Fast prediction of compressor flow field in nuclear power system based on proper orthogonal decomposition and deep learning - Yang, Jun; Huang, Yanping; Wang, Dianle; Sui, Xi; Li, Yong; Zhao, Ling (Frontiers in Energy Research, 2023)
Research and development on digital twins of nuclear power systems has focused on high-precision real-time simulation and the prediction of local complex three-dimensional fluid dynamics. Traditional computational fluid dynamics (CFD) methods cannot take into consideration the efficiency and accuracy of fluid dynamics. In this study, a fast-flow field-prediction framework based on proper orthogonal decomposition (POD) and deep learning is proposed. Compressed data containing original flow-field information were obtained using POD and deep neural network (DNN) was used to construct the POD–DNN flow field reduction model, to achieve the fast flow field prediction. The calculation accuracy and speed of the reduced-order model were analyzed in detail considering the flow field of the nuclear compressor, the key flow equipment of the nuclear power system, as objects. Results show that the average relative deviation of the POD–DNN was < 10%, and the calculation time was < 1% of that of the CFD. This research shows that the high-fidelity model constructed using model reduction and deep learning is a feasible method for the realization of the digital twin of the nuclear power system in engineering.