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

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

Energies, 2023, vol. 16, issue 14, 1-18

Abstract: This work illustrates the applications of the 2nd-BERRU-PM methodology to polyethylene-reflected plutonium (PERP) OECD/NEA reactor physics benchmark. Using concepts from information theory and thermodynamics, the 2nd-BERRU-PM (2nd-order best-estimate results with reduced uncertainties predictive modeling) methodology is constructed in the most inclusive “joint-phase-space of parameters, computed and measured responses”. Consequently, the 2nd-BERRU-PM methodology simultaneously calibrates responses and parameters, while simultaneously reducing the predicted standard deviations in these quantities. This uncertainty reduction is illustrated for the PERP benchmark, which is modeled using the neutron transport Boltzmann equation, the solution of which is representative of “large-scale computations”. The situations analyzed in this work pertain to values of the measured responses which are consistent with the computed response values, in that the standard deviations of the measured responses overlap with the standard deviations of the computed responses. The situations that can arise when the measured values appear to be inconsistent with the computed values will be analyzed in the accompanying Part II.

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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