基于卷积神经网络的深度学习流场特征识别及应用进展

doi:10.7527/S1000-6893.2020.24736

Abstract

Abstract: With excellent performance, the deep learning architecture has enabled new developments in application of machine learning in fluid mechanics, and can cope with many challenges and needs in fluid mechanics. Due to powerful nonlinear mapping capabilities and hierarchical extraction of information features, the Convolutional Neural Network (CNN) has become a tool that cannot be ignored in current research on flow features. This paper summarizes the progress and achievements in this research area. First, the developments of deep learning for fluid mechanics and CNNs are briefly reviewed. Then, the research progress of using deep CNN in flow prediction, flow shape optimization, improving the accuracy of flow field visualization, and generation confrontation is introduced. Finally, prospects of application of deep learning in flow field recognition are discussed to provide a reference for subsequent research.

Key words: convolutional neural network, flow field recognition, flow prediction, shape optimization, Poisson equation, generative adversarial network, deep learning

CLC Number:

YE Shuran, ZHANG Zhen, WANG Yiwei, HUANG Chenguang. Progress in deep convolutional neural network based flow field recognition and its applications[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2021, 42(4): 524736-524736.

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Progress in deep convolutional neural network based flow field recognition and its applications

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