Research on Prediction Method of In-core Capacity Factor Based on Graph Convolutional Network

The distribution of capacity factor of core directly affects the safe operation of the reactor. In order to achieve accurate prediction of capacity factor distribution, the spatial relationship of the distribution of each sensitive segments in the power range detector and the derivation process of the capacity factor physical model are fully considered in the paper, which proposes a graph data structure by the research on the neutron transport matrix, which is suitable for capacity factor distribution prediction, and uses the graph convolutional network (GCN) to predict the capacity factor. Based on the historical data of a second-generation pressurized water reactor unit, analysis of examples is conducted and the result shows that the spatial characteristics of signal of the power range detector can be integrated effectively by the proposed graph data structure. Combining the GCN model to predict two different situations of stable and large fluctuations in the capacity factor, the result show that two different situations of capacity factor can be predicted accurately by GCN model, which solves the problem of unsatisfactory prediction results of traditional time series prediction models in situations under large fluctuations. Therefore, the method proposed in this paper is suitable for prediction of in-core capacity factor, which has a high reference value for improving the safety and reliability of nuclear reactor operation.