Nuclear data correlation between different isotopes via integral information

Abstract

This paper presents a Bayesian approach based on integral experiments to create correlations between different isotopes which do not appear with differential data. A simple Bayesian set of equations is presented with random nuclear data, similarly to the usual methods applied with differential data. As a consequence, updated nuclear data (cross sections, [see formula in PDF], fission neutron spectra and covariance matrices) are obtained, leading to better integral results. An example for 235U and 238U is proposed taking into account the Bigten criticality benchmark.

Examples of Monte Carlo techniques applied for nuclear data uncertainty propagation

“…The 235 U random files are obtained from TENDL-2014, a total number of 5000 samples were used in this work. This number seems enough to reach an adequate convergence in BMC [18].The high sensitivity of the fission cross section in Godiva shows an increase in fission around +1.4% if only Godiva is used in the adjustment. LLNL-pulsed sphere goes in the opposite direction, with -1.0%.…”

“…The requirement of a well-known prior and likelihood probability density function is a serious limitation of the previous approach if non-informative prior distributions are known [9,11]. To overcome this limitation the Bayesian Monte Carlo (BMC) approach [9,11,12,13] takes advantage of TMC method to generate "a priori" random files which are not explicitly normal. The BMC technique will incorporate this integral "a priori" information through likelihood factors that are defined in Eq.…”

“…To overcome this limitation different renormalized k-functions have been introduced. For instance, the Backward-Forward MC method (BFMC) [12,13] proposed a renormalization function to avoid such large dispersion of weights. Recently, other methods have been developed with BMC to compensate for the model deficiencies and inconsistent data.…”

“…However, the keff value is a global parameter that can be achieved through many possible combinations of nuclear data. It can lead to inherent compensating effects in the adjustment work [12,13]. In recent works, other sources of integral data are pointed out to extend the application and the prediction capabilities of future evaluations.…”

Monte Carlo integral adjustment of nuclear data libraries – experimental covariances and inconsistent data

“…IA is a well-established field [1,2] and can be performed by deterministic adjustment techniques [1] or by Monte Carlo IA (MC-IA) [3,4]; this paper concerns the later which sometimes is referred to as Bayesian Monte Carlo (BMC) [5]. However, since BMC can be used for both differential-and integral data and this particular paper addresses the use of integral data, we will use the term MC-IA MC-IA has been explored by many authors, both for improving the best estimate (central value) of the ND file [6], but also to reduce the ND uncertainty [2 -4].…”