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- W4206780690 abstract "Traditional airfoil optimization approaches often rely on intensive computational fluid dynamics (CFD) simulations and often needs high performance computing, which makes the optimization a computationally expensive process. In this work, we proposed a novel airfoil optimization framework that does not require any CFD simulations, the DockingNet — it is a neural network based model that docks an airfoil generator and an aerodynamic coefficient predictor. Instead of optimizing the pre-defined airfoil parameters, DockingNet searches the learned airfoil latent space via gradient ascent. Benefiting from the CFD free scheme, DockingNet reduces the optimization computation cost from hours to seconds." @default.
- W4206780690 created "2022-01-25" @default.
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- W4206780690 date "2022-01-03" @default.
- W4206780690 modified "2023-09-27" @default.
- W4206780690 title "Deep neural network for airfoil optimization" @default.
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- W4206780690 doi "https://doi.org/10.2514/6.2022-0801" @default.
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