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

Frontiers in Energy Research

Energy Research

Jun Yang¹  Yong Li¹  Xi Sui¹  Ling Zhao¹  Yanping Huang²  Dianle Wang³ 

[1]Reactor Engineering Research Sub-Institute, Nuclear Power Institute of China, Chengdu, China

[2]Reactor Engineering Research Sub-Institute, Nuclear Power Institute of China, Chengdu, China

[3]CNNC Key Laboratory on Nuclear Reactor Thermal Hydraulics Technology, Nuclear Power Institute of China, Chengdu, China

[4]Reactor Engineering Research Sub-Institute, Nuclear Power Institute of China, Chengdu, China

[5]College of Physics, Sichuan University, Chengdu, China

关键词: digital twin;    computational fluid dynamics;    model reduction algorithm;    proper orthogonal decomposition;    deep learning;    compressor;   

DOI  :  10.3389/fenrg.2023.1163043

 received in 2023-02-10, accepted in 2023-03-21,  发布年份 2023

来源: Frontiers

【 摘 要 】

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.

【 授权许可】

Unknown   
Copyright © 2023 Yang, Huang, Wang, Sui, Li and Zhao.