Nuclear Engineering and Technology

Korean Nuclear Society (한국원자력학회)
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Implementation and benchmarking of the local weight window generation function for OpenMC

  • Hu, Yuan (State Key Laboratory of Nuclear Physics and Technology, School of Physics, Peking University) ;
  • Yan, Sha (State Key Laboratory of Nuclear Physics and Technology, School of Physics, Peking University) ;
  • Qiu, Yuefeng (Institute for Neutron Physics and Reactor Technology, Karlsruhe Institute of Technology)
  • Received : 2022.01.18
  • Accepted : 2022.04.25
  • Published : 2022.10.25
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Abstract

OpenMC is a community-driven open-source Monte Carlo neutron and photon transport simulation code. The Weight Window Mesh (WWM) function and an automatic Global Variance Reduction (GVR) method was recently developed and implemented in a developmental branch of OpenMC. This WWM function and GVR method broaden OpenMC's usage in general purposes deep penetration shielding calculations. However, the Local Variance Reduction (LVR) method, which suits the source-detector problem, is still missing in OpenMC. In this work, the Weight Window Generator (WWG) function has been developed and benchmarked for the same branch. This WWG function allows OpenMC to generate the WWM for the source-detector problem on its own. Single-material cases with varying shielding and sources were used to benchmark the WWG function and investigate how to set up the particle histories utilized in WWG-run and WWM-run. Results show that there is a maximum improvement of WWM generated by WWG. Based on the above results, instructions on determining the particle histories utilized in WWG-run and WWM-run for optimal computation efficiency are given and tested with a few multi-material cases. These benchmarks demonstrate the ability of the OpenMC WWG function and the above instructions for the source-detector problem. This developmental branch will be released and merged into the main distribution in the future.

Keywords

Acknowledgement

Part of the analysis was performed on the High Performance Computing Platform of the Center for Life Science (Peking University).

References

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