SDF-GAN: aerofoil shape parameterisation via an adversarial auto-encoder

Abstract

Current aerodynamic design processes suffer from expensive optimisation procedures, in part due to the requirement to search large design spaces. Recent advances in deep learning, and more precisely the development of high quality generative models such as Generative Adversarial Networks (GANs) and Variational Auto-Encoders (VAEs), have opened up new possibilities for shape parameterisation and aerodynamic design. As the focus of work in this area so far has primarily been on generative aerofoil design via coordinate-based shape representations,aerofoil generation via image-based shape representation is yet to receive much attention. This study aims to rectify this by investigating aerofoil shape generation using signed distance field geometry representations. A key issue with direct geometry representation is the potential lack of smoothness in the output design. Two features are proposed to mitigate this: the first is an auto-encoding architecture, which was found to help drive the output designs to smoother geometries; the second is a Hanning filter provided by XFOIL, which acts to smooth the surface speed distribution and return the corresponding geometry. Network hyper-parameters are set through an optimisation procedure using an LP-tau design of experiments. The approach is compared to alternative state-of-the-art parameterisation techniques.

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Identifiers

ORCID for Joshua Thomas Bamford: orcid.org/0000-0001-7416-5703

ORCID for Andy Keane: orcid.org/0000-0001-7993-1569

ORCID for David Toal: orcid.org/0000-0002-2203-0302

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