High-speed fluid–structure interaction predictions using a deep learning transformer architecture

This paper presents the development and application of a Transformer deep-learning model to fluid–structure problems induced by shock-turbulent boundary layer interaction. The model was trained on data from experiments conducted at a hypersonic wind tunnel under flow conditions that allowed for a Mach number of 5.3 and a Reynolds number of ∼19.3×106/m. The shock-wave turbulent boundary layer interaction occurred over an elastic panel. The Transformer was trained using panel deformation measurements taken at different probe locations and the pressure in the cavity beneath the panel. The trained Transformer was subsequently applied to unseen data corresponding to various mean cavity pressures and panel deformations. The capability of the Transformer to capture aeroelastic trends is promising, with interpolation accuracy shown to depend on the volume of data used in training and the location to which the model is applied. The practical implications of this study for aeroelastic research are significant, offering new insights and potential solutions to real-world aeroelastic challenges.