Genetic algorithm for nuclear data evaluation applied to subcritical neutron multiplication inference benchmark experiments. (November 2019)

• Record Type: Journal Article
• Authors: Arthur, Jennifer; Bahran, Rian; Hutchinson, Jesson; Pozzi, Sara A.
• Is Part Of: Annals of nuclear energy. Volume 133(2019)
• Page Start: 853
• Page End: 862
• Publication Date: 2019-11
• Subjects: Genetic algorithm -- Subcritical benchmark -- Nuclear data evaluation; Nuclear energy -- Periodicals; Nuclear engineering -- Periodicals; 621.4805
• DOI: 10.1016/j.anucene.2019.07.024
• Languages: English
• ISSNs: 0306-4549

Abstract

Highlights: A genetic algorithm is used to adjust fission neutron multiplicity. The algorithm improves subcritical benchmark simulation performance. It is suggested to decrease the fission neutron multiplicity parameters. 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 (C-E)/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 α -phase spherical plutonium benchmarks. The algorithm results suggest changing the mean ( ν ¯ ) 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.