文摘
It needs long time to predict radioactive contaminant diffusion in receiving water under accident condition by using computational fluid dynamics (CFD) model. In order to shorten the computation time, a hybrid model based on CFD and time series neural network (TSNN) is proposed in this paper. The concentration change of radioactive contamination in an inland reservoir after a postulated accident is studied as a case. The result shows that this hybrid model can predict the contaminant diffusion trend and shorten at least 50% of iteration time. Priori knowledge integrated into the neural network model is able to reduce the mean square error of network output to 9.66×10?, which makes neural network output more close to the simulated contaminant concentration.