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

ZHAN Qingliang; BAI Chunjin; ZHANG Ning; GE Yaojun

doi:10.7527/S1000-6893.2021.26531

ACTA AERONAUTICAET ASTRONAUTICA SINICA >

2022 , Vol. 43 >Issue 11: 526531 - 526531

DOI: https://doi.org/10.7527/S1000-6893.2021.26531

Articles

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

ZHAN Qingliang ,
BAI Chunjin ,
ZHANG Ning ,
GE Yaojun

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1. College of Transportation and Engineering, Dalian Maritime University, Dalian 116026, China;
2. State Key Laboratory for Disaster Reduction in Civil Engineering, Tongji University, Shanghai 200092, China

Received date: 2021-10-18

Revised date: 2021-11-08

Online published: 2021-12-09

Supported by

National Natural Science Foundation of China (51778495, 51978527); Industry Key Laboratory of Bridge Structure Wind Resistance Technology (Shanghai) Open Project (KLWRTBMC21-02); Research Project of Liaoning Education Department (LJKZ0052)

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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

Cite this article

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 . DOI: 10.7527/S1000-6893.2021.26531

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