Singular value decomposition of adjoint flux distributions for Monte Carlo variance reduction

Biondo, Elliott; Evans, Thomas; Davidson, Gregory; Hamilton, Steven P.

doi:10.1016/j.anucene.2020.107327

Singular value decomposition of adjoint flux distributions for Monte Carlo variance reduction

Journal Article · Tue Jan 21 00:00:00 EST 2020 · Annals of Nuclear Energy (Oxford)

DOI:https://doi.org/10.1016/j.anucene.2020.107327· OSTI ID:1606986

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Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

Monte Carlo (MC) shielding calculations often use weight windows (WWs) and biased sources formed from a deterministic estimate of the adjoint flux to improve the convergence rate of tallies. This requires a significant amount of computer memory, which can limit the memory available for high-resolution tally output. A new method is proposed for reducing these memory requirements by using singular value decomposition (SVD) in linear or logarithmic space to approximate the adjoint flux. This method’s performance is evaluated using the Shift and Denovo codes for streaming and diffusion base case problems, followed by problems using the Westinghouse AP1000 and the Joint European Torus. The log SVD reduced WW memory requirements by an order of magnitude in all cases without a significant performance penalty. Additionally, the linear SVD reduced biased source memory requirements by an order of magnitude, but further investigation is needed to account for observed limitations.

View Accepted Manuscript (DOE)

Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 1606986

Journal Information:: Annals of Nuclear Energy (Oxford), Journal Name: Annals of Nuclear Energy (Oxford) Journal Issue: C Vol. 141; ISSN 0306-4549

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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73 NUCLEAR PHYSICS AND RADIATION PHYSICS
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Singular value decomposition of adjoint flux distributions for Monte Carlo variance reduction

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