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Global sensitivity analysis of reactor parameters / Bolade Adewale Adetula

Calculations of reactor parameters of interest (such as neutron multiplication factors, decay heat,
reaction rates, etc.), are often based on models which are dependent on groupwise neutron cross
sections. The uncertainties associated with these neutron cross sections are propagated to the final
result of the calculated reactor parameters. There is a need to characterize this uncertainty and to
be able to apportion the uncertainty in a calculated reactor parameter to the different sources of
uncertainty in the groupwise neutron cross sections, this procedure is known as sensitivity analysis.
The focus of this study is the application of a modified global sensitivity analysis technique to
calculations of reactor parameters that are dependent on groupwise neutron cross–sections. Sensitivity
analysis can help in identifying the important neutron cross sections for a particular model,
and also helps in establishing best–estimate optimized nuclear reactor physics models with reduced
uncertainties.
In this study, our approach to sensitivity analysis will be similar to the variance–based global
sensitivity analysis technique, which is robust, has a wide range of applicability and provides
accurate sensitivity information for most models. However, this technique requires input variables
to be mutually independent. A modification to this technique, that allows one to deal with input
variables that are block–wise correlated and normally distributed, is presented.
The implementation of the modified technique involves the calculation of multi–dimensional integrals,
which can be prohibitively expensive to compute. Numerical techniques specifically suited
to the evaluation of multidimensional integrals namely Monte Carlo, quasi–Monte Carlo and sparse
grids methods are used, and their efficiency is compared. The modified technique is illustrated and
tested on a two–group cross–section dependent problem. In all the cases considered, the results obtained
with sparse grids achieved much better accuracy, while using a significantly smaller number of samples. / Thesis (M.Sc. Engineering Sciences (Nuclear Engineering))--North-West University, Potchefstroom Campus, 2011.

Identiferoai:union.ndltd.org:NWUBOLOKA1/oai:dspace.nwu.ac.za:10394/5561
Date January 2011
CreatorsAdetula, Bolade Adewale
PublisherNorth-West University
Source SetsNorth-West University
Detected LanguageEnglish
TypeThesis

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