Reik Thormann, Hans Martin Bleecke

DOI Number: N/A

Conference number: IFASD-2024-030

In this paper, the combination of an auto-encoder coupled with a transformer is presented to predict unsteady surface pressures due to forced excitation. While the used neural-
network architecture operates in the the time domain, training data are computed with the linearized, frequency-domain CFD solver for a generic, high-aspect-ratio wing. These data are transfered into the time-domain to train the network, while the network’s outputs are Fourier transformed and compared to their corresponding reference. Results are compared for unsteady, local surface pressures, frequency response functions of the generalized aerodynamic force matrices as well as flutter predictions.

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In Categories: 1. IFASD 2024, 1.Events, Steady/Unsteady Aerodynamics
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