The inner working conditions of a reactor are complicated and affected by many factors. Accurate prediction of the key thermal parameters of the reactor core under various working conditions can greatly improve reactor safety. Most of the existing research focuses on the prediction method that uses a single neural network. In the case of excessive noise, a single neural network cannot sufficiently eliminate noise and accurately detect data change.

This study aims to propose a novel transient thermal hydraulic parameter prediction method for fast reactor core, making use of a model that is based on the empirical mode decomposition (EMD) and singular spectrum analysis (SSA) combined with an adaptive radial basis function (RBF) neural network.

Firstly, the 1/2 China Experimental Fast Reactor (CEFR) was used as the research object, and the fast reactor subchannel program SUBCHANFLOW was employed to generate a time series of transient core thermal hydraulic parameters. Then, two combined models, i.e., EMD-RBF and EMD-SSA-RBF, were used to predict the core mass flow rate and time series of the maximum temperature on the surface of the cladding. Both the single step prediction and continuous prediction were performed.

The results show that compared with a single RBF neural network, the single-step prediction errors of mass flow rate with the EMD-RBF combined neural network and EMD-SSA-RBF combined neural network are reduced by 41.2% and 86.7% respectively, whilst the single-step prediction errors of temperature are reduced by 44.7% and 60.5% respectively. Not only the prediction errors are significantly reduced, but also the calculation time for parameter prediction is shortened.

The combined neural network models proposed in this study can make fast and high-precision predictions, providing advantages over the deep neural network. Hence have certain reference value for improving the safety of the reactor in engineering applications.