Generative Modeling for Flap Kinematics: Enhancing Design Pipelines with Variational Autoencoders
Year: 2025
Type: article
Abstract: The preliminary design of high-lift configurations for fixed-wing aircraft is a complex, multi-disciplinary task usually relying on extensive Design of Experiment (DoE) studies. This work presents a Variational Autoencoder (VAE)-based pipeline for generating Adaptive Dropped-Hinge Flap (ADHF) concepts that satisfy engineering constraints on cruise drag, separation behavior, and actuation efficiency. Trained on a curated set of desirable designs, the VAE produces novel configurations with characteristics similar to proven solutions, enabling efficient exploration of the design space. A post-processing framework evaluates these candidates and relates them to the original user input. The ADHF-VAE pipeline delivers a broader and higher-quality range of designs in significantly fewer iterations than standard DoE, providing improved starting points for downstream aerodynamic, kinematic, and integration analyses. This framework represents a first step towards automated, data-driven synthesis of high-lift systems for future aircraft design.
DOI: 10.25967/650353
Authors: Schreiber, A., Yanev, R.Y., Staack, I., Scholz, U.
Language: English
Subfield: Global and Planetary Change
Field: Environmental Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7094957575
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