The proposed method is verified using two numerical examples: a two-dimensional (2D) airfoil and a turbine blade. However, the present study shows that the optimal network is not always optimal in terms of reproducing the 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. When CVAE is applied to mechanical design, it is desired to draw shapes that reproduce the specified aerodynamic performance. Then, shapes are generated by specifying the continuous label and latent vector. In the CVAE model, a shape is fed as an input and the corresponding aerodynamic performance index is fed as a continuous label. 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 present work, we achieve this goal using a conditional variational autoencoder (CVAE). An objective of mechanical design is to obtain a shape that satisfies specific requirements.
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