Demonstrative Application to an OECD/NEA Reactor Physics Benchmark of the 2nd-BERRU-PM Method—II: Nominal Computations Apparently Inconsistent with Measurements

Ruixian Fang and Dan G. Cacuci ()
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Ruixian Fang: Center for Nuclear Science and Energy, Department of Mechanical Engineering, University of South Carolina, Columbia, SC 29208, USA
Dan G. Cacuci: Center for Nuclear Science and Energy, Department of Mechanical Engineering, University of South Carolina, Columbia, SC 29208, USA

Energies, 2023, vol. 16, issue 15, 1-8

Abstract: This work presents illustrative applications of the 2nd-BERRU-PM (second-order best-estimate results with reduced uncertainties predictive modeling) methodology to the leakage response of a polyethylene-reflected plutonium OECD/NEA reactor physics benchmark, which is modeled using the neutron transport Boltzmann equation. The 2nd-BERRU-PM methodology simultaneously calibrates responses and parameters while simultaneously reducing the predicted standard deviation values of these quantities. The situations analyzed in this work pertain to the values of measured responses that appear to be inconsistent with the computed response values, in that the standard deviation values of the measured responses do not initially overlap with the standard deviation values of the computed responses. It is shown that the inconsistency diminishes as higher-order sensitivities are progressively included, thus illustrating their significant impact. In all cases, the 2nd-BERRU-PM methodology yields predicted best-estimate standard deviation values that are smaller than both the computed and the experimentally measured values of the standard deviation for the model response under consideration.

Keywords: second-order predictive modeling methodology; 2nd-BERRU-PM; reduced uncertainties; maximum entropy; data assimilation; predictive modeling; high-order sensitivities (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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