全局减方差方法的HBR-2基准题应用

Global Variance Reduction Method Applied to HBR-2 Benchmark

  • 摘要: 对于深穿透类型的屏蔽问题,在合理的时间内计算得到可信的结果对于蒙特卡罗(MC)方法是一个巨大的挑战。基于离散纵标(SN)方法的局部和全局减方差方法能有效降低MC计算深穿透问题的计数误差。本文基于HBR-2基准题比较了全局减方差方法和局部减方差方法的计算效率。结果表明,对于HBR-2基准题,局部和全局减方差方法均取得了较好的结果。全局减方差方法1次计算即可同时优化辐照监督管和堆外探测器的计数,因此实际应用更加方便和高效。

     

    Abstract: For deep-penetration shielding problem, it is a great challenge to obtain reliable results with Monte Carlo (MC) method in reasonable time. Local variance reduction (LVR) method and global variance reduction (GVR) method based on discrete ordinate (SN) method can decrease tally error of deep-penetration problem in MC calculation. Calculation efficiencies of LVR method and GVR method for the HBR-2 benchmark were compared in this paper. Numerical results show that LVR method and GVR method both obtain satisfied results for the HBR-2 benchmark. GVR method can optimize both tallies of radiation surveillance capsule and ex-core detector at once, therefore it is more convenient and efficient.

     

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