurity requirements for U.S. construction. The technical work in the program is meant to (1) support nuclear reactor vendors with advanced MC&A, PPS, and Cybersecurity designs for next generation reactors, (2) provide technical bases for the regulator, and (3) promote the integration of Safeguards and Security by Design early in the design process. Existing domestic regulations for safeguards and security, as outlined in the Code of Federal Regulations, were written for large light water reactors, and rule-making efforts are underway to develop regulations more suited to different reactor designs. The ARSS program seeks to remove roadblocks in the deployment of new and advanced reactors by solving regulatory challenges, reducing safeguards and security costs, and utilizing the latest technologies and approaches for robust plant monitoring and protection. This roadmap discusses the goals of the ARSS program, current research, and program plan for the next five years.
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Risk-Informing Critical Digital Assets (CDAs) for Nuclear Power Systems
Explore the source record for details and available documents.
Muhlheim, Michael↗
Development of a nearly autonomous management and control system for advanced reactors
This presentation discusses the development of a Nearly Autonomous Management and Control (NAMAC) system for advanced reactors. This presentation first introduces the NAMAC-enabled plant control. Next, this presentation introduces major technologies of implementing NAMAC, including operational workflow, digital twin, and advanced machine learning algorithms. To demonstrate the capability of the NAMAC system, NAMAC is connected with a simulator of the Experimental Breeder Reactor II, where NAMAC is activated to provide recommendations during a single loss of flow accident.
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Digital-twin-based improvements to diagnosis, prognosis, strategy assessment, and discrepancy checking in a nearly autonomous management and control system
We report the Nearly Autonomous Management And Control System (NAMAC) is a comprehensive control system to assist plant operations by furnishing control recommendations to operators in a broad class of situations. This study refines a NAMAC system for making reasonable recommendations during complex loss of flow (LOF) scenarios with a validated EBR-II simulator, digital twins improved by machine-learning algorithms, a multi-attribute decision-making scheme, and a discrepancy checker for identifying unexpected recommendation effects. The performance of each NAMAC component is assessed, while the capability of NAMAC is demonstrated and evaluated in a class of LOF scenarios.
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Development and Application of a Data-Driven Methodology for Validation of Risk-Informed Safety Margin Characterization Models
The document is the Final Technical Progress Report for the Nuclear Energy University Program’s Integrated Research Project (IRP) on “Development and Application of a Data-Driven Methodology for Validation of Risk-Informed Safety Margin Characterization Models” that supports the LWR Sustainability Program’s RISMC R&D Pathway. The project goal is to develop and demonstrate a data-driven methodology for validation of advanced computer models used in nuclear power plant safety analysis. Specifically, the advanced computer models are those in the toolkit developed to support risk-informed safety margin characterization (RISMC), an integrated deterministic/probabilistic safety analysis methodology developed in the Department of Energy’s Light Water Reactor Sustainability (LWR-S) program. The report reflects the progress made towards the project’s stated goal by contributions by researchers and graduate students from universities, with support from researchers from national laboratories and industry companies. The project organization, effort coordination and technical implementation are summarized, followed by discussion of main findings, issues, and path forward. Selected chapters provide a more detailed description of tasks, approaches and respective findings and recommendations. Noteworthy are contributions that serve as guidelines for methodology development. It is also noted that this report is complemented by other milestone reports (as stand-alone deliverables) that provide detailed discussion of the technical developments. The project results have been documented in a number (12) dissertations and these, 50+ peer-reviewed publications in technical journals and conference proceedings.
42 ENGINEERING↗
Using deep learning to explore local physical similarity for global-scale bridging in thermal-hydraulic simulation
Not Available
Bao, Han (ORCID:0000000190598824)↗
Plant-Level Scenario-Based Risk Analysis for Enhanced Resilient PWR – SBO and LBLOCA
This report documents the activities performed during the FY2018 for the 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. There are two main focus areas in FY2018 for the ERP R&D efforts. One is to evaluate the risk impact brought by the ATF designs and FLEX in the selected accident scenarios. The other is to investigate various approaches to accomplish the ERP research objective, i.e., use RISA methods and toolkit to enhance existing reactors safety features and reduce plant operating costs.
99 GENERAL AND MISCELLANEOUS↗
Modeling the Risk of U.S. Offshore Oil & Gas Exploration-Well Drilling, Commercial Nuclear Plants, and Human Spaceflight
Probabilistic Risk Assessment (PRA) has been applied in different industries for many years. Each industry and technology area presents varying challenges and priorities, and the risk models therefore need to be somewhat different. This paper compares aspects of risk modeling of offshore drilling, nuclear plant operation, and human spaceflight. Risk models in all three technology areas have certain high-level similarities: (1) they employ redundancy and diversity in their means to prevent or mitigate risk, including a mix of active and passive systems designed to respond to off-normal evolutions; (2) they are affected by human reliability; (3) their models require consideration of coupling between scenario structure and scenario phenomenology. But in examining the models in more detail, one sees important differences in methodology and emphasis. For purposes of comparison, this paper discusses aspects of a risk model of an offshore drilling operation in the U.S. Gulf of Mexico, focusing on where such a development differs in important ways from models of commercial U.S. nuclear plants and models developed for human spaceflight.
Boyer, Roger L.↗
Modeling the Risk of U.S. Offshore Oil & Gas Exploration-Well Drilling, Commercial Nuclear Plants, and Human Spaceflight
Nuclear power, offshore oil & gas exploration, and human spaceflight all have high consequence potential if something goes wrong. Each has had at least one major incident that pointed to a need for improved risk assessment. Careful risk analysis is very important for technologies having complexity, uncertainty, and high consequence potential. All rely on multiple barriers/controls/redundancy to minimize risk. NRC (Nuclear Regulatory Commission) has moved towards risk informed regulation with Probabilistic Risk Assessment (PRA) as a major input.
PRA↗
Study of Data-Driven Mesh-Model Optimization in System Thermal-Hydraulic Simulation
Quantification of nuclear power plant safety risk requires a systematic and yet practical approach to identification of accident scenarios, assessment of their likelihood and consequences. Instrumental to this goal is risk-informed safety margin characterization (RISMC) framework, whose realization requires computationally robust and affordable methods for sufficiently accurate simulation of complex multi-dimensional physical phenomena, such as turbulent and multi-phase flow. The CFD-like correlation-based codes with 3D simulation capability (e.g., GOTHIC) ensure computational efficiency using coarse mesh size and the sub-grid phenomena in the boundary layer that can be captured by adequate constitutive correlations. However, the error sources and user effects on the selection of mesh size and models lead to unpredictable simulation error, while rich High-Fidelity (HF) data from experiments and numerical simulation using validated code or DNS are not fully explored. It would be useful to have a “smart” data-driven multi-scale framework in which the low-resolution models can be “taught” to emulate high-resolution models. The objective of this work is to develop and evaluate a physical-based data-driven mesh-model optimization approach (Optimized Mesh/Model Information System, OMIS) to estimate the simulation error and give advice on the optimized selection of coarse mesh size and Low-Fidelity (LF) models for System Thermal Hydraulic (STH) simulation to achieve accuracy comparable to that of HF models. This approach takes advantages of computational efficiency of coarse-mesh simulation and application of Machine Learning (ML) algorithms.
97 MATHEMATICS AND COMPUTING↗
Construction of a cyber attack model for nuclear power plants - 115
The consideration of how a compromised digital component can impact neighboring components is critical to understanding the progression of cyber attacks. The degree of influence that one component may have on another depends on a variety of factors, including the sharing of resources such as network bandwidth or processing power, the level of trust between components, and the inclusion of segmentation devices such as firewalls. The interactions among components via mechanisms that are unique to the digital world are not usually considered in traditional probabilistic risk assessment (PRA). This means potential sequences of events that may occur during an attack may be missed if one were to only look at conventional accident sequences. This paper presents a method where, starting from the initial attack vector, the progression of a cyber attack can be modeled. The propagation of the attack is modeled by considering certain attributes of the digital components in the system. These attributes determine the potential vulnerability of a component to different classes of attack and the capability gained by the attackers once they are in control of the equipment. The use of attributes allows similar components (components with the same set of attributes) to be modeled in the same way, thereby reducing the computing resources required for analyzing large systems. (authors)
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Construction of a Cyber Attack Model for Nuclear Power Plants
The consideration of how one compromised digital equipment can impact neighboring equipment is critical to understanding the progression of cyber attacks. The degree of influence that one component may have on another depends on a variety of factors, including the sharing of resources such as network bandwidth or processing power, the level of trust between components, and the inclusion of segmentation devices such as firewalls. The interactions among components via mechanisms that are unique to the digital world are not usually considered in traditional PRA. This means potential sequences of events that may occur during an attack may be missed if one were to only look at conventional accident sequences. This paper presents a method where, starting from the initial attack vector, the progression of a cyber attack can be modeled. The propagation of the attack is modeled by considering certain attributes of the digital components in the system. These attributes determine the potential vulnerability of a component to a class of attack and the capability gained by the attackers once they are in control of the equipment. The use of attributes allows similar components (components with the same set of attributes) to be modeled in the same way, thereby reducing the computing resources required for analysis of large systems.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
Light Water Reactor Sustainability Program Industry Application External Hazard Analyses Problem Statement
Risk-Informed Margin Management Industry Application on External Events. More specifically, combined events, seismically induced external flooding analyses for a generic nuclear power plant with a generic site soil, and generic power plant system and structure. The focus of this report is to define the problem above, set up the analysis, describe the methods to be used, tools to be applied to each problem, and data analysis and validation associated with the above.
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NASA System Safety Handbook. Volume 2: System Safety Concepts, Guidelines, and Implementation Examples
This is the second of two volumes that collectively comprise the NASA System Safety Handbook. Volume 1 (NASASP-210-580) was prepared for the purpose of presenting the overall framework for System Safety and for providing the general concepts needed to implement the framework. Volume 2 provides guidance for implementing these concepts as an integral part of systems engineering and risk management. This guidance addresses the following functional areas: 1.The development of objectives that collectively define adequate safety for a system, and the safety requirements derived from these objectives that are levied on the system. 2.The conduct of system safety activities, performed to meet the safety requirements, with specific emphasis on the conduct of integrated safety analysis (ISA) as a fundamental means by which systems engineering and risk management decisions are risk-informed. 3.The development of a risk-informed safety case (RISC) at major milestone reviews to argue that the systems safety objectives are satisfied (and therefore that the system is adequately safe). 4.The evaluation of the RISC (including supporting evidence) using a defined set of evaluation criteria, to assess the veracity of the claims made therein in order to support risk acceptance decisions.
Safety Case↗
Demonstration of emulator-based Bayesian calibration of safety analysis codes: Theory and formulation
System codes for simulation of safety performance of nuclear plants may contain parameters whose values are not known very accurately. New information from tests or operating experience is incorporated into safety codes by a process known as calibration, which reduces uncertainty in the output of the code and thereby improves its support for decision-making. The work reported here implements several improvements on classic calibration techniques afforded by modern analysis techniques. The key innovation has come from development of code surrogate model (or code emulator) construction and prediction algorithms. Use of a fast emulator makes the calibration processes used here with Markov Chain Monte Carlo (MCMC) sampling feasible. This study uses Gaussian Process (GP) based emulators, which have been used previously to emulate computer codes in the nuclear field. The present work describes the formulation of an emulator that incorporates GPs into a factor analysis-type or pattern recognition-type model. This “function factorization” Gaussian Process (FFGP) model allows overcoming limitations present in standard GP emulators, thereby improving both accuracy and speed of the emulator-based calibration process. Calibration of a friction-factor example using a Method of Manufactured Solution is performed to illustrate key properties of the FFGP based process.
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Risk-Informed Margin Management (RIMM) Industry Applications IA1 - Integrated Cladding ECCS/LOCA Performance Analysis - Problem Statement
The U. S. NRC is currently proposing rulemaking designated as “10 CFR 50.46c” to revise the LOCA/ECCS acceptance criteria to include the effects of higher burnup on cladding performance as well as to address some other issues. The NRC is also currently resolving the public comments with the final rule expected to be issued in the summer of 2016. The impact of the final 50.46c rule on the industry will involve updating of fuel vendor LOCA evaluation models, NRC review and approval, and licensee submittal of new LOCA evaluations or reanalyses and associated technical specification revisions for NRC review and approval. The rule implementation process, both industry and NRC activities, is expected to take 5-10 years following the rule effective date. The need to use advanced cladding designs is expected. A loss of operational margin will result due to the more restrictive cladding embrittlement criteria. Initial and future compliance with the rule may significantly increase vendor workload and licensee cost as a spectrum of fuel rod initial burnup states may need to be analyzed to demonstrate compliance. Consequently there will be an increased focus on licensee decision making related to LOCA analysis to minimize cost and impact, and to manage margin.
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BAYESIAN CALIBRATION OF SAFETY CODES USING DATA FROM SEPARATE-AND INTEGRAL EFFECTS TESTS
Large-scale system codes for simulation of safety performance of nuclear plants may contain parameters whose values are not known very accurately. In order to be able to use the results of these simulation codes with confidence, it is important to learn how the uncertainty on the values of these parameters affects the output of the codes. New information from tests or operating experience is incorporated into safety codes by a process known as calibration, which reduces uncertainty in the output of the safety code, and thereby improves its support for decision-making. Modern analysis capabilities afford very significant improvements on classical ways of doing calibration, and the work reported here implements some of those improvements. The key innovation has come from development of safety code surrogate model (code emulator) construction and prediction algorithms. A surrogate is needed for calibration of plant-scale simulation codes because the multivariate nature of the problem (i.e., the need to adjust multiple uncertain parameters at once to fit multiple pieces of new information) calls for multiple evaluations of performance, which, for a computation-intensive model, makes calibration very computation-intensive. Use of a fast surrogate makes the calibration processes used here with Markov Chain Monte Carlo (MCMC) sampling feasible. Moreover, most traditional surrogates do not provide uncertainty information along with their predictions, but the Gaussian Process (GP) based code surrogates used here do. This improves the soundness of the code calibration process. Results are demonstrated on a simplified scenario with data from Separate and Integral Effect Tests.
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