Fast prediction of compressor flow field in nuclear power system based on proper orthogonal decomposition and deep learning

Abstract

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 the original flow field information are obtained using POD and deep neural network (DNN) is used to construct the POD-DNN flow field reduction model to achieve fast flow field prediction. The calculation accuracy and speed of the reduced-order model are analyzed in detail, considering the flow field of the nuclear compressor and key flow equipment of the nuclear power system as objects. The results show that the average relative deviation of the POD-DNN is <10% and calculation time is <1% when compared to those of CFD. This research shows that the high-fidelity model constructed using model reduction and deep learning is a feasible method for the realization of digital twins of the nuclear power system in engineering.

A Bibliometric Analysis of Digital Twin in the Supply Chain

“…There is also a potential research gap for the application of digital twin in a deep learning architecture for mobile edge computing. Moreover, Yang et al [98] proposed a model with model reduction and deep neural network as a basic to develop digital twin for nuclear power system but noted that there was still a research gap to increase the efficiency of the model. Moreover, digital twin is also seen as a driver to speed up the [95] used the artificial neural network, which was a part of deep learning, for activity recognition, which was then used to perform discrete-event simulation.…”

“…There is also a potential research gap for the application of digital twin in a deep learning architecture for mobile edge computing. Moreover, Yang et al[98] proposed…”