On the use of shannon entropy of the fission distribution for assessing convergence of Monte Carlo criticality calculations
Abstract
Monte Carlo calculations of k-eigenvalue problems are based on a power iteration procedure. To obtain correct results free of contamination from the initial guess for the fission distribution, it is imperative to determine when the iteration procedure has converged, so that a sufficient number of the initial batches are discarded prior to beginning the Monte Carlo tallies. In this paper, we examine the convergence behavior using both theory and numerical testing, demonstrating that k{sub eff} may converge before the fission distribution for problems with a high dominance ratio. Thus, it is necessary to assess convergence of both k{sub eff} and the fission distribution to obtain correct results. To this end, the Shannon entropy of the fission distribution has been found to be a highly effective means of characterizing convergence of the fission distribution. The latest version of MCNP5 includes new capabilities for computing and plotting the Shannon entropy of the fission distribution as an important new tool for assessing problem convergence. Examples of the application of this new tool are presented for a variety of practical criticality problems. (authors)
- Authors:
-
- Los Alamos National Laboratory, MS F663, PO Box 1663, Los Alamos, NM 87544 (United States)
- Publication Date:
- Research Org.:
- American Nuclear Society, 555 North Kensington Avenue, La Grange Park, IL 60526 (United States)
- OSTI Identifier:
- 22039478
- Resource Type:
- Conference
- Resource Relation:
- Conference: PHYSOR-2006: American Nuclear Society's Topical Meeting on Reactor Physics - Advances in Nuclear Analysis and Simulation, Vancouver, BC (Canada), 10-14 Sep 2006; Other Information: Country of input: France; 8 refs.
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 97 MATHEMATICAL METHODS AND COMPUTING; 22 GENERAL STUDIES OF NUCLEAR REACTORS; COMPUTERIZED SIMULATION; CONVERGENCE; CRITICALITY; DISTRIBUTION; EIGENVALUES; ENTROPY; FISSION; MONTE CARLO METHOD; MULTIPLICATION FACTORS
Citation Formats
Brown, F B. On the use of shannon entropy of the fission distribution for assessing convergence of Monte Carlo criticality calculations. United States: N. p., 2006.
Web.
Brown, F B. On the use of shannon entropy of the fission distribution for assessing convergence of Monte Carlo criticality calculations. United States.
Brown, F B. 2006.
"On the use of shannon entropy of the fission distribution for assessing convergence of Monte Carlo criticality calculations". United States.
@article{osti_22039478,
title = {On the use of shannon entropy of the fission distribution for assessing convergence of Monte Carlo criticality calculations},
author = {Brown, F B},
abstractNote = {Monte Carlo calculations of k-eigenvalue problems are based on a power iteration procedure. To obtain correct results free of contamination from the initial guess for the fission distribution, it is imperative to determine when the iteration procedure has converged, so that a sufficient number of the initial batches are discarded prior to beginning the Monte Carlo tallies. In this paper, we examine the convergence behavior using both theory and numerical testing, demonstrating that k{sub eff} may converge before the fission distribution for problems with a high dominance ratio. Thus, it is necessary to assess convergence of both k{sub eff} and the fission distribution to obtain correct results. To this end, the Shannon entropy of the fission distribution has been found to be a highly effective means of characterizing convergence of the fission distribution. The latest version of MCNP5 includes new capabilities for computing and plotting the Shannon entropy of the fission distribution as an important new tool for assessing problem convergence. Examples of the application of this new tool are presented for a variety of practical criticality problems. (authors)},
doi = {},
url = {https://www.osti.gov/biblio/22039478},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Sat Jul 01 00:00:00 EDT 2006},
month = {Sat Jul 01 00:00:00 EDT 2006}
}