کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
1727924 1521107 2016 7 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Real variance analysis of Monte Carlo eigenvalue calculation by McCARD for BEAVRS benchmark
ترجمه فارسی عنوان
تجزیه و تحلیل واریانس واقعی و محاسبات مقادیر ویژه مونت کارلو توسط McCard برای معیار BEAVRS
کلمات کلیدی
McCard؛ واریانس واقعی؛ واریانس ظاهری؛ BEAVRS؛ روش دسته ای مبتنی بر تاریخ
موضوعات مرتبط
مهندسی و علوم پایه مهندسی انرژی مهندسی انرژی و فناوری های برق
چکیده انگلیسی


• The real variances of local tallies are estimated for BEAVRS using McCARD.
• The history-based batch method predicts the real variance best.
• The real to apparent SD ratio of FA-wise tally is larger than that of pin-wise tally.
• The correlation coefficients between the local tallies are investigated.
• The positive pin power correlation in a FA is the cause of large real SD of FA power.

The real variances of local tallies, such as the pin-wise and assembly-wise fission powers, were estimated for the Benchmark for Evaluation and Validation of Reactor Simulations (BEAVRS) fresh core problem using real variance estimation methods implemented in the Seoul National University Monte Carlo (MC) code, McCARD. This code employs Gelbard’s batch method, Ueki’s method, the fission-source distribution inter-cycle correlation method, and the history-based batch method. Results show that the estimated apparent variances of the local tallies tend to be smaller than the real one, whereas the apparent variance of a global MC tally such as the effective multiplication factor is similar to the real one. Moreover, it was observed that the real-to-apparent standard deviation (SD) ratio of the assembly-wise fission power is larger than that of the pin-wise fission power. The large real-to-apparent SD ratio of the former is explained by considering the correlation coefficients between the local tallies.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Annals of Nuclear Energy - Volume 90, April 2016, Pages 205–211
نویسندگان
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