A statistical analysis of results from Monte Carlo codes

Richard Wakeford; P D Clemson; S. Barratt; K Skilling

A statistical analysis of results from Monte Carlo codes

Richard Wakeford, P D Clemson, S. Barratt, K Skilling

Division of Population Health, Health Services Research & Primary Care (L5)

Research output: Chapter in Book/Conference proceeding › Conference contribution

Abstract

Following analytical work by Brissenden and Garlick, in which they showed that the eigenvalue and eigenvalue variance estimates generated by conventional tracking in MONK would be negatively biased, we set up a numerical experiment to determine whether these systematic errors could be statisically detected. Results are given in Table 1. The eigenvalue bias could not be detected, but the eigenvalue variance bias was found to lower artificially the variance for the system under study by a factor of 2. Superhistory tracking in MONK6 removes this eigenvalue variance bias, and genuinely reduces the variance. An exploratory survey of KENO5 runs shows that if eigenvalue variance bias exists within this code, then it is small. The precision achieved by KENO5 and by superhistory tracking in MONK6 is about the same for a given number of neutron histories, for the particular system under study.

Original language	Undefined
Title of host publication	ISCS’87 – Proceedings of an International Seminar on Nuclear Criticality Safety, October 19-23, 1987, Tokyo, Japan
Publication status	Published - 1987

Cite this

@inproceedings{c99616c1d4d947a8b9d6d64ead057085,

title = "A statistical analysis of results from Monte Carlo codes",

abstract = "Following analytical work by Brissenden and Garlick, in which they showed that the eigenvalue and eigenvalue variance estimates generated by conventional tracking in MONK would be negatively biased, we set up a numerical experiment to determine whether these systematic errors could be statisically detected. Results are given in Table 1. The eigenvalue bias could not be detected, but the eigenvalue variance bias was found to lower artificially the variance for the system under study by a factor of 2. Superhistory tracking in MONK6 removes this eigenvalue variance bias, and genuinely reduces the variance. An exploratory survey of KENO5 runs shows that if eigenvalue variance bias exists within this code, then it is small. The precision achieved by KENO5 and by superhistory tracking in MONK6 is about the same for a given number of neutron histories, for the particular system under study.",

author = "Richard Wakeford and Clemson, {P D} and S. Barratt and K Skilling",

year = "1987",

language = "Undefined",

booktitle = "ISCS{\textquoteright}87 – Proceedings of an International Seminar on Nuclear Criticality Safety, October 19-23, 1987, Tokyo, Japan",

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TY - GEN

T1 - A statistical analysis of results from Monte Carlo codes

AU - Wakeford, Richard

AU - Clemson, P D

AU - Barratt, S.

AU - Skilling, K

PY - 1987

Y1 - 1987

N2 - Following analytical work by Brissenden and Garlick, in which they showed that the eigenvalue and eigenvalue variance estimates generated by conventional tracking in MONK would be negatively biased, we set up a numerical experiment to determine whether these systematic errors could be statisically detected. Results are given in Table 1. The eigenvalue bias could not be detected, but the eigenvalue variance bias was found to lower artificially the variance for the system under study by a factor of 2. Superhistory tracking in MONK6 removes this eigenvalue variance bias, and genuinely reduces the variance. An exploratory survey of KENO5 runs shows that if eigenvalue variance bias exists within this code, then it is small. The precision achieved by KENO5 and by superhistory tracking in MONK6 is about the same for a given number of neutron histories, for the particular system under study.

AB - Following analytical work by Brissenden and Garlick, in which they showed that the eigenvalue and eigenvalue variance estimates generated by conventional tracking in MONK would be negatively biased, we set up a numerical experiment to determine whether these systematic errors could be statisically detected. Results are given in Table 1. The eigenvalue bias could not be detected, but the eigenvalue variance bias was found to lower artificially the variance for the system under study by a factor of 2. Superhistory tracking in MONK6 removes this eigenvalue variance bias, and genuinely reduces the variance. An exploratory survey of KENO5 runs shows that if eigenvalue variance bias exists within this code, then it is small. The precision achieved by KENO5 and by superhistory tracking in MONK6 is about the same for a given number of neutron histories, for the particular system under study.

M3 - Conference contribution

BT - ISCS’87 – Proceedings of an International Seminar on Nuclear Criticality Safety, October 19-23, 1987, Tokyo, Japan

ER -