フルコア炉心核モンテカルロ中性子計算および計算効率解析における機械学習核データ精度の評価
Alexander Hashemi1, Rafael Macián-Juan2, Martin Ohlerich3
1Chair of Nuclear Engineering, Technical University of Munich (TUM), Garching, 85748, Munich, Germany. alexander.hashemi@tum.de.
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.
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