A two parameter iterative approach of multiplication correction for assaying plutonium metal parts with neutron multiplicity counting

Liu, Xiaobo; Chen, Ligao

doi:10.1016/j.nima.2021.165779

Cited by 1 publication

(1 citation statement)

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“…The method was validated by simulation experiments and experimentally verified for real metal plutonium parts. Using a pre-obtained set of correction factors, the method can greatly improve the bias in analyzing plutonium metal parts with geometry such as spherical, hemispherical, and spheres of unknown radius and thickness (Liu and Chen, 2021). Kaile Li et al introduced a neural network to implement a method for rapid prediction of sample quality by three parameters and effectively reduce the error by a genetic algorithm (Li et al, 2021a).…”

Section: Introductionmentioning

confidence: 99%

Simulation study of neutron multiplicity of plutonium samples of different shapes

Zhang

Yao

et al. 2022

Front. Energy Res.

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In this paper, an optimized boron-coated straw tube neutron multiplicity counter is used to simulate neutron multiplicity for six different shapes of weapons-grade plutonium samples. It was found that under the point model equation, the mass simulation results for samples of different shapes showed different degrees of negative deviations with increasing mass, and the deviations increased with increasing mass. On this basis, the reasons for the deviation are analyzed and a multilayer perceptron is introduced, and three counting rates and preset qualities of samples of different qualities under six shapes are trained. The results showed that the multi-layer perceptron could better fit the relationship between the counting rates of different shapes and the preset quality. The sparrow search algorithm was introduced to optimize the initial weights and thresholds of the multilayer perceptron, and the optimized model was used to predict another four samples of different shapes. The results show that the optimized multilayer perceptron can better fit the relationship between the counting rate and the mass of samples with different shapes. When the sample mass is less than 3 kg, the relative deviation of the three counting rates for samples with four shapes to the sample mass prediction is less than 2%, which meets the requirements of neutron multiplicity measurement.

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Section: Introductionmentioning

confidence: 99%