Data-driven design exploration method using conditional variational autoencoder for airfoil design

An objective of mechanical design is to obtain a shape that satisfies specific requirements. In the present work, we achieve this goal using a conditional variational autoencoder (CVAE). The method enables us to analyze the relationship between aerodynamic performance and the shape of aerodynamic pa...

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Veröffentlicht in:Structural and multidisciplinary optimization Jg. 64; H. 2; S. 613 - 624
Hauptverfasser: Yonekura, Kazuo, Suzuki, Katsuyuki
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2021
Springer Nature B.V
Schlagworte:
Aerodynamics
Airfoils
Approximation
Computational Mathematics and Numerical Analysis
Deep learning
Design
Designers
Engineering
Engineering Design
Machine learning
Methods
Neural networks
Optimization
Performance indices
Research Paper
Theoretical and Applied Mechanics
Turbine blades
Turbines
Variational autoencoder
Design exploration
Airfoil design
ISSN:1615-147X, 1615-1488
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Zusammenfassung:An objective of mechanical design is to obtain a shape that satisfies specific requirements. In the present work, we achieve this goal using a conditional variational autoencoder (CVAE). The method enables us to analyze the relationship between aerodynamic performance and the shape of aerodynamic parts, and to explore new designs for the parts. In the CVAE model, a shape is fed as an input and the corresponding aerodynamic performance index is fed as a continuous label. Then, shapes are generated by specifying the continuous label and latent vector. When CVAE is applied to mechanical design, it is desired to draw shapes that reproduce the specified aerodynamic performance. In ordinal CVAE, the model is trained to minimize reconstruction loss and latent loss, and it is usually optimized considering the sum of these losses. However, the present study shows that the optimal network is not always optimal in terms of reproducing the aerodynamic performance. The proposed method is verified using two numerical examples: a two-dimensional (2D) airfoil and a turbine blade. In the airfoil example, we demonstrate the effects of latent dimension, and in the turbine design example, we demonstrate that the proposed method can be applied to a real turbine design problem and reduce the design time.
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ISSN:1615-147X
1615-1488
DOI:10.1007/s00158-021-02851-0