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Title: Variance Estimation in Monte Carlo Eigenvalue Simulations Using Spectral Analysis Method

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  1. ORNL
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Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
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Conference: 2017 ANS Annual Meeting - San Francisco, Washington, United States of America - 6/11/2017 12:00:00 AM-6/15/2017 12:00:00 AM
Country of Publication:
United States

Citation Formats

BANERJEE, KAUSHIK . Variance Estimation in Monte Carlo Eigenvalue Simulations Using Spectral Analysis Method. United States: N. p., 2017. Web.
BANERJEE, KAUSHIK . Variance Estimation in Monte Carlo Eigenvalue Simulations Using Spectral Analysis Method. United States.
BANERJEE, KAUSHIK . Thu . "Variance Estimation in Monte Carlo Eigenvalue Simulations Using Spectral Analysis Method". United States. doi:.
title = {Variance Estimation in Monte Carlo Eigenvalue Simulations Using Spectral Analysis Method},
author = {BANERJEE, KAUSHIK .},
abstractNote = {},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Thu Jun 01 00:00:00 EDT 2017},
month = {Thu Jun 01 00:00:00 EDT 2017}

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  • Monte Carlo eigenvalue calculations using a fixed number of histories per generation are biased. Previous work has shown that the bias in eigenvalue is generally smaller than observed standard deviations. In this paper, the authors propose a method for bounding [delta][psi] the bias in the unnormalized fission source. In addition, the authors construct a plausibility argument suggesting that the bias is generally insignificant. Results presented are for discretized diffusion calculations based on a Monte Carlo-generated source. Additional calculations have been run for true transport problems using a very fast, but more conventional, Monte Carlo code.
  • CCFE perform Monte-Carlo transport simulations on large and complex tokamak models such as ITER. Such simulations are challenging since streaming and deep penetration effects are equally important. In order to make such simulations tractable, both variance reduction (VR) techniques and parallel computing are used. It has been found that the application of VR techniques in such models significantly reduces the efficiency of parallel computation due to 'long histories'. VR in MCNP can be accomplished using energy-dependent weight windows. The weight window represents an 'average behaviour' of particles, and large deviations in the arriving weight of a particle give rise tomore » extreme amounts of splitting being performed and a long history. When running on parallel clusters, a long history can have a detrimental effect on the parallel efficiency - if one process is computing the long history, the other CPUs complete their batch of histories and wait idle. Furthermore some long histories have been found to be effectively intractable. To combat this effect, CCFE has developed an adaptation of MCNP which dynamically adjusts the WW where a large weight deviation is encountered. The method effectively 'de-optimises' the WW, reducing the VR performance but this is offset by a significant increase in parallel efficiency. Testing with a simple geometry has shown the method does not bias the result. This 'long history method' has enabled CCFE to significantly improve the performance of MCNP calculations for ITER on parallel clusters, and will be beneficial for any geometry combining streaming and deep penetration effects. (authors)« less
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