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 (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 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.