基于计数器数据分解的RMC全堆燃耗计算研究

Research of Full Core Burnup Calculations Based on Tally Data Decomposition in RMC

内存不足是蒙特卡罗方法大规模输运模拟的关键问题。对于反应堆燃耗分析,需在输运过程中统计大量反应截面数据,计算机内存限制了燃耗计算规模。本文基于反应堆蒙特卡罗程序(RMC),利用数据分解方法对计数器数据并行存储,并与点燃耗并行耦合,实现计数器数据分解和燃耗数据分解的综合并行方法。对全堆基准题进行数值测试,结果表明综合并行方法可明显降低计算内存,验证了数据分解对蒙特卡罗大规模燃耗分析的有效性。

Insufficient memory is the bottleneck problem for large scale transport simulation using Monte Carlo methods. When doing reactor burnup analysis, excessive reaction cross sections are required to be tallied in transport step, thereby the scale of depletion is restricted by the memory storage of computers. To address this problem, a combined parallel method is proposed and implemented in Reactor Monte Carlo code RMC. Tally data is distributed in parallel processes by using tally data decomposition algorithm, which is coupled with the parallel point depletion module. Full core benchmark tests are carried out. The results illustrate that the memory footprint are reduced evidently by using the combined parallel method. It is demonstrated that the data decomposition methods are effective to realize the full core burnup calculations.