Capturing and utilizing the random feature in Monte Carlo fission source distributions

•Three mesh-free SW-based methods are proposed to capture the FSD random error term’s 1m feature.•A mesh-free source convergence auto-diagnosis algorithm is introduced utilizing the 1m feature.•The algorithm can automatically save 58% to 81% of the computational time to reach a converged fission sou...

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Published inAnnals of nuclear energy Vol. 180; p. 109468
Main Authors Shen, Pengfei, Huo, Xiaodong, Huang, Shanfang, Guo, Yuchuan, Shao, Zeng, Yang, Haifeng, Wang, Kan
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 01.01.2023
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Online AccessGet full text
ISSN0306-4549
1873-2100
DOI10.1016/j.anucene.2022.109468

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Abstract •Three mesh-free SW-based methods are proposed to capture the FSD random error term’s 1m feature.•A mesh-free source convergence auto-diagnosis algorithm is introduced utilizing the 1m feature.•The algorithm can automatically save 58% to 81% of the computational time to reach a converged fission source in models with a dominance ratio of around 0.99. The Monte Carlo algorithm commonly adopts the power iteration (PI) process in criticality calculations. Previous studies have shown that random error term in the PI process significantly influences the source convergence diagnosis and variance underestimation phenomenon. This paper uses the Sliced Wasserstein (SW) distance of the fission source distributions (FSDs) to estimate the error terms in the PI process. Three mesh-free SW-based methods are proposed to capture the random error term's 1m feature for the OECD source convergence fissile slab model, sphere array model, and the BEAVRS model. Then, a mesh-free source convergence auto-diagnosis algorithm is introduced based on the methods. The algorithm sets the batch size at five stages. The 1m feature of the random error term is utilized to achieve auto-diagnosis at the target batch size. Numerical calculation results show that the auto-diagnosis algorithm is practical and efficient for source convergence diagnosis and acceleration, which can automatically save 58 % to 81 % of the computational time to reach a converged fission source in models with the dominance ratio of around 0.99.
AbstractList •Three mesh-free SW-based methods are proposed to capture the FSD random error term’s 1m feature.•A mesh-free source convergence auto-diagnosis algorithm is introduced utilizing the 1m feature.•The algorithm can automatically save 58% to 81% of the computational time to reach a converged fission source in models with a dominance ratio of around 0.99. The Monte Carlo algorithm commonly adopts the power iteration (PI) process in criticality calculations. Previous studies have shown that random error term in the PI process significantly influences the source convergence diagnosis and variance underestimation phenomenon. This paper uses the Sliced Wasserstein (SW) distance of the fission source distributions (FSDs) to estimate the error terms in the PI process. Three mesh-free SW-based methods are proposed to capture the random error term's 1m feature for the OECD source convergence fissile slab model, sphere array model, and the BEAVRS model. Then, a mesh-free source convergence auto-diagnosis algorithm is introduced based on the methods. The algorithm sets the batch size at five stages. The 1m feature of the random error term is utilized to achieve auto-diagnosis at the target batch size. Numerical calculation results show that the auto-diagnosis algorithm is practical and efficient for source convergence diagnosis and acceleration, which can automatically save 58 % to 81 % of the computational time to reach a converged fission source in models with the dominance ratio of around 0.99.
ArticleNumber 109468
Author Huo, Xiaodong
Wang, Kan
Yang, Haifeng
Shen, Pengfei
Huang, Shanfang
Guo, Yuchuan
Shao, Zeng
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Keywords Source convergence diagnosis
Sliced Wasserstein distance
Monte Carlo
Power Iteration method
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Snippet •Three mesh-free SW-based methods are proposed to capture the FSD random error term’s 1m feature.•A mesh-free source convergence auto-diagnosis algorithm is...
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StartPage 109468
SubjectTerms Monte Carlo
Power Iteration method
Sliced Wasserstein distance
Source convergence diagnosis
Title Capturing and utilizing the random feature in Monte Carlo fission source distributions
URI https://dx.doi.org/10.1016/j.anucene.2022.109468
Volume 180
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