Machine learning to forecast unsteady, motion-induced unsteady forces

ifasd2024 Tracking Number 30

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.