United States Nuclear Regulatory Commission - Protecting People and the Environment

Proposal for the Development and Implementation of an Uncertainty and Sensitivity Analysis Module in SNAP (NUREG/IA-0407)

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Publication Information

Manuscript Completed: September 2011
Date Published: April 2012

Prepared by:
Prof. Dr. Rafael Macián-Juan, Dr. Wolfgang Tietsch*, Dr. Felix Sassen*

Lehrstuhl für Nukleartechnik
Technische Universität München
Boltzmannstr. 15
85747 Garching, Germany

*Westinghouse Electric Germany GmbH
Dudenstrasse 44
68167 Mannheim, Germany

A. Calvo, NRC Project Manager

Prepared as part of:
The Agreement on Research Participation and Technical Exchange
Under the Thermal-Hydraulic Code Applications and Maintenance Program (CAMP)

Published by:
Office of Nuclear Regulatory Research
U.S. Nuclear Regulatory Commission
Washington, DC 20555-0001

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This report presents a proposal for the implementation of a module in the SNAP program that permits uncertainty propagation in the codes which it supports. In particular, as a first application, it suggests that the module be developed and tested with TRACE. Once the functionality and applicability have been tested, an extension to the other codes could be carried out with relatively small additional effort. The module in SNAP should allow for both propagating uncertainty from model input variables and physics models implemented in the code. Furthermore it should be capable to support sensitivity analysis with respect to model input parameters.

The report is structured in several sections that give an overview of the most commonly used uncertainty propagation methodologies today. The review performed leads to the conclusion that the most promising methodology for implementation in SNAP is, in the opinion of the authors, the one based on a statistical computational framework. For this reason, the report is focused on the proposal of an uncertainty module for SNAP that can successfully and efficiently carry out uncertainty propagation for the codes that it supports based on the statistical methodology developed by LANL and GRS.

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