Research on Rapid Analysis Method of Ex-core Detector Response Based on BP Neural-network Algorithm

The response of the ex-core detector describes the corresponding relationship between neutron fluence rate and current signal, which plays a crucial role in the safe operation of the reactor. In response to the problem that both deterministic and Monte Carlo methods cannot balance computational efficiency and accuracy in calculating the response of ex-core detectors, a back propagation (BP) neural network algorithm is used to quickly calculate the response of ex-core detectors. Based on the core design system CMS, the physical modeling of the existing million-kilowatt pressurized water reactor core in China was carried out. The fuel assembly arrangement and burnup changes in the core are used as the inputs of the BP neural network, and the corresponding fuel assembly arrangement and detector responses outside the reactor under different burnup are used as the outputs of the BP neural network. A three-layer BP neural network model is constructed and optimized. After calculation verification, the optimized model can quickly calculate the response of the ex-core detector, and the predicted value has a smaller error compared to the core design system CMS calculation value. It has good engineering application prospects, providing a new idea for calculating the response of the ex-core detector.