Analysis of Full-Core Calculation of RMC
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摘要: 使用清华大学的"探索100"高性能并行计算机,基于美国核能署数据中心的连续能量全堆基准计算模型和法国电力集团的多群全堆基准计算模型,就通用蒙特卡罗程序(MCNP)全堆大规模并行计算开展了研究。针对堆用蒙特卡罗程序(RMC)与MCNP的全堆计算性能进行系统的比较研究。结果表明,MCNP在并行模式和计数器性能等方面均有不足,这些不足严重影响MCNP在反应堆全堆计算上的效率。而RMC在这些问题上取得了较大的改善,能够适用于反应堆全堆精细功率密度计算。因而,在反应堆全堆计算性能上,RMC优于MCNP。Abstract: With the Inspur TS1000 HPC Server of Tsinghua University,a lot of calculations have been done based on the NEA Data Bank full-core benchmark model and EDF 3D pressurized water reactor(PWR) full-core calculations through large-scale paralleling.The performance of MCNP which is the widely used general Monte Carlo code and RMC which is used for reactor analysis and developed by Tsinghua University is compared systematically.It is found that MCNP is unable to calculate the local power density of full-core reactors at the required accuracy because its limits on the parallel model,the performance of tallying and so on,while RMC is well-suited owing to its improvement on those limits.Thus,it can be concluded that the performance of RMC is better than MCNP in terms of detailed power density distributions calculation in full-core reactors.
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Key words:
- RMC /
- MCNP /
- Full-core Calculation /
- Large-scale Parallel
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