Automated parameterization for aerodynamic shape optimization via deep geometric learning
File(s)
Accepted version
Author(s)
Type
Journal Article
Abstract
Traditional aerodynamic shape optimization (ASO) is costly and time-intensive, requiring extensive manual tuning for parameterization to define the design space and manipulate geometries. Conventional parameterization methods limit flexibility and demand specialized knowledge, making efficient ASO challenging. We introduce the deep geometric mapping (DeepGeo) model, a neural-network-based approach that automates parameterization, accelerating ASO and reducing costs. By leveraging the expressive capacity of neural networks, DeepGeo handles large shape deformation while preserving global surface smoothness, allowing efficient optimization in high-dimensional design spaces. Eliminating the need for training datasets and manual hyperparameter tuning and integrating computational fluid dynamics (CFD) mesh deformation, DeepGeo significantly lowers ASO’s implementation complexity and cost. Case studies, including two-dimensional circle, Common Research Model wing, and blended-wing–body aircraft optimization, validate DeepGeo’s effectiveness, achieving results comparable to state-of-the-art free-form deformation with minimal manual intervention. This work positions DeepGeo as a novel and effective parameterization framework that streamlines the workflow and reduces implementation complexity in advanced ASO.
Date Issued
2025-12-01
Date Acceptance
2025-05-16
Citation
AIAA Journal, 2025, 63 (12), pp.5430-5447
ISSN
0001-1452
Publisher
American Institute of Aeronautics and Astronautics (AIAA)
Start Page
5430
End Page
5447
Journal / Book Title
AIAA Journal
Volume
63
Issue
12
Copyright Statement
© 2025 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved. This is the author’s accepted manuscript made available under a CC-BY licence in accordance with Imperial’s Research Publications Open Access policy (www.imperial.ac.uk/oa-policy)
License URL
Publication Status
Published
Date Publish Online
2025-07-24