Uncertainty propagation based on correlated sampling technique for nuclear data applications

A correlated sampling technique has been implemented to estimate the impact of cross section modifications on the neutron transport and in Monte Carlo simulations in one single calculation. This implementation has been coupled to a Total Monte Carlo approach which consists in propagating nuclear data uncertainties with random cross section files. The TMC-CS (Total Monte Carlo with Correlated Sampling) approach offers an interesting speed-up of the associated computation time. This methodology is detailed in this paper, together with two application cases to validate and illustrate the gain provided by this technique: the highly enriched uranium/iron metal core reflected by a stainless-steel reflector HMI-001 benchmark, and the PETALE experimental programme in the CROCUS zero-power light water reactor.

Uncertainty propagation for the design study of the PETALE experimental programme in the CROCUS reactor

The PETALE experimental programme in the CROCUS reactor intends to provide integral measurements to constrain stainless steel nuclear data. This article presents the tools and the methodology developed to design and optimize the experiments, and its operating principle. Two acceleration techniques have been implemented in the Serpent2 code to perform a Total Monte Carlo uncertainty propagation using variance reduction and correlated sampling technique. Their application to the estimation of the expected reaction rates in dosimeters is also discussed, together with the estimation of the impact of the nuisance parameters of aluminium used in the experiment structures.

Scalable Correlated Sampling for Join Query Estimations on Big Data

Estimate query results within limited time constraints is a challenging problem in the research of big data management. Query estimation based on simple random samples per- forms well for simple selection queries; however, return results with extremely high relative errors for complex join queries. Existing methods only work well with foreign key joins, and the sample size can grow dramatically as the dataset gets larger. This research implements a scalable sampling scheme in a big data environment, namely correlated sampling in map-reduce, that can speed up search query length results, give precise join query estimations, and minimize storage costs when presented with big data. Extensive experiments with large TPC-H datasets in Apache Hive show that our sampling method produces fast and accurate query estimations on big data.

Universal modification of vector weighted method of correlated sampling with finite computational cost

The weighted method of dependent trials or weighted method of correlated sampling (MCS) allows one to construct estimators for functionals based on the same Markov chain simultaneously for a given range of the problem parameters. Choosing an appropriate Markov chain, it is necessary to take into account additional conditions providing the finiteness of the computational cost of weighted MCS. In this paper we study the issue of finite computational cost of the method of correlated sampling (MCS) in application to evaluation of linear functionals of solutions to a set of systems of 2nd kind integral equations. A universal modification of the vector weighted MCS is constructed providing the branching of chain trajectory according to elements of matrix weights. It is proved that the computational cost of the constructed algorithm is bounded in the case the base functionals are also bounded. The results of numerical experiments using the modified weighted estimator are presented for some problems of the theory of radiation transfer subject to polarization.

Total Monte Carlo acceleration for the PETALE experimental programme in the CROCUS reactor

The Bayesian Monte Carlo technics requires individual evaluations of random cross section files based on a Total Monte Carlo propagation. This article discusses the use of a Correlated Sampling acceleration applied to TMC calculations for experiments where a brute force technics is too expensive. An e_cient estimation of the reaction rate uncertainties in small dosimeters is obtained, together with the inter-dosimeter correlation associated to the cross section uncertainties.