基于NECP-MCX的蒙特卡罗-确定论耦合及权窗网格粗化方法研究

对于屏蔽计算的深穿透问题，由于仅有少量粒子能够穿透屏蔽层到达计数区，所以计算效率极低。为解决该问题，基于一致性共轭驱动重要性抽样方法研究了蒙特卡罗-确定论耦合方法（简称耦合方法）。本研究实现的耦合方法可以基于蒙特卡罗的组合实体几何建模，自动生成确定论SN计算所需的输入参数，利用SN共轭计算生成一致的源偏倚和权窗参数，提供给蒙特卡罗方法正向计算使用。同时耦合方法使用的基于网格的权窗在大规模问题中会遇到内存瓶颈，本文基于贡献因子理论，研究了自动网格粗化方法。新开发了嵌套网格的结构用于网格粗化，以节省权窗占用的内存，同时不影响权窗的效果。基于NECP-MCX程序实现了耦合方法和网格粗化方法。数值结果表明，对于HBR-2基准题，相比于MCNP程序的权窗自动生成方法，耦合方法的品质因子最高提高了2个数量级。在不影响计算精度和效率的前提下，可以将权窗网格最多减少为原来的1/226。 

Research on Hybrid Monte-Carlo-Deterministic and Weight-Window Mesh-Coarsening Method Based on NECP-MCX

In the Monte Carlo simulation of a deep-penetration problem, only a small number of particles can penetrate the shielding layer and reach the target region, resulting in a very low computational efficiency. In order to solve the deep-penetration problem, the hybrid Monte-Carlo-Deterministic method is studied based on the consistent adjoint driven importance sampling method(CADIS). The hybrid method can automatically generate the input parameters required for deterministic SN calculation from the constructive solid geometry in the Monte-Carlo modeling. The adjoint SN calculation is used to generate consistent source biasing and weight-window parameters for forward calculation of the Monte-Carlo method. On the other hand, the mesh-based weight window applied in the hybrid method will encounter memory bottleneck in large-scale problems. A new structure of nested mesh is developed for mesh coarsening to save the memory of the weight-window parameters. The coarse mesh does not affect the variance reduction effect of the importance sampling. Based on NECP-MCX code system, the hybrid method and mesh coarsening method are implemented. The numerical results of HBR-2 benchmark show that the figure of merit(FOM) of the hybrid method is up to two orders of magnitude higher than that of MCNP. The weight window mesh can be reduced by 226 times without affecting the accuracy and efficiency of the final results.