Abstract Performance enhancement of the spectral analysis method (SAM) for evaluating the real variance of local tallies from the partial current–based coarse-mesh finite difference (p-CMFD) feedback is verified and explained. In the SAM, on successive Monte Carlo (MC) cycles, the real variance is obtained from the cyclewise samples instead of an explicit evaluation of covariance. However, if the cycle correlation is strong, there is a bias and variance trade-off in the evaluated true uncertainty. This study shows that the p-CMFD feedback reduces the cycle covariance and hence eliminates the trade-off. A one-dimensional slab reactor and a three-dimensional simplified BEAVRS benchmark problem are analyzed, and the real standard deviation of the local tally is estimated from the SAM and compared with that from the conventional multibatch method. It is shown that the SAM with p-CMFD feedback can accurately calculate the real uncertainty without changing the MC algorithm and incurring computation burden.

中文翻译：

---

使用谱分析方法通过 p-CMFD 反馈加速蒙特卡罗临界计算中的实方差估计

摘要 验证并解释了频谱分析方法 (SAM) 的性能增强，用于从基于部分电流的粗网格有限差分 (p-CMFD) 反馈评估局部计数的真实方差。在 SAM 中，在连续的蒙特卡罗 (MC) 循环中，实际方差是从循环样本中获得的，而不是对协方差的显式评估。但是，如果周期相关性很强，则在评估的真实不确定性中存在偏差和方差的权衡。该研究表明 p-CMFD 反馈降低了循环协方差，从而消除了权衡。分析了一个一维平板反应器和一个三维简化的 BEAVRS 基准问题，并从 SAM 估计了局部计数的真实标准偏差，并与传统的多批次方法进行了比较。