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BackgroundThe accuracy of transient thermal hydraulic parameter prediction of reactor cores under various working conditions directly affects reactor safety. Mass flow rate and temperature are important parameters of core thermal hydraulics, which are often modeled as time-series prediction problems.PurposeThis study aims to solve the accuracy problem of continuous prediction of core thermal hydraulic parameters under instantaneous conditions and to test the feasibility of a gated cycle unit based on the attention mechanism in core parameter prediction.MethodsThe 1/2 full core model of China Experimental Fast Reactor (CEFR) core was taken as the research object. The subchannel SUBCHANFLOW program was employed to generate the time series of transient core thermal hydraulic parameters. The gated recurrent unit (GRU) model based on soft attention was used to predict the mass flow and temperature time series of the core.ResultsThe results show that, compared with the adaptive radial basis function (RBF) neural network, the GRU network model with soft attention offers better prediction results. The average relative error of temperature is