基于人工神经网络的压水堆燃料破损状态监测

为提升对核反应堆燃料棒包壳破损的预测能力，建立两个串联的人工神经网络分别判断燃料棒包壳是否破损以及破损程度。通过改变沾污铀质量、增加数据扰动、改变运行功率和使用更少的特征核素进行训练，对用于判断是否破损的神经网络模型和判断破损等级的神经网络进行了性能测试和分析。在沾污铀质量小于0.5 g、数据扰动在30%以内、单棒功率在77 kW到120 kW之间的条件下，第1个人工神经网络能较好地判断出是否破损。第2个神经网络，对于考虑的5种破损程度，判断的精确性较高。与传统的碘同位素比值法相比，神经网络方法响应更快，精度更高。结果表明，人工神经网络可用于预测反应堆燃料包壳是否发生破损以及破损程度。

PWR Fuel Failure Monitoring Based on Artificial Neural Network

In order to improve the accuracy of predicting the degree of the fuel defect, two serial artificial neural networks were established to determine the fuel defect state and its degree. After changing the mass of tramp uranium, increasing data fluctuations, changing single rod power, and taking fewer characteristic nuclides as input vector, performance tests and analysis were performed to evaluate the suitability of the neural network. The same analysis was performed to analyze the second network, which was trained to determine the degree of the fuel defect. Under the condition that the mass of tramp uranium is less than 0.5 g, the data fluctuation is within 30%, and the single rod power is between 77 kW and 120 kW, the first artificial neural network can better determine whether the cladding is damaged. The second neural network has a higher accuracy for the five levels of damage. Compared with the traditional iodine isotope ratio method, the neural network method is faster and more accurate. The results show that the artificial neural network can be used to predict whether the reactor fuel cladding is damaged and the degree of fuel defect.