Nuclear Engineering and Technology

Korean Nuclear Society (한국원자력학회)
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The methods of CADIS-NEE and CADIS-DXTRAN in NECP-MCX and their applications

  • Qingming He (School of Nuclear Science and Technology, Xi'an Jiaotong University) ;
  • Zhanpeng Huang (School of Nuclear Science and Technology, Xi'an Jiaotong University) ;
  • Liangzhi Cao (School of Nuclear Science and Technology, Xi'an Jiaotong University) ;
  • Hongchun Wu (School of Nuclear Science and Technology, Xi'an Jiaotong University)
  • Received : 2023.11.08
  • Accepted : 2024.02.18
  • Published : 2024.07.25
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Abstract

This paper presents two new methods for variance reduction for shielding calculation in Monte Carlo radiation transport. One method is CADIS-NEE, which combines Consistent Adjoint Driven Importance Sampling (CADIS) and next-event estimator (NEE) methods to increase the calculation efficiency of tallies at points. The other is CADIS-deterministic transport (DXTRAN), which combines CADIS and DXTRAN to obtain higher performance than using CADIS and DXTRAN separately. The combination processes are derived and implemented in the hybrid Monte-Carlo-Deterministic particle-transport code NECP-MCX. Various problems are tested to demonstrate the effectiveness of the two methods. According to the results, the two combination methods have higher efficiency than using CADIS, NEE or DXTRAN separately. In a long-distance photon-transport problem, CADIS-NEE converges faster than NEE and the figure of merit (FOM) of CADIS-NEE is 75.6 times of NEE. In a labyrinthine problem, CADIS-DXTRAN's FOM surpasses that of DXTRAN and CADIS by a factor of 45.3 and 17.7, respectively. Therefore, it is advisable to employ these two novel methods selectively in appropriate scenarios to reduce variance.

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Acknowledgement

This work was supported by the National Natural Science Foundation of China (No. U2067209), the Young Elite Scientists Sponsorship Program by CAST (2019QNRC001) and the Innovative Scientific Program of CNNC.

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