Incorporating Riemannian Geometric Features for Learning Coefficient of Pressure Distributions on Airplane Wings

• URL: http://arxiv.org/abs/2401.09452v1
• Date: Fri, 22 Dec 2023 13:09:17 GMT
• Title: Incorporating Riemannian Geometric Features for Learning Coefficient of Pressure Distributions on Airplane Wings
• Authors: Liwei Hu, Wenyong Wang, Yu Xiang, Stefan Sommer
• Abstract summary: The aerodynamic coefficients of aircrafts are significantly impacted by its geometry. Motivated by geometry theory, we propose to incorporate geometrician features for learning Coefficient of Pressure distributions on wing surfaces. Our method reduces the predicted mean square error (MSE) of CP by an average of 8.41% for the DLR-F11 aircraft test set.
• Score: 4.980486951730395
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The aerodynamic coefficients of aircrafts are significantly impacted by its geometry, especially when the angle of attack (AoA) is large. In the field of aerodynamics, traditional polynomial-based parameterization uses as few parameters as possible to describe the geometry of an airfoil. However, because the 3D geometry of a wing is more complicated than the 2D airfoil, polynomial-based parameterizations have difficulty in accurately representing the entire shape of a wing in 3D space. Existing deep learning-based methods can extract massive latent neural representations for the shape of 2D airfoils or 2D slices of wings. Recent studies highlight that directly taking geometric features as inputs to the neural networks can improve the accuracy of predicted aerodynamic coefficients. Motivated by geometry theory, we propose to incorporate Riemannian geometric features for learning Coefficient of Pressure (CP) distributions on wing surfaces. Our method calculates geometric features (Riemannian metric, connection, and curvature) and further inputs the geometric features, coordinates and flight conditions into a deep learning model to predict the CP distribution. Experimental results show that our method, compared to state-of-the-art Deep Attention Network (DAN), reduces the predicted mean square error (MSE) of CP by an average of 8.41% for the DLR-F11 aircraft test set.

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