Data decomposition method for full-core Monte Carlo transport–burnup calculation

Hong-Fei Liu; Peng Ge; Sheng-Peng Yu; Jing Song; Xiao-Lei Zheng

doi:10.1007/s41365-018-0366-4

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Data decomposition method for full-core Monte Carlo transport–burnup calculation

NUCLEAR ENERGY SCIENCE AND ENGINEERING | Updated：2021-02-07

- Data decomposition method for full-core Monte Carlo transport–burnup calculation
- Nuclear Science and Techniques Vol. 29, Issue 2, Article number: 20(2018)
- Affiliations：
  
  1.Key Laboratory of Neutronics and Radiation Safety, Institute of Nuclear Energy Safety Technology, Chinese Academy of Sciences, Hefei 230031, China
- Author bio：
  
  E-mail address: xiaolei.zheng@fds.org.cn
- Funds：
- DOI：10.1007/s41365-018-0366-4
  CLC：
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Hong-Fei Liu, Peng Ge, Sheng-Peng Yu, et al. Data decomposition method for full-core Monte Carlo transport–burnup calculation. [J]. Nuclear Science and Techniques 29(2):20(2018)
DOI：

Hong-Fei Liu, Peng Ge, Sheng-Peng Yu, et al. Data decomposition method for full-core Monte Carlo transport–burnup calculation. [J]. Nuclear Science and Techniques 29(2):20(2018) DOI： 10.1007/s41365-018-0366-4.

摘要

Abstract

Monte Carlo transport simulations of a full core reactor with a high-fidelity structure have been made possible by modern-day computing capabilities. Performing transport–burnup calculations of a full core model typically includes millions of burnup areas requiring hundreds of gigabytes of memory for burnup related tallies. This paper presents the study of a parallel computing method for full-core Monte Carlo transport–burnup calculations and the development of a thread-level data decomposition method. The proposed method decomposes tally accumulators into different threads and improves the parallel communication pattern and memory access efficiency. A typical PWR(Pressurized Water Reactor) burnup assembly along with the BEAVRS(Benchmark for Evaluation And Validation of Reactor Simulations) model was used to test the proposed method. The result indicates that the method effectively reduces memory consumption and maintains high parallel efficiency.

关键词

Keywords

Monte CarloBurnup CalculationData DecompositionBEAVRSSuperMC

references

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