4/25/2024 0 Comments Uiuc airfoil databaseNew and plausible airfoils are generated with success. This demonstrates an efficient learning-based airfoil design framework that encodes and optimizes the airfoil on the latent domain and synthesizes promising airfoil candidates for required aerodynamic performance.Ī deep learning-based generative model for airfoils is proposed.Ī combination of Generative Adversarial Network and Variational Autoencoder is implemented to learn latent representations and generate airfoils. By optimizing shapes on the learned latent domain via a genetic algorithm, synthesized airfoils can evolve to target aerodynamic properties. By interpolating/extrapolating feature vectors or sampling from Gaussian noises, the model can automatically synthesize novel airfoil shapes, some of which possess competitive or even better aerodynamic properties as compared to airfoils used for model training purposes. Our experiments show that the learned features encode shape information thoroughly and comprehensively without predefined design parameters. Our model can (1) encode the existing airfoil into a latent vector and reconstruct the airfoil from that, (2) generate novel airfoils by randomly sampling the latent vectors and mapping the vectors to the airfoil coordinate domain, and (3) synthesize airfoils with desired aerodynamic properties by optimizing learned features via a genetic algorithm. Our model is built upon VAEGAN, a neural network that combines Variational Autoencoder with Generative Adversarial Network and is trained by the gradient-based technique. The representations are then used in the optimization of synthesized airfoil shapes based on their aerodynamic performance. In this work, we propose a data-driven shape encoding and generating method, which automatically learns representations from existing airfoils and uses the learned representations to generate new airfoils. Usually, such design relies on the prior definition of design parameters and places restrictions on synthesizing novel shapes. The current design of aerodynamic shapes, like airfoils, involves computationally intensive simulations to explore the possible design space.
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