基于RMC的计数器数据分解方法研究

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Research on Tally Data Decomposition Algorithms Based on Reactor Monte Carlo Code RMC

1. Department of Engineering Physics, Tsinghua University, Beijing, 100084, China;
2. Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing, 100084, China;
3. Science and Technology on Reactor System Design Technology Laboratory, Nuclear Power Institute of China, Chengdu, 610041, China

摘要: 蒙特卡罗模拟方法(蒙卡方法)在反应堆物理分析中的应用受计算机内存不足的限制,数据分解方法是一种有效的解决思路。对蒙卡方法的内存占用进行定量分析,并基于自主堆用蒙特卡罗程序(RMC),采取了同步式和异步式2种通信方法,设计并实现计数器数据分解算法;通过数值试验测试算法的性能,结果表明,计数器数据分解算法能够明显减少内存占用,而且不会对程序的并行性能产生影响。
- 数据分解 /
- 计数器 /
- 蒙特卡罗 /
- 内存 /
- RMC /
- 并行效率
Abstract: The applications of Monte Carlo method in reactor physics analysis are restricted due to excessive memory demand in solving large-scale problems, while data decomposition is supposed to be a remedy. In this paper quantitative memory requirements in MC simulation are analyzed. Two types of tally data decomposition algorithms, which utilize synchronous and asynchronous communications, are designed and implemented in Reactor Monte Carlo code（RMC）. Numerical tests are carried out to evaluate performance of new algorithms. It is shown that tally data decomposition algorithm can reduce memory size effectively while parallel efficiency of the code is not affected.
- Data decomposition /
- Tally /
- Monte Carlo /
- Memory /
- RMC /
- Parallel efficiency