ANN model for prediction of concentration change of radionuclide based on physical mechanism

HU Tiesong, ZHOU Yanchen, WANG Xianjia

Systems Engineering - Theory & Practice ›› 2016, Vol. 36 ›› Issue (1) : 263-272.

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Systems Engineering - Theory & Practice ›› 2016, Vol. 36 ›› Issue (1) : 263-272. DOI: 10.12011/1000-6788(2016)01-0263-10

ANN model for prediction of concentration change of radionuclide based on physical mechanism

  • HU Tiesong1, ZHOU Yanchen1,2, WANG Xianjia3
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Abstract

It needs long time to predict radioactive contaminant diffusion in receiving water by using mechanism model based on computational fluid dynamics, which is not applicable in emergency situation under accident condition. In order to shorten the computation time, a new artificial neural network model that combines species transport equation which governs contaminant diffusion and neural network model is proposed, and an improved particle swarm optimization algorithm is used to obtain the weight and threshold values of neural network. In this paper, long half-life radionuclide diffusion in Fushui reservoir after a postulated accident happened in Xianning nuclear power station in Hubei Province is studied as a case. The result shows that this proposed model can basically predict the contaminant diffusion trend, and the prediction result fit well with CFD simulation output. Compared with the conventional black box neural network model and the ones with priori knowledge obtained from data monotone, the priori knowledge obtained from equation of physical mechanism is a stronger constrain, which can make the prediction result more close to the simulation output.

Key words

computational fluid dynamics / neural network / priori knowledge / physical mechanism / nuclear accident

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HU Tiesong , ZHOU Yanchen , WANG Xianjia. ANN model for prediction of concentration change of radionuclide based on physical mechanism. Systems Engineering - Theory & Practice, 2016, 36(1): 263-272 https://doi.org/10.12011/1000-6788(2016)01-0263-10

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Funding

The General Program of National Natural Foundation of China (71171151)
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