Prediction of Heat Transfer Coefficient of Two Phase Flow Sodium Reactor Based on Artificial Neural Network

Abstract

Liquid sodium metal is one of the main coolants for fast neutron reaction in nuclear reactors with high heat load and high thermal conductivity. Sodium reactor is one of the most promising and commercialized reactor types in modern nuclear power system. Because the chemical properties of liquid sodium metal are very active, so in a sealed environment, it is easy to react with oxygen, water and other substances, resulting in an explosion. Therefore, it is necessary to design a reasonable experimental circuit and conditions under the appropriate experimental environment to carry out the experiment. With liquid metal sodium as the experimental medium, this paper studies the flow heat transfer characteristics of a two-phase flow sodium reactor by establishing a mathematical model. The key heat transfer parameters in a two-phase flow sodium reactor are predicted by building an artificial neural network model, and the parameters affecting the network performance are adjusted by the artificial neural network. The error of the final result predicted by the adjusted artificial neural network model is within the range of ±10%. The prediction results show that the prediction by the artificial neural network model has higher prediction accuracy.