Quantifying reactor safety margins part 3: Assessment and ranging of parameters

Wulff, W.; Boyack, B.E.; Catton, I.; Duffey, Romney B.; Griffith, P.; Katsma, K.R.; Lellouche, G.S.; Lévy, S.; Rohatgi, U.S.; Wilson, G.E.; Usnrc, N. Zuber

doi:10.1016/0029-5493(90)90073-7

Cited by 50 publications

(9 citation statements)

References 4 publications

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“…Analysis results indicate that an increase in fuel thermal conductivity by 200% and 500% would reduce PCT by 56°C and 92°C, respectively; also, increased fuel thermal conductivity would result in faster quench times [for nominal, 200% and 500%, the calculated quench times are 213, 194, and 177 seconds, respectively (from Figure 12)]. The magnitude of this change normalized against the thermal resistance of the fuel (directly proportional to the stored energy) is consistent with earlier analyses focused on parameter uncertainty (Wulff et al 1990).…”

Section: Example Analyses For Hypothetical Atf Conceptssupporting

confidence: 82%

Advanced Fuels Campaign: Light Water Reactor Accident Tolerant Fuel Performance Metrics

Merrill¹,

Teague²,

Youngblood³

et al. 2014

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Section: Example Analyses For Hypothetical Atf Conceptssupporting

confidence: 82%

Advanced Fuels Campaign: Light Water Reactor Accident Tolerant Fuel Performance Metrics

Merrill¹,

Teague²,

Youngblood³

et al. 2014

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“…[159] The PIRT was developed to assess the safety of nuclear reactors and has primarily been used for that purpose. [21,196,197,199,204] The PIRT is critical for planning validation experiments because it helps establish both sufficiency and efficiency of the validation activities. To demonstrate sufficiency requires a careful response to the question: What has to be done to establish a necessary level of confidence in the application of the code?…”

Section: Modeling and Simulation Requirementsmentioning

confidence: 99%

Verification, validation, and predictive capability in computational engineering and physics

Oberkampf

Trucano

Hirsch

2004

Applied Mechanics Reviews

511

197

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Developers of computer codes, analysts who use the codes, and decision makers who rely on the results of the analyses face a critical question: How should confidence in modeling and simulation be critically assessed? Verification and validation (V&V) of computational simulations are the primary methods for building and quantifying this confidence. Briefly, verification is the assessment of the accuracy of the solution to a computational model. Validation is the assessment of the accuracy of a computational simulation by comparison with experimental data. In verification, the relationship of the simulation to the real world is not an issue. In validation, the relationship between computation and the real world, i.e., experimental data, is the issue.-3 -This paper presents our viewpoint of the state of the art in V&V in computational physics. (In this paper we refer to all fields of computational engineering and physics, e.g., computational fluid dynamics, computational solid mechanics, structural dynamics, shock wave physics, computational chemistry, etc., as computational physics.) We do not provide a comprehensive review of the multitudinous contributions to V&V, although we do reference a large number of previous works from many fields. We have attempted to bring together many different perspectives on V&V, highlight those perspectives that are effective from a practical engineering viewpoint, suggest future research topics, and discuss key implementation issues that are necessary to improve the effectiveness of V&V. We describe our view of the framework in which predictive capability relies on V&V, as well as other factors that affect predictive capability. Our opinions about the research needs and management issues in V&V are very practical: What methods and techniques need to be developed and what changes in the views of management need to occur to increase the usefulness, reliability, and impact of computational physics for decision making about engineering systems?We review the state of the art in V&V over a wide range of topics, for example, prioritization of V&V activities using the Phenomena Identification and Ranking Table (PIRT), code verification, software quality assurance (SQA), numerical error estimation, hierarchical experiments for validation, characteristics of validation experiments, the need to perform nondeterministic computational simulations in comparisons with experimental data, and validation metrics. We then provide an extensive discussion of V&V research and implementation issues that we believe must be addressed for V&V to be more effective in improving confidence in computational predictive capability. Some of the research topics addressed are development of improved procedures for the use of the PIRT for prioritizing V&V activities, the method of manufactured solutions for code verification, development and use of hierarchical validation diagrams, and the construction and use of validation metrics incorporating statistical measures. Some of the implementation topics address...

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“…In an earlier paper, Wulff et al [1990] assessed the reactor safety margins and ranges of parameters in a gas-cooled reactor.…”

Section: Task 1: Development Of the Scheduler Probability Module Andmentioning

confidence: 99%

Uncertainty Quantification in the Reliability and Risk Assessment of Generation IV Reactors: Final Scientific/Technical Report

Vierow¹,

Aldemir²

2009

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4or scenarios. This capability allows the grouping of branches and scenarios based off of user-specified bins of plot data.Finally, one of the major additions to the software is the DET generation software web interface. The web interface serves as a front-end to the server and DATABASE MANAGEMENT SYSTEM and can be used to launch experiments, register new types of simulators, halt and resume experiments, visualize the current event tree status, and download all files associated with a particular branch. The web interface can be viewed from any internet browser and outside of the web-server host's local network (barring firewall restrictions). The web interface has provided a more user-friendly means of executing experiments since a command line tool is no longer necessary. This interface does not contain any analysis tools and was designed as a first step in making a more user-friendly experiment execution system. Task 2: Linking the SCHEDULER and the DATABASE MANAGEMENT SYSTEM to MELCORA plant simulator is being used to follow the transient along each branch. The ADAPT framework is designed to be simulator-independent, however, MELCOR was linked to ADAPT for demonstration purposes. Branches are generated using user-specified criteria which are supplied in a simulatorindependent input file. MELCOR Control Functions are utilized to provide the necessary simulator control for use with ADAPT. The Ohio State University developers of the computational infrastructure provided a training session on their software in September 2006. This included explanation of the system requirements, software capabilities, overview of the software, demonstration of a test problem, and many other aspects of the software. The TAMU PI and one grad student attended the training session. The PI and student provided input to the infrastructure developers regarding operating system compatibility, output parameters and other factors that would render the software compatible with TAMU user equipment and the MELCOR code. Task 3: Determination of key uncertainties in reactor modeling and high risk initiating eventsThe detailed consideration of the key uncertainties and event selection that were conducted are described below. A Generation IV Pebble Bed Modular Reactor (PBMR) was selected as the reactor design to be simulated. This high-temperature gas-cooled reactor design is a good test of the ADAPT framework and computational system. Further, the MELCOR input deck was modified to accommodate the event and evaluation of the figures of merit.

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Quantifying reactor safety margins part 3: Assessment and ranging of parameters

Cited by 50 publications

References 4 publications

Advanced Fuels Campaign: Light Water Reactor Accident Tolerant Fuel Performance Metrics

Advanced Fuels Campaign: Light Water Reactor Accident Tolerant Fuel Performance Metrics

Verification, validation, and predictive capability in computational engineering and physics

Uncertainty Quantification in the Reliability and Risk Assessment of Generation IV Reactors: Final Scientific/Technical Report

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