Metrics for Diagnosing Undersampling in Monte Carlo Tally Estimates

Conference · Thu Jan 01 00:00:00 EST 2015

OSTI ID:1221723

Perfetti, Christopher M. ^[1]; Rearden, Bradley T. ^[1]

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1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Reactor and Nuclear Systems Div.

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.

• Research Organization: Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA)
• DOE Contract Number: AC05-00OR22725
• OSTI ID: 1221723
• Resource Relation: Conference: Joint International Conference on Mathematics and Computation, Supercomputing in Nuclear Applications and the Monte Carlo Method, Nashville, TN (United States), 19-23 Apr 2015
• Country of Publication: United States
• Language: English

Related Subjects

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS
MONTE CARLO METHOD
PWR TYPE REACTORS
EIGENVALUES
COMPARATIVE EVALUATIONS
MARKOV PROCESS
DIAGNOSIS
FUEL ASSEMBLIES
CONVERGENCE
SAMPLING
ERRORS
ACCURACY
Monte Carlo
tally biases
undersampling
convergence metrics
SCALE