Genetic algorithm for nuclear data evaluation applied to subcritical neutron multiplication inference benchmark experiments
Abstract
An optimization algorithm has been developed for the first time for application to International Criticality Safety Benchmark Evaluation Project (ICSBEP) subcritical neutron multiplication inference benchmark experiments. The optimization algorithm is a genetic algorithm for nuclear data evaluation adjustments, specifically applied to subcritical benchmark measurements. The algorithm has been tested and yields improvement in (CE)/E values of subcritical benchmark observables of interest. In this work, the genetic algorithm is applied to improvement of fission neutron multiplicity distribution parameters using several subcritical neutron multiplication inference benchmarks; specifically a series of reflected 4.5 kg αphase spherical plutonium benchmarks. The algorithm results suggest changing the mean ($$\bar{v}$$) and standard deviation (σ) of the number of neutrons emitted by ^{240}Pu in spontaneous fission from 2.1510 to 2.1460 and from 1.1510 to 1.1395, respectively. In addition, the standard deviation of the number of neutrons emitted by ^{239}Pu in induced fission should remain unchanged at 1.1400. These changes are all within 1 standard deviation.
 Authors:

 Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Univ. of Michigan, Ann Arbor, MI (United States)
 Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
 Univ. of Michigan, Ann Arbor, MI (United States)
 Publication Date:
 Research Org.:
 Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
 Sponsoring Org.:
 USDOE National Nuclear Security Administration (NNSA)
 OSTI Identifier:
 1558209
 Report Number(s):
 LAUR1829992
Journal ID: ISSN 03064549
 Grant/Contract Number:
 89233218CNA000001
 Resource Type:
 Journal Article: Accepted Manuscript
 Journal Name:
 Annals of Nuclear Energy (Oxford)
 Additional Journal Information:
 Journal Volume: 133; Journal Issue: C; Journal ID: ISSN 03064549
 Publisher:
 Elsevier
 Country of Publication:
 United States
 Language:
 English
 Subject:
 42 ENGINEERING
Citation Formats
Arthur, Jennifer Ann, Bahran, Rian Mustafa, Hutchinson, Jesson D., and Pozzi, Sara A. Genetic algorithm for nuclear data evaluation applied to subcritical neutron multiplication inference benchmark experiments. United States: N. p., 2019.
Web. doi:10.1016/j.anucene.2019.07.024.
Arthur, Jennifer Ann, Bahran, Rian Mustafa, Hutchinson, Jesson D., & Pozzi, Sara A. Genetic algorithm for nuclear data evaluation applied to subcritical neutron multiplication inference benchmark experiments. United States. doi:10.1016/j.anucene.2019.07.024.
Arthur, Jennifer Ann, Bahran, Rian Mustafa, Hutchinson, Jesson D., and Pozzi, Sara A. Sat .
"Genetic algorithm for nuclear data evaluation applied to subcritical neutron multiplication inference benchmark experiments". United States. doi:10.1016/j.anucene.2019.07.024. https://www.osti.gov/servlets/purl/1558209.
@article{osti_1558209,
title = {Genetic algorithm for nuclear data evaluation applied to subcritical neutron multiplication inference benchmark experiments},
author = {Arthur, Jennifer Ann and Bahran, Rian Mustafa and Hutchinson, Jesson D. and Pozzi, Sara A},
abstractNote = {An optimization algorithm has been developed for the first time for application to International Criticality Safety Benchmark Evaluation Project (ICSBEP) subcritical neutron multiplication inference benchmark experiments. The optimization algorithm is a genetic algorithm for nuclear data evaluation adjustments, specifically applied to subcritical benchmark measurements. The algorithm has been tested and yields improvement in (CE)/E values of subcritical benchmark observables of interest. In this work, the genetic algorithm is applied to improvement of fission neutron multiplicity distribution parameters using several subcritical neutron multiplication inference benchmarks; specifically a series of reflected 4.5 kg αphase spherical plutonium benchmarks. The algorithm results suggest changing the mean ($\bar{v}$) and standard deviation (σ) of the number of neutrons emitted by 240Pu in spontaneous fission from 2.1510 to 2.1460 and from 1.1510 to 1.1395, respectively. In addition, the standard deviation of the number of neutrons emitted by 239Pu in induced fission should remain unchanged at 1.1400. These changes are all within 1 standard deviation.},
doi = {10.1016/j.anucene.2019.07.024},
journal = {Annals of Nuclear Energy (Oxford)},
issn = {03064549},
number = C,
volume = 133,
place = {United States},
year = {2019},
month = {7}
}
Figures / Tables:
Figures / Tables found in this record: