基于时程卷积自编码的机翼绕流特征识别方法

doi:10.7527/S1000-6893.2021.26531

Abstract

Abstract: The flow around an airfoil directly affects its aerodynamics, and flow feature analysis is critical to ensure its aerodynamic characteristics. The feature recognition based on spatial parameters is greatly affected by subjective threshold. And flow analysis based on the snapshot data, which is difficult to obtain a large-scale snapshot in experiment, is difficult to fully represent the time-varying features of the flow evolution. Based on the low-dimensional representation model of flow time-history data, an unsupervised autoencoder method is proposed to identify flow features automatically. Deep learning is used to mine the implicit flow features in the time-history signal, and a low-dimensional representation model of the time-history data is established. The low-dimensional representation code is further analyzed, and the measurement point samples containing different features are clustered to extract and identify flow features. Analysis of the flow field around the NACA0012 airfoil with a low Reynolds number is carried out. The flow separation zone was successfully identified using the proposed feature classification method. Results proved that the proposed method can be used to solve the problems such as flow feature extraction, feature analysis, and feature characterization.

Key words: flow time-history, convolutional autoencoder, deep learning, feature extraction, flow around airfoil

CLC Number:

V211.3

ZHAN Qingliang, BAI Chunjin, ZHANG Ning, GE Yaojun. Feature extraction method of flow around airfoil based on time-history convolutional autoencoder[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2022, 43(11): 526531-526531.

References

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Feature extraction method of flow around airfoil based on time-history convolutional autoencoder

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