A new method for nuclear accident source term inversion based on GA-BPNN algorithm

Abstract

Rapid and accurate prediction and evaluation of accident consequences can provide scientific basis for decision-making of nuclear emergency measures. Accident source term estimation under reactor accident conditions is an important part of nuclear accident consequence evaluation. In order to accurately estimate the information of radioactive source terms released from nuclear power plants to the environment, an inversion model of accident source terms based on BP neural network algorithm (BPNN) was constructed. And to resolve the defect that BPNN is easy to fall into local minimum during training process, genetic algorithm (GA) was used to optimize the weights and thresholds of BPNN. In this paper, referring to the release rates of radioactive source term from the Fukushima nuclear accident. The release rates of 131I and 137Cs diffused into the environment in stable atmosphere were taken as the two target outputs of the GA-BPNN, and the meteorological data for one hour at fixed monitoring points were taken as the target inputs. And the simulation results showed that for the release rate of 131I and 137Cs, the mean relative errors of the training and the testing sample sets were both below 2 % which indicates that the GA-BPNN model not only improves the shortcoming of BPNN, but also increases the speed and accuracy of source term inversion.