Abstract Dynamic Monte Carlo (DMC) simulation of realistic nuclear reactors requires powerful variance reduction methods for even a few seconds of real time calculations. State-of-the-art numerical methods deal with the dynamic nature of the problem via successive Monte Carlo transport and TH (thermal-hydraulic) runs in a time step by time step manner. Such halting of the sample population at the beginning of time steps also allows for a joint handling of samples in a variance reduction effort. A theoretical framework is given for the connection of weight distribution and tally variance by factorizing it into a population variance accumulated by previous time steps and the variance caused by the transport process in the last interval. A long term importance function is proposed for decreasing the main contribution of a power release tally variance. Novel techniques are shown and compared when using Russian Roulette and Splitting. A simple fast critical assembly and a detailed thermal reactor geometry are used for testing showing that a factor of at least two orders of magnitude is to be gained by a simple population comb targeting the average weight. Further improvement using importance and variance functions is less than a factor two.

中文翻译：

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动态蒙特卡罗的基于种群的方差减少

摘要 现实核反应堆的动态蒙特卡罗 (DMC) 模拟需要强大的方差减少方法，即使是几秒钟的实时计算。最先进的数值方法通过连续的蒙特卡罗传输和 TH（热力水力）以时间步长的方式运行来处理问题的动态性质。在时间步长开始时样本总体的这种停止还允许在方差减少工作中联合处理样本。通过将权重分布和计数方差分解为前一时间步累积的总体方差和上一区间运输过程引起的方差，给出了权重分布与计数方差的联系的理论框架。提出了一个长期重要性函数来减少功率释放计数方差的主要贡献。使用俄罗斯轮盘赌和分裂时，将展示和比较新技术。一个简单的快速临界组件和一个详细的热反应器几何结构用于测试，表明通过针对平均重量的简单种群梳可以获得至少两个数量级的因子。使用重要性和方差函数的进一步改进小于两倍。