Evaluating machine learned nuclear data precision in full core nuclear reactor Monte Carlo neutronics and computational efficiency analyses.

Abstract

This study evaluates the novel machine learning based reduction of cross-sections and energy grid of continuous-energy nuclear data for one year full core Monte Carlo criticality and burn-up analysis using OpenMC. The approach modifies OpenMC's ENDF/B-VII.1 Hierarchical Data Format, version 5 (HDF5) nuclear data files, retaining ∼10% to 50% of nuclear data for 23 nuclides while preserving thresholds and resonances. EPR and VVER-1000 full core models benchmark reduced nuclear data library against the original (windowed multipole disabled), to quantify performance and fidelity. Wall time decreased by 17.81% in EPR and 42.5% in VVER-1000. Peak memory (MaxRSS) decreased by 4.4% in EPR and increased by 5.0% in VVER-1000. The maximum absolute difference in [Formula: see text] for VVER-1000 remains within 96.79 pcm at all times. VVER-1000 end of cycle reaction rates relative differences found for U-235 [Formula: see text] 0.0017%, U-238 [Formula: see text] 0.0605%, Xe-135 [Formula: see text] 0.0128%, Sm-149 [Formula: see text] 0.03%. Inventories EOC relative difference were 0.0039% U-235, 0.0003% U-238, 0.0135% Xe-135, 0.0341% Sm-149. The EOC relative difference for the Plutonium vector has been analyzed. Results prove that the developed reduction method accelerates full core analysis, reduces MaxRSS while maintaining fidelity in neutronics studies.