Flow field prediction of supercritical airfoils via variational autoencoder based deep learning framework

Wang, Jing1; He, Cheng2; Li, Runze3; Chen, Haixin3; Zhai, Chen1; Zhang, Miao1

2021-08-01

10.1063/5.0053979

PHYSICS OF FLUIDS   影响因子和分区

1070-6631

1089-7666

Effective access to obtain the complex flow fields around an airfoil is crucial in improving the quality of supercritical wings. In this study, a systematic method based on generative deep learning is developed to extract features for depicting the flow fields and predict the steady flow fields around supercritical airfoils. To begin with, a variational autoencoder (VAE) network is designed to extract representative features of the flow fields. Specifically, the principal component analysis technique is adopted to realize feature reduction, aiming to obtain the optimal dimension of features in VAE. Afterward, the extracted features are incorporated into the dataset, followed by the mapping from the airfoil shapes to features via a multilayer perception (MLP) model. Eventually, a composite network is adopted to connect the MLP and the decoder of VAE for predicting the flow fields given the airfoil. The proposed VAE network achieves compression of high-dimensional flow field data into ten representative features. The statistical results indicate the accurate and generalized performance of the proposed method in reconstructing and predicting flow fields around a supercritical airfoil. Especially, our method obtains accurate prediction results over the shock area, indicating its superiority in conducting turbulent flow under high Reynolds number.

SCI ; EI

英语

其他

National Key Project of China[GJXM92579] ; National Natural Science Foundation of China[92052203,61903178,"U20A2030"] ; Shanghai Sailing Program[21YF1459400] ; Shenzhen Science and Technology Program[RCBS20200714114817264]

Mechanics ; Physics

Mechanics ; Physics, Fluids & Plasmas

WOS:000686299700007

AMER INST PHYSICS

20213510821124

Airfoils ; Flow fields ; Forecasting ; Learning systems ; Reynolds number ; Steady flow

Fluid Flow, General:631.1 ; Aircraft, General:652.1

PHYSICS

Web of Science

1.Shanghai Aircraft Design & Res Inst, Shanghai 200436, Peoples R China
2.Southern Univ Sci & Technol, Dept Comp Sci & Engn, Guangdong Prov Key Lab Brain Inspired Intelligent, Shenzhen 518055, Peoples R China
3.Tsinghua Univ, Sch Aerosp Engn, Beijing 100084, Peoples R China

Wang, Jing,He, Cheng,Li, Runze,et al. Flow field prediction of supercritical airfoils via variational autoencoder based deep learning framework[J]. PHYSICS OF FLUIDS,2021,33(8).

Wang, Jing,He, Cheng,Li, Runze,Chen, Haixin,Zhai, Chen,&Zhang, Miao.(2021).Flow field prediction of supercritical airfoils via variational autoencoder based deep learning framework.PHYSICS OF FLUIDS,33(8).

Wang, Jing,et al."Flow field prediction of supercritical airfoils via variational autoencoder based deep learning framework".PHYSICS OF FLUIDS 33.8(2021).