Kavli Affiliate: Jing Wang
| First 5 Authors: Hairun Xie, Jing Wang, Miao Zhang, ,
| Summary:
Traditional airfoil parametric technique has significant limitation in modern
aerodynamic optimization design.There is a strong demand for developing a
parametric method with good intuitiveness, flexibility and representative
accuracy. In this paper, two parametric generative schemes based on deep
learning methods are proposed to represent the complicate design space under
specific constraints. 1. Soft-constrained scheme: The CVAE-based model trains
geometric constraints as part of the network and can provide constrained
airfoil synthesis; 2. Hard-constrained scheme: The VAE-based model serves to
generate diverse airfoils, while an FFD-based technique projects the generated
airfoils to the final airfoils satisfying the given constraints. The
statistical results show that the reconstructed airfoils are accurate and
smooth without extra filters. The soft constrained scheme tend to synthesize
and explore airfoils efficiently and effectively, concentrating to the
reference airfoil in both geometry space and objective space. The constraints
will loose for a little bit because the inherent property of the model. The
hard constrained scheme tend to generate and explore airfoils in a wider range
for both geometry space and objective space, and the distribution in objective
space is closer to normal distribution. The synthesized airfoils through this
scheme strictly conform with constraints, though the projection may produce
some odd airfoil shapes.
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