Addressing challenges in nuclear data adjustment techniques using integral experiments based upon uranium solution thermal systems

Abstract A stochastic technique in view of determining neutronic parameters is used along with the JEFF-3.3 nuclear data library, trying to adjust in conjunction with the Asymptotic Generalized Linear Least-Squares (AGLLS) assimilation methodology, a demonstrative limited number of basic ENDF parameters for a few nuclides, which are U-235, U-238 and hydrogen. Correspondingly, the current investigations are primarily focused on low-enriched and intermediate-enrichment uranium solution thermal systems for which the effective multiplication factor, k eff , which is the parameter to assimilate, has been measured. Even though the nuclear data seems well known for these nuclides, which is confirmed by the overall good performance of prior, unadjusted data, some deficiencies are highlighted, justifying dedicated assimilation studies. Similarly to fast-spectrum systems, it is confirmed that meaningful, weak adjustments can be obtained on the one hand if the chosen assimilation database does not exhibit too strong analytical cross-correlations among the individual multiplication factors. For k eff , which is a global parameter, such strong correlations are anticipated for systems having similar ratios of the overall number of atoms of hydrogen to those of U-235 in the core, largely determining the slowing-down characteristics. Another condition for reasonable adjustments, on the other hand, is that the benchmarks part of the database should be consistent among each other. It is also verified that a sufficiently large number of multiplication factors would be required to reach a converged, reference adjustment. However, without enlarging the range of nuclides and ENDF parameters in addition to the selection of systems, a continuous increase of the number of parameters to assimilate is not possible in a meaningful way without having to extend the database to other integral parameters than k eff such as spectral indices, which unfortunately are not available for these thermal systems. Namely, adding new parameters of the same type to databases consisting of three sufficiently decoupled systems, always increases remarkably the analytical cross-correlation of the k eff s; in which cases AGLLS either provides stronger, non-physical adjustments or even diverges, as at least one eigenvalue of the analytical covariance matrix of the parameters to assimilate decreases too much, approaching singularity in the worst case.