Book > Verification Validation And Uncertainty Quantification Of Multi Physics Modeling Of Nuclear Reactors

Improved Best Estimate Plus Uncertainty Methodology Including Advanced Validation Concepts to License Evolving Nuclear Reactors

Improved Best Estimate Plus Uncertainty Methodology Including Advanced Validation Concepts to License Evolving Nuclear Reactors

Detail Book : Improved Best Estimate Plus Uncertainty Methodology Including Advanced Validation Concepts to License Evolving Nuclear Reactors written by , published by which was released on 28 November 2020. Download Improved Best Estimate Plus Uncertainty Methodology Including Advanced Validation Concepts to License Evolving Nuclear Reactors Books now! Available in PDF, ePub and Kindle. Many evolving nuclear energy programs plan to use advanced predictive multi-scale multi-physics simulation and modeling capabilities to reduce cost and time from design through licensing. Historically, the role of experiments was primary tool for design and understanding of nuclear system behavior while modeling and simulation played the subordinate role of supporting experiments. In the new era of multi-scale multi-physics computational based technology development, the experiments will still be needed but they will be performed at different scales to calibrate and validate models leading predictive simulations. Cost saving goals of programs will require us to minimize the required number of validation experiments. Utilization of more multi-scale multi-physics models introduces complexities in the validation of predictive tools. Traditional methodologies will have to be modified to address these arising issues. This paper lays out the basic aspects of a methodology that can be potentially used to address these new challenges in design and licensing of evolving nuclear technology programs. The main components of the proposed methodology are verification, validation, calibration, and uncertainty quantification. An enhanced calibration concept is introduced and is accomplished through data assimilation. The goal is to enable best-estimate prediction of system behaviors in both normal and safety related environments. To achieve this goal requires the additional steps of estimating the domain of validation and quantification of uncertainties that allow for extension of results to areas of the validation domain that are not directly tested with experiments, which might include extension of the modeling and simulation (M & S) capabilities for application to full-scale systems. The new methodology suggests a formalism to quantify an adequate level of validation (predictive maturity) with respect to required selective data so that required testing can be minimized for cost saving purposes by showing further testing wold not enhance the quality of the validation of predictive tools. The proposed methodology is at a conceptual level. When matured and if considered favorably by the stakeholders, it could serve as a new framework for the next generation of the best estimate plus uncertainty licensing methodology that USNRC developed previously. In order to come to that level of maturity it is necessary to communicate the methodology to scientific, design and regulatory stakeholders for discussion and debates. This paper is the first step to establish this communication.

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Release Date : 28 November 2020
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Improved Best Estimate Plus Uncertainty Methodology Including Advanced Validation Concepts to License Evolving Nuclear Reactors

Improved Best Estimate Plus Uncertainty Methodology Including Advanced Validation Concepts to License Evolving Nuclear Reactors

Many evolving nuclear energy programs plan to use advanced predictive multi-scale multi-physics simulation and modeling capabilities to reduce cost and time from design through licensing. Historically, the role of experiments was primary tool for design and understanding of nuclear system behavior while modeling and simulation played the subordinate role of

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Uncertainty Quantification in Multiscale Materials Modeling

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Model Validation and Uncertainty Quantification  Volume 3

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Advances in computing hardware and algorithms have dramatically improved the ability to simulate complex processes computationally. Today's simulation capabilities offer the prospect of addressing questions that in the past could be addressed only by resource-intensive experimentation, if at all. Assessing the Reliability of Complex Models recognizes the ubiquity of uncertainty

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