非定常输运模拟中基于粒子标识分类的源偏倚算法

在非定常输运问题的多步蒙特卡罗模拟中，根据粒子的不同属性进行标识分类可以得到非常细致的系统相关标识物理量.对于某些目标标识物理量，模拟的样本中造成有效贡献的样本相对较少，导致这些物理量模拟结果的涨落较大，而单靠增加总样本数不能高效地使有效样本达到一个合理的水平.本文提出一种基于标识分类的源偏倚算法，将增加的所有样本定向赋予目标类粒子，从而高效地降低目标标识物理量的统计误差且不影响非目标标识物理量的计算.以一维多层介质非定常输运模型验证上述结论.

Particle-Flag Based Source Bias Algorithm for Simulating Time-Dependent Particle Transport

In multi-step Monte Carlo simulation of time-dependent particle transport problems, particle-flag based physical quantity can be calculated by appropriate classification of diverse particle's attributes. Some particle-flag based physical quantities' fluctuation are strong since only very small fraction of total histories can make non-zero contribution and it is inefficient to deal with this problem by increasing purely total history number. A source bias algorithm is proposed to decrease stochastic error of target quantity by increasing number of source particle with a specific type only. Meanwhile, precision of non-target quantities are hardly decreased. A one-dimensional multi-layer model is utilized to display effect of the method.