Mesh-free Semi-quantitative Variance Underestimation Elimination Method in Monte Caro algorithm

Peng-Fei Shen; Xiao-Dong Huo; Ze-Guang Li; Zeng Shao; Hai-Feng Yang; Peng Zhang; Kan Wang

doi:10.1007/s41365-022-01156-1

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Mesh-free Semi-quantitative Variance Underestimation Elimination Method in Monte Caro algorithm

NUCLEAR ENERGY SCIENCE AND ENGINEERING | Updated：2023-02-13

- Mesh-free Semi-quantitative Variance Underestimation Elimination Method in Monte Caro algorithm
- Nuclear Science and Techniques Vol. 34, Issue 1, Article number: 14(2023)
- Affiliations：
  
  1.Department of Engineering Physics, Tsinghua University, Beijing 100084, China
  2.China Nuclear Power Engineering Corporation, Beijing 100840, China
- Author bio：
  
  * lizeguang@mail.tsinghua.edu.cn
- Funds：
- DOI：10.1007/s41365-022-01156-1
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Peng-Fei Shen, Xiao-Dong Huo, Ze-Guang Li, et al. Mesh-free Semi-quantitative Variance Underestimation Elimination Method in Monte Caro algorithm. [J]. Nuclear Science and Techniques 34(1):14(2023)
DOI：

Peng-Fei Shen, Xiao-Dong Huo, Ze-Guang Li, et al. Mesh-free Semi-quantitative Variance Underestimation Elimination Method in Monte Caro algorithm. [J]. Nuclear Science and Techniques 34(1):14(2023) DOI： 10.1007/s41365-022-01156-1.

摘要

Abstract

The inter-cycle correlation of fission source distributions (FSDs) in the Monte Carlo Power Iteration process results in variance underestimation of tallied physical quantities, especially in large local tallies. This study provides a mesh-free Semi-quantitative Variance Underestimation Elimination (SeVUE) method to obtain a credible confidence interval for the tallied results. This method comprises two procedures: Estimation and Elimination. The FSD inter-cycle correlation length is estimated in the Estimation procedure using the Sliced Wasserstein distance algorithm. The batch method was then used in the Elimination procedure. The FSD inter-cycle correlation length was proved to be the optimum batch length to eliminate the variance underestimation problem. We exemplified this method using the OECD sphere array model and 3D PWR BEAVRS model. The results showed that the average variance underestimation ratios of local tallies declined from 37% and 87% to within ±5% in these models.

关键词

Keywords

Monte Carlo algorithmPower Iteration processInter-cycle correlationVariance UnderestimationSliced Wasserstein distance

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