A. Schreiber, R.Y. Yanev, I. Staack, U. Scholz

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.

Deutscher Luft- und Raumfahrtkongress 2025, Augsburg

Deutsche Gesellschaft für Luft- und Raumfahrt - Lilienthal-Oberth e.V., Bonn, 2025

Conference Paper

englisch

21,0 x 29,7 cm, 12 Seiten

urn:nbn:de:101:1-2510241301071.164089603828

10.25967/650353

data-driven design, kinematics, generative modeling, design space exploration, variational autoencoder

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Schreiber, A.; Yanev, R.Y.; et al. (2025): Generative Modeling for Flap Kinematics: Enhancing Design Pipelines with Variational Autoencoders. Deutsche Gesellschaft für Luft- und Raumfahrt - Lilienthal-Oberth e.V.. (Text). https://doi.org/10.25967/650353. urn:nbn:de:101:1-2510241301071.164089603828.

24.10.2025