Center for Strategic Studies, China Academy of Engineering Physics, Beijing, 100088, China.
Center for Strategic Studies, China Academy of Engineering Physics, Beijing, 100088, China.
Appl Radiat Isot. 2021 Apr;170:109626. doi: 10.1016/j.apradiso.2021.109626. Epub 2021 Feb 2.
Future international nuclear disarmament may involve the dismantlement of nuclear warheads. In nuclear warhead dismantlement verification, the mass information of the fissile material in the pit is an important attribute of nuclear warheads, and can be used to verify that the nuclear warheads demanded by the nuclear disarmament treaty have indeed been dismantled. In this paper, a method of reconstructing the fissile material mass of the pit based on the activation effect of the explosive and the neural network is proposed, and may be applied in the future nuclear warhead dismantlement verification. Firstly, the number and average abundance of C produced by the neutron activation reactions in the explosive inside the nuclear warhead was calculated based on the Monte Carlo numerical simulation. Secondly, it is found that the spatial distribution of the C abundances in the explosive is closely related to the fissile material mass of the pit through the numerical simulation. Then, neural networks were established to reconstruct the fissile material mass of the pit through the training. The testing results show that, the fissile material mass of the pit can be reconstructed accurately based on the activation effect of the explosive and the neural network, and the reconstruction precision is better than 10%.
未来的国际核裁军可能涉及核弹头的拆除。在核弹头拆除核查中，弹坑内易裂变材料的质量信息是核弹头的一个重要属性，可以用于核查核裁军条约所要求的核弹头确实已被拆除。本文提出了一种基于爆炸物激活效应和神经网络的弹坑内易裂变材料质量重建方法，该方法可能应用于未来的核弹头拆除核查中。首先，基于蒙特卡罗数值模拟，计算了核弹头内爆炸物中中子激活反应生成的 C 的数量和平均丰度。其次，通过数值模拟发现，爆炸物中 C 的丰度空间分布与弹坑内易裂变材料的质量密切相关。然后，通过训练建立神经网络，通过爆炸物的激活效应来重建弹坑内易裂变材料的质量。测试结果表明，基于爆炸物的激活效应和神经网络可以准确地重建弹坑内易裂变材料的质量，重建精度优于 10%。
