Metrics for diagnosing undersampling in Monte Carlo tally estimates

Christopher M. Perfetti, Bradley T. Rearden

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This study explored the potential of using Markov chain convergence diagnostics to predict the prevalence and magnitude of biases due to undersampling in Monte Carlo eigenvalue and flux tally estimates. Five metrics were applied to two models of pressurized water reactor fuel assemblies and their potential for identifying undersampling biases was evaluated by comparing the calculated test metrics with known biases in the tallies. Three of the five undersampling metrics showed the potential to accurately predict the behavior of undersampling biases in the responses examined in this study.

Original languageEnglish
Title of host publicationMathematics and Computations, Supercomputing in Nuclear Applications and Monte Carlo International Conference, M and C+SNA+MC 2015
PublisherAmerican Nuclear Society
Pages268-280
Number of pages13
ISBN (Electronic)9781510808041
StatePublished - 2015
EventMathematics and Computations, Supercomputing in Nuclear Applications and Monte Carlo International Conference, M and C+SNA+MC 2015 - Nashville, United States
Duration: Apr 19 2015Apr 23 2015

Publication series

NameMathematics and Computations, Supercomputing in Nuclear Applications and Monte Carlo International Conference, M and C+SNA+MC 2015
Volume1

Conference

ConferenceMathematics and Computations, Supercomputing in Nuclear Applications and Monte Carlo International Conference, M and C+SNA+MC 2015
Country/TerritoryUnited States
CityNashville
Period04/19/1504/23/15

Keywords

  • Convergence metrics
  • Monte Carlo
  • SCALE
  • Tally biases
  • Undersampling

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