Application of Neural Network in Prediction of Radionuclide Diffusion in Receiving Water  

作　　者：ZHOU Yanchen HU Tiesong 

机构地区：[1]State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University

出　　处：《Wuhan University Journal of Natural Sciences》2015年第1期73-78,共6页武汉大学学报（自然科学英文版）

基　　金：Supported by the National Natural Science Foundation of China(51339004,71171151)

摘　　要：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.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.

关 键 词：inland nuclear accident radionuclide diffusion computational fluid dynamics priori knowledge time series neural network 

分 类 号：X591[环境科学与工程—环境工程]