Uncertainty Analyses for Localized Tallies in Monte Carlo Eigenvalue Calculations
- Oak Ridge Associated Universities (ORAU), Oak Ridge, TN (United States)
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
It is well known that statistical estimates obtained from Monte Carlo criticality simulations can be adversely affected by cycle-to-cycle correlations in the fission source. In addition, there are several other more fundamental issues that may lead to errors in Monte Carlo results. These factors can have a significant impact on the calculated eigenvalue, localized tally means and their associated standard deviations. In fact, modern Monte Carlo computational tools may generate standard deviation estimates that are a factor of five or more lower than the true standard deviation for a particular tally due to the inter-cycle correlations in the fission source. The magnitude of this under-prediction can climb as high as one hundred when combined with an ill-converged fission source or poor sampling techniques. Since Monte Carlo methods are widely used in reactor analysis (as a benchmarking tool) and criticality safety applications, an in-depth understanding of the effects of these issues must be developed in order to support the practical use of Monte Carlo software packages. A rigorous statistical analysis of localized tally results in eigenvalue calculations is presented using the SCALE/KENO-VI and MCNP Monte Carlo codes. The purpose of this analysis is to investigate the under-prediction in the uncertainty and its sensitivity to problem characteristics and calculational parameters, and to provide a comparative study between the two codes with respect to this under-prediction. It is shown herein that adequate source convergence along with proper specification of Monte Carlo parameters can reduce the magnitude of under-prediction in the uncertainty to reasonable levels; below a factor of 2 when inter-cycle correlations in the fission source are not a significant factor. In addition, through the use of a modified sampling procedure, the effects of inter-cycle correlations on both the mean value and standard deviation estimates can be isolated.
- Research Organization:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). LDRD Director's R&D; Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States); Oak Ridge Associated Universities (ORAU), Oak Ridge, TN (United States)
- Sponsoring Organization:
- USDOE National Nuclear Security Administration (NNSA), Nuclear Criticality Safety Program (NCSP)
- DOE Contract Number:
- AC05-00OR22725
- OSTI ID:
- 1019008
- Resource Relation:
- Conference: International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering (M&C 2011), Rio de Janeiro (Brazil), 8-12 May 2011
- Country of Publication:
- United States
- Language:
- English
Similar Records
Diagnosing undersampling in Monte Carlo eigenvalue and flux tally estimates - 14515
Variance Estimation in Monte Carlo Eigenvalue Simulations Using Spectral Analysis Method
Related Subjects
73 NUCLEAR PHYSICS AND RADIATION PHYSICS
CONVERGENCE
CRITICALITY
EIGENVALUES
FISSION
MONTE CARLO METHOD
SAFETY
SAMPLING
SENSITIVITY
SPECIFICATIONS
Nuclear Criticality Safety Program (NCSP)
Monte Carlo Software Packages Support
Monte Carlo Criticality Simulations
Statistical Estimates
Error Variability
Uncertainty Under-Prediction
Problem Characteristics Sensitivity
Calculational Parameters Sensitivity
Reductions
Inter-Cycle Correlations Isolation