A Manifold-based Airfoil Geometric-feature Extraction and Discrepant
Data Fusion Learning Method
- URL: http://arxiv.org/abs/2206.12254v1
- Date: Thu, 23 Jun 2022 08:55:21 GMT
- Title: A Manifold-based Airfoil Geometric-feature Extraction and Discrepant
Data Fusion Learning Method
- Authors: Yu Xiang, Guangbo Zhang, Liwei Hu, Jun Zhang, Wenyong Wang
- Abstract summary: We propose a manifold-based airfoil-feature extraction and discrepant data fusion learning method (MDF) to extract geometric-features of airfoils.
Experimental results show that our method could extract geometric-features of airfoils more accurately with existing methods.
- Score: 17.632073629030845
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Geometrical shape of airfoils, together with the corresponding flight
conditions, are crucial factors for aerodynamic performances prediction. The
obtained airfoils geometrical features in most existing approaches (e.g.,
geometrical parameters extraction, polynomial description and deep learning)
are in Euclidean space. State-of-the-art studies showed that curves or surfaces
of an airfoil formed a manifold in Riemannian space. Therefore, the features
extracted by existing methods are not sufficient to reflect the
geometric-features of airfoils. Meanwhile, flight conditions and geometric
features are greatly discrepant with different types, the relevant knowledge of
the influence of these two factors that on final aerodynamic performances
predictions must be evaluated and learned to improve prediction accuracy.
Motivated by the advantages of manifold theory and multi-task learning, we
propose a manifold-based airfoil geometric-feature extraction and discrepant
data fusion learning method (MDF) to extract geometric-features of airfoils in
Riemannian space (we call them manifold-features) and further fuse the
manifold-features with flight conditions to predict aerodynamic performances.
Experimental results show that our method could extract geometric-features of
airfoils more accurately compared with existing methods, that the average MSE
of re-built airfoils is reduced by 56.33%, and while keeping the same predicted
accuracy level of CL, the MSE of CD predicted by MDF is further reduced by
35.37%.
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