Metrics for Diagnosing Undersampling in Monte Carlo Tally Estimates
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.
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Reactor and Nuclear Systems Div.
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- DOE Contract Number:
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- 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
- Research Org:
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
- Sponsoring Org:
- USDOE National Nuclear Security Administration (NNSA)
- Country of Publication:
- United States
- 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
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