Abstract

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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