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 9.66×10-8, which makes neural network output more close to the simulated contaminant concentration.
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机译:基于优化遗传神经网络的井下运输机械滚动轴承故障诊断(The Application of Optimizing the GENETIC NEURAL NETWORK to the Fault Diagnosis of Rolling Bearings of Transporting Machinery Underground)