Learning Mappings from Iced Airfoils to Aerodynamic Coefficients Using a Deep Operator Network

Zhao, Tun; Qian, Weikang; Jiang, Lin; Hai, Chen; Ao, Hou-jun; Gong, Chen; He, Lei

doi:10.1061/jaeeez.aseng-4508

Cited by 8 publications

(1 citation statement)

References 18 publications

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“…Nevertheless, Chen and Zhang's work only examined the correlation between airfoils and aerodynamic coefficients under fixed inflow conditions, which has significant limitations. Zhao [29] established a mapping relationship between iced airfoils and aerodynamic coefficients using a deep operator network that employs CNN to extract the shape features of the iced airfoils. Although CNN is adept at image processing and can handle irregular airfoil shapes, the thickness of the airfoil's contour line and the image resolution can influence the representation of the airfoil, potentially compromising the accuracy of the aerodynamic characteristic prediction model.…”

Section: Introductionmentioning

confidence: 99%

Fast Prediction of Airfoil Aerodynamic Characteristics Based on a Combined Autoencoder

Wang,

Qian,

Zhao

et al. 2024

Symmetry

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Aircraft airfoils are classified into two main categories: symmetrical and asymmetrical. Both types of airfoils have a significant impact on the flight performance and safety of the aircraft. The fast prediction of the aerodynamic coefficients and pressure distributions of airfoils is crucial for the design of aircraft. The traditional wind tunnel test and CFD methods have the disadvantages of high test cost and high time consumption. To solve these problems, a combined autoencoder (CAE) network is proposed in this paper, which can achieve the fast prediction of airfoil aerodynamic coefficients and pressure distributions. The network consists of an airfoil shape autoencoder (AE) network and a multilayer perceptron (MLP) network. Firstly, an autoencoder network reflecting the characteristics of the airfoil shape is established, and the effects of different latent variables on the performance of the autoencoder network are investigated. Then, the latent variables obtained from the autoencoder are concatenated with the inflow conditions such as the Reynolds number and the angle of attack to be used as inputs to the MLP network, and the aerodynamic coefficients of different airfoils in different inflow conditions are predicted. The effects of various latent variable inputs, as well as the direct input of the airfoil shape into the MLP network, on the prediction performance of aerodynamic coefficients are compared and analyzed. The optimal aerodynamic coefficient prediction network is then obtained. Finally, the CAE network is also applied to predict the pressure distributions of different airfoils in different inflow conditions and the effects of different latent variables and input conditions on the prediction performance of the pressure distributions are analyzed and compared with the advantages and disadvantages of the CAE network and the conditional variational autoencoder (CVAE) network. The results demonstrate that the proposed method is capable of accurately predicting aerodynamic characteristics in a shorter time, offering a valuable reference for the fast and efficient design of aircraft airfoils.

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Section: Introductionmentioning

confidence: 99%