Predicting correlation coefficients for Monte Carlo eigenvalue simulations with multitype branching process
Name
Forget_Predicting Correlation Coefficients for Monte Carlo Eigenvalue.pdf
Description
Accepted version
Size
2.13 MB
Format
Adobe PDF
Checksum (MD5)
a0f2d824ca3927f71df010c17b8e1d2f
Author(s)
Miao, Jilang
Forget, Benoit
Smith, Kord
Date Issued
2018
Journal
Annals of Nuclear Energy
Publisher
Elsevier BV
Version
Author's final manuscript
Abstract
© 2017 Elsevier Ltd This paper provides a prediction method of the generation-to-generation correlations as observed when solving large scale eigenvalue problems such as full core nuclear reactor simulations. Knowing the correlations enables correction of the variance underestimation that occurs when assuming that the active generations are independent. The Monte Carlo power iteration is cast in the Multitype Branching Process (MBP) framework by discretizing the neutron phase space which allows calculation of spatial and temporal moments. These moments can then provide auto-correlation coefficients between the generations of MBP and are shown to accurately predict the auto-correlation coefficients of the original Monte Carlo simulation. This prediction capability was demonstrated on the full core 2D PWR BEAVRS benchmark and compared successfully with variance estimates from independent simulations.
MIT Department
Massachusetts Institute of Technology. Department of Nuclear Science and Engineering
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/J.ANUCENE.2017.10.014
