Abstract Monte Carlo (MC) shielding calculations often use weight windows (WWs) and biased sources formed from a deterministic estimate of the adjoint flux to improve the convergence rate of tallies. This requires a significant amount of computer memory, which can limit the memory available for high-resolution tally output. A new method is proposed for reducing these memory requirements by using singular value decomposition (SVD) in linear or logarithmic space to approximate the adjoint flux. This method’s performance is evaluated using the Shift and Denovo codes for streaming and diffusion base case problems, followed by problems using the Westinghouse AP1000 and the Joint European Torus. The log SVD reduced WW memory requirements by an order of magnitude in all cases without a significant performance penalty. Additionally, the linear SVD reduced biased source memory requirements by an order of magnitude, but further investigation is needed to account for observed limitations.

中文翻译：

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用于蒙特卡罗方差减少的伴随通量分布的奇异值分解

摘要 蒙特卡罗 (MC) 屏蔽计算通常使用权重窗口 (WW) 和由伴随通量的确定性估计形成的偏置源，以提高计数的收敛速度。这需要大量的计算机内存，这会限制可用于高分辨率计数输出的内存。提出了一种新方法，通过在线性或对数空间中使用奇异值分解 (SVD) 来近似伴随通量来减少这些内存需求。该方法的性能使用 Shift 和 Denovo 代码来评估流和扩散基本案例问题，然后是使用西屋 AP1000 和联合欧洲环面的问题。log SVD 在所有情况下都将 WW 内存需求降低了一个数量级，而没有显着的性能损失。此外，