On the use of integral experiments for uncertainty reduction of reactor macroscopic parameters within the TMC methodology
2016 (English)In: Progress in nuclear energy (New series), ISSN 0149-1970, E-ISSN 1878-4224, Vol. 88, 43-52 p.Article in journal (Refereed) Published
The current nuclear data uncertainties observed in reactor safety parameters for some nuclides call for safety concerns especially with respect to the design of GEN-IV reactors and must therefore be reduced significantly. In this work, uncertainty reduction using criticality benchmark experiments within the Total Monte Carlo methodology is presented. Random nuclear data libraries generated are processed and used to analyze a set of criticality benchmarks. Since the calculated results for each random nuclear data used are different, an algorithm was used to select (or assign weights to) the libraries which give a good description of experimental data for the analyses of the benchmarks. The selected or weighted libraries were then used to analyze the ELECTRA reactor. By using random nuclear data libraries constrained with only differential experimental data as our prior, the uncertainties observed were further reduced by constraining the files with integral experimental data to obtain a posteriori uncertainties on the k(eff). Two approaches are presented and compared: a binary accept/reject and a method of assigning file weights based on the likelihood function. Significant reductions in (PU)-P-239 and Pb-208 nuclear data uncertainties in the k(eff) were observed after implementing the two methods with some criticality benchmarks for the ELELIRA reactor. (C) 2015 Elsevier Ltd. All rights reserved.
Place, publisher, year, edition, pages
2016. Vol. 88, 43-52 p.
Nuclear data, uncertainty reduction, binary accept/reject, file weights, Total Monte Carlo, criticality benchmarks, ELECTRA
Research subject Physics with specialization in Applied Nuclear Physics
IdentifiersURN: urn:nbn:se:uu:diva-264410DOI: 10.1016/j.pnucene.2015.11.015ISI: 000372564400006OAI: oai:DiVA.org:uu-264410DiVA: diva2:860196
FunderSwedish Research Council