Variance Estimation in Monte Carlo Eigenvalue Simulations Using Spectral Analysis Method
- Texas A and M University - Corpus Christi (United States)
- Oak Ridge National Laboratory (United States)
The Monte Carlo (MC) method is the 'gold standard' for performing nuclear reactor calculations and criticality safety analyses because of its ability to handle complex geometries and physics. Reactor and criticality safety analyses require solving eigenvalue problems in complex nuclear systems. Once a stationary fission source distribution is obtained in MC simulations, the sample mean of many stationary cycles is calculated. The MC simulation sample mean is typically referred to as a tally. Variance, or standard deviation, of the sample mean/tally is needed to indicate the level of statistical uncertainty in the simulation and to understand the convergence of the sample mean. Current MC codes typically use sample variance to estimate the statistical uncertainty of the simulation and assume that the MC stationary cycles are independent. However, it is known that cycles are correlated, and estimators of the variance that ignore these correlations systematically underestimate the variance. In this paper, we propose a novel method in the Fourier frequency domain to estimate the variance of the sample mean in MC eigenvalue calculations. Results indicate that the proposed estimator can be unbiased compared to the real variance. The proposed estimator requires minimal additional computing effort.
- OSTI ID:
- 23047476
- Journal Information:
- Transactions of the American Nuclear Society, Vol. 116; Conference: 2017 Annual Meeting of the American Nuclear Society, San Francisco, CA (United States), 11-15 Jun 2017; Other Information: Country of input: France; 6 refs.; available from American Nuclear Society - ANS, 555 North Kensington Avenue, La Grange Park, IL 60526 (US); ISSN 0003-018X
- Country of Publication:
- United States
- Language:
- English
Similar Records
Adaptive Reweighted Variance Estimation for Monte Carlo Eigenvalue Simulations
Variance Estimation in Monte Carlo Eigenvalue Simulations Using Spectral Analysis Method