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Energies 2016, 9(12), 1039; doi:10.3390/en9121039

Exploring Stochastic Sampling in Nuclear Data Uncertainties Assessment for Reactor Physics Applications and Validation Studies

Laboratory for Reactor Physics and Systems Behaviour, Paul Scherrer Institute, 5232 Villigen PSI, Switzerland
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Academic Editor: Dan Gabriel Cacuci
Received: 25 September 2016 / Revised: 25 November 2016 / Accepted: 29 November 2016 / Published: 9 December 2016
(This article belongs to the Special Issue Advances in Predictive Modeling of Nuclear Energy Systems)
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Abstract

The quantification of uncertainties of various calculation results, caused by the uncertainties associated with the input nuclear data, is a common task in nuclear reactor physics applications. Modern computation resources and improved knowledge on nuclear data allow nowadays to significantly advance the capabilities for practical investigations. Stochastic sampling is the method which has received recently a high momentum for its use and exploration in the domain of reactor design and safety analysis. An application of a stochastic sampling based tool towards nuclear reactor dosimetry studies is considered in the given paper with certain exemplary test evaluations. The stochastic sampling not only allows the input nuclear data uncertainties propagation through the calculations, but also an associated correlation analysis performance with no additional computation costs and for any parameters of interest can be done. Thus, an example of assessment of the Pearson correlation coefficients for several models, used in practical validation studies, is shown here. As a next step, the analysis of the obtained information is proposed for discussion, with focus on the systems similarities assessment. The benefits of the employed method and tools with respect to practical reactor dosimetry studies are consequently outlined. View Full-Text
Keywords: neutronics; nuclear data; stochastic sampling; Pearson correlation coefficient; nuclear reactor dosimetry; uncertainty analysis; Monte Carlo simulation; light water reactor; validation neutronics; nuclear data; stochastic sampling; Pearson correlation coefficient; nuclear reactor dosimetry; uncertainty analysis; Monte Carlo simulation; light water reactor; validation
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MDPI and ACS Style

Vasiliev, A.; Rochman, D.; Pecchia, M.; Ferroukhi, H. Exploring Stochastic Sampling in Nuclear Data Uncertainties Assessment for Reactor Physics Applications and Validation Studies. Energies 2016, 9, 1039.

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