Aeroelasticity & Structural Dynamics in a Fast Changing World
17 – 21 June 2024, The Hague, The Netherlands
Home Program Author Index Search

Machine learning to forecast unsteady, motion-induced unsteady forces


Go-down ifasd2024 Tracking Number 30

Presentation:
Session: Data driven methods 2
Room: Room 1.1
Session start: 11:00 Thu 20 Jun 2024

Reik Thormann   reik.thormann@airbus.com
Affifliation: Airbus Operations

Hans Martin Bleecke   hans.bleecke@airbus.com
Affifliation: Airbus Operations


Topics: - Computational Aeroelasticity (High and low fidelity (un)coupled analysis methods:), - Reduced Order Modeling (High and low fidelity (un)coupled analysis methods:)

Abstract:

During flutter analyses of aircraft, a large parameter space has to be analyzed covering the extended flight envelope for a large set of mass variations and failure cases. With increasing aspect ratio and elasticity of the wing, flutter may become sensible with respect to the linearization point (lift coefficient, static aeroelastic equilibrium per dynamic pressure). Thus, assessing a good prediction of the flutter speed is crucial in a multi-disciplinary optimization already within the design phase. Therefore, machine learning techniques are explored to predict unsteady, motion induced unsteady forces for flutter analyses in a first step and secondly, consider amplitude-nonlinearities to support limit cycle analyses. The ROM setup is chosen such, that first the dimensionality of the problem is reduced. While for the structural deformations a transformation to modal coordinates is common, a so-called auto-encoder is used for the aerodynamic data. The modal coordinates are linked to the aerodynamic, latent subspace either via a Recurrent Neural Network (RNN) such as LSTM or a Transformer.