Impact of experimental correlation on transposition method carry out with critical integral experiments
Abstract
In order to estimate the bias on the effective multiplication factor (k$_{eff}$) of a criticality
application case, and the associated uncertainty due to Nuclear Data (ND), a method which
takes advantage of the integral experiments information and is based on ND
sensitivity/uncertainty analyses and adjustment, has been implemented in a tool called RIB
(Représentativité, Incertitude, Biais). Such methodology has been worldwide studied for years
in various fields. The RIB tool, developed at CEA, is related to the experimental validation
database of the French criticality-safety package, CRISTAL V2.0, containing more than 2000
experiments from the International Criticality Safety Benchmark Evaluation Project handbook
(ICSBEP) and French experimental programs. In most cases, even if a correlation is identified
between the experiments, the value of this correlation might not be known.
Validation tests of the RIB tool, in particular by arbitrary applying strong correlations
between integral experiments (>0.9), point out some unrealistic results: a strong deviation of
the bias with significant reduction of the uncertainty due to ND. To check the RIB tool
implementation, transposition method equations have been implemented in Matlab. Another
tool developed at CEA and dedicated to ND evaluation, CONRAD (COde for Nuclear
Reaction Analysis and Data Assimilation), has also been used to estimate bias and uncertainty
due to ND through the transposition method using integral experiments. Results obtained with
these three tools from several combinations « Application case/integral experiments », have
been compared, confirming the RIB observed tendencies. Focusing on the post-adjustment
ND, it seems that such phenomenon is associated to a strong variation of the cross sections
which may be due to Peelle’s pertinent puzzle effect. This paper describes the different tools
and presents the results obtained for the tested combinations « Application case/integral
experiments » in function of the experimental correlation factor. Some potential explanations
of the observed results, using strong experimental correlation factors, are discussed.
Origin : Files produced by the author(s)
Loading...