Modeling of critical experiments and its impact on integral covariance matrices and correlation coefficients. (June 2016)

• Record Type: Journal Article
• Authors: Peters, Elisabeth; Sommer, Fabian; Stuke, Maik
• Is Part Of: Annals of nuclear energy. Volume 92(2016:Jun.)
• Page Start: 355
• Page End: 362
• Publication Date: 2016-06
• Subjects: Criticality safety -- Code validation -- Correlation coefficients -- Covariance matrices -- Monte Carlo Sampling; Nuclear energy -- Periodicals; Nuclear engineering -- Periodicals; 621.4805
• DOI: 10.1016/j.anucene.2016.02.011
• Languages: English
• ISSNs: 0306-4549

Abstract

Highlights: 9 different modeling approaches for data from critical experiments are discussed and analyzed. The impact of different modeling assumptions on integral covariance matrices is shown. The resulting correlation coefficients of k eff due to experimental parameters vary between 0 and 1. Possible impacts on validation procedures are discussed. Abstract: In this manuscript we study the modeling of experimental data and its impact on the resulting integral experimental covariance and correlation matrices. By investigating a set of three low enriched and water moderated UO2 fuel rod arrays we found that modeling the same set of data with different, yet reasonable assumptions concerning the fuel rod composition and its geometric properties leads to significantly different covariance matrices or correlation coefficients. Following a Monte Carlo Sampling approach, we show for nine different modeling assumptions the corresponding correlation coefficients and sensitivity profiles for each pair of the effective neutron multiplication factor k eff . Within the 95 % confidence interval the correlation coefficients vary from 0 to 1, depending on the modeling assumptions. Our findings show that the choice of modeling can have a huge impact on integral experimental covariance matrices. When the latter are used in a validation procedure to derive a bias, this procedure can be affected by the choice of modeling assumptions, too. The correct consideration of correlated data seems to be inevitable if the experimental data in a validation procedure is limited or one cannot rely on a sufficient number of uncorrelated data sets, e.g. from different laboratories using different setups.