Comparative study of data-driven and model-driven approaches in prediction of nuclear power plants operating parameters

Abstract

Nuclear energy plays a crucial role in mitigating climate change as a near carbon-free and clean energy source. Predicting operating parameters is key to the digitalization and intellectualization of nuclear power plants, improving energy efficiency and reducing costs. Parameter prediction methods mainly consist of model-driven and data-driven approaches, and a comparative study is necessary to select the appropriate prediction method or combine them. In this paper, the gated recurrent unit network and the thermal–hydraulic program RELAP5 are chosen as representative data-driven and model-driven approaches to be used for predicting parameters for a specific operating condition to assess the characteristics and capabilities of each. The chosen experiment is a steam generator tube rupture accident which is important to pressurized water reactors. In this paper the modelling process and the prediction results for both approaches are given. The correlation coefficient for dome pressure, dome temperature, and primary outlet temperature of both approaches is greater than 0.993, except that the correlation coefficient of downcomer temperature is 0.83865 using RELAP5 and 0.99392 using gated recurrent unit network. Then the two approaches are compared from six perspectives of practical application, including accuracy (gated recurrent unit network and RELAP5 received scores of 8 and 6, respectively.), data requirements (7 and 5, respectively), model building algorithm (7 and 5, respectively), prediction speed (10 and 6, respectively), expertise requirements (6 and 6, respectively), and interpretability (5 and 9, respectively). It shows that model-driven and data-driven approaches both require specialist knowledge and appropriate data for modelling process. The data-driven gated recurrent unit network exhibits better accuracy and higher speed. The model-driven RELAP5 has better interpretability compared to the black-box gated recurrent unit network. It is suggested that hybrid approaches of model-driven and data-driven approaches could better deal with the prediction problems.