SN共轭函数用于蒙特卡罗粒子输运自动减方差的研究

基于S_N输运计算平台ARES编制了三维共轭输运计算模块，根据一致性共轭驱动重要性抽样方法自动生成减方差参数，用于加速MCNP5计算.数值结果表明，自动生成的减方差参数可有效提高蒙特卡罗计算效率，并保证结果无偏.自动减方差技术利用S_N共轭函数可更经济准确的估计粒子重要性，避免手动估算减方差参数的复杂工作，对于复杂屏蔽问题的蒙特卡罗计算具有较好的应用前景.

Application of SN Adjoint Function on Automated Variance Reduction for Monte Carlo Particle Transport Calculation

Three-dimensional adjoint transport calculation module was integrated into S_N transport code ARES in which automated variance reduction parameters are generated based on consistent adjoint driven importance sampling method to accelerate calculation of MCNP5. It shows that automated variance reduction parameters are effective to improve MC calculational efficiency and to produce unbiased statistical results. Automated variance reduction technique with S_N function estimates particle importance more economically and accurately. It avoids obstacles of manual estimation and could be applied for MC simulation of large-scale, complicated shielding problems.