Document: NUREG-0800
Document ID: 2717fe7f-71fd-4f1e-bd08-7685b24763ba
Document Type: srp
Title: Standard Review Plans are prepared for the guidance of the Office of Nuclear Reactor Regulation staff responsible for th
Source: NUREG-0800
Source URL: https://www.nrc.gov/docs/ML0301/ML030160606.pdf
Revision Date: 2023-06
Chapter: 15
Section ID: 15
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Content:
firm that the level of detail in the model is equivalent to or greater than the level of detail required to specify the answer to the problem of interest. For example, a one-dimensional flow model can not provide information about the velocity profile in the vicinity of a pipe bend or the degree of thermal stratification in a horizontal pipe. A detailed, three-dimensional flow model would be needed to provide this type of information. The reviewers should confirm that the equations and derivations are correct. There must be sufficient text to adequately describe the derivation, including all assumptions and equations. The derivations must be sufficiently detailed to allow the reviewers to understand the logical progression of steps involved in the derivation. Simplifying assumptions must have a technical justification and a range of validity associated with them. Models that are typically used in nuclear reactor analysis are highly phenomenological and/or empirical in nature. They are either proposed using physical or engineering 9 judgement based on observations of experimental data or derived using averaging procedures applied to detailed first-principle models. These models often represent processes that occur on length and time scales that are too small to be resolved in the computation or processes for which we lack understanding to model from first principles. These models require closure relationships based on information from experimental measurements or detailed first-principle calculations. The reviewers should ensure that the range of validity of the closure relationships is specified and is adequate to cover the range of conditions encountered in the accident scenario. This is especially true of empirical correlations that are derived directly from experimental data without recourse to any physical modeling. In most applications, especially those with a large number of processes and parameters, it is difficult, if not impossible, to design test