Surrogate model for predicting severe accident progression in nuclear power plant using deep learning methods and Rolling-Window forecast

Abstract

This paper introduces methods to develop a surrogate model based on deep learning methods and rolling-window forecast for fast and accurate prediction of severe accidents in a nuclear power plant. The surrogate model was trained using time series data, which represents thermal–hydraulic behavior in the nuclear power plant under multi-component failures while various mitigation strategies are also implemented. The model uses a rolling-window forecast to predict selected thermal–hydraulic variables for each time step using the previous time-step variables. To improve the accuracy, the model was further refined to consider the hysteresis effect of the variables using the previous three-time steps. The value of the performance metrics measured by the mean absolute error was reduced by 64 percent in the three-step model compared to the single-step model. The proposed surrogate model has the potential as a practical severe accident simulator for accident management support tools.