Methodology for an efficient statistical cross sections sampling in the unresolved resonance range

Autor: C. Jeannesson, Luiz Leal, M. Coste-Delclaux, Cédric Jouanne
Přispěvatelé: Institut de Radioprotection et de Sûreté Nucléaire (IRSN), Service Neutronique et Criticité (IRSN/PSN-RES/SNC), CEA-Direction des Energies (ex-Direction de l'Energie Nucléaire) (CEA-DES (ex-DEN)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA), 50% fonds publics, GTR : Criticité/Acquisition et exploitation de données expérimentales
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Annals of Nuclear Energy
Annals of Nuclear Energy, 2022, 166, pp.108794. ⟨10.1016/j.anucene.2021.108794⟩
ISSN: 0306-4549
DOI: 10.1016/j.anucene.2021.108794⟩
Popis: International audience; Cross sections are crucial nuclear data that describe the probability for a particular nuclear reaction to occur between an incident particle and a target nuclide. In the context of neutron transport, accurate cross section calculations are notably crucial in that they provide the input for reactor physics and criticality safety calculations. Cross sections are computed from a large variety of models that sometimes involve experimental data, in particular to describe in detail the resonant shape of the cross sections at low energy. In this paper, the statistical contributions of compound nucleus resonances to the cross sections are investigated. This is particularly useful when artificial sets of statistical resonances must be sampled, eg. in the framework of the ladder method used to compute probability tables in the unresolved resonance range, where resonances are experimentally indistinguishable. The ladder method is a Monte Carlo based technique in which sets of resonances (called ladders) are sampled around reference energies in the unresolved resonance range from average resonance parameters. In this paper, a methodology is proposed to estimate the statistical weight of the resonances for cross sections calculations in the unresolved resonance range. This provides practical insights to determine the minimal number of resonances to be sampled in the unresolved resonance range, and the needed number of Monte Carlo histories. The methods of the present article can be extended to any physical problem based on a statistical sampling of nuclear resonances. In particular, the conclusions can be directly applied to nuclear data processing codes, to some evaluation techniques that require resonances sampling, and to Monte Carlo transport codes that handle the unresolved resonance range on-the-fly.
Databáze: OpenAIRE
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