Abstract
Aeroelastic instabilities such as flutter have a crucial role in limiting the operating range and reliability of turbomachinery. This paper offers an alternative approach to aeroelastic analysis, where the sensitivity of aerodynamic damping with respect to main flow and structural parameters is quantified through a surrogate-model-based investigation. The parameters are chosen based on previous studies and are represented by a uniform distribution within applicable intervals. The surrogate model is an artificial neural network, trained and tested to achieve an error within 1% of the test data. The quantity of interest is aerodynamic damping and the datasets are obtained from a linearised aeroelastic solver. The sensitivity of aerodynamic damping with respect to the input variables is obtained by calculating normalised gradients from the surrogate model at specific operating conditions. The results show a quantitative comparison of sensitivity across the different input parameters. The outcome of the sensitivity analysis is then used to decide the most appropriate action to take in order to induce stability in unstable operating conditions. The work is a preliminary study, carried out on a simplified two dimensional compressor cascade and it is aimed at proving the validity of a data-driven approach in studying the aeroelastic behaviour of turbomachinery. To the best of the authors’ knowledge, this is the first time a data-driven flutter model has been investigated. The initial results are encouraging, indicating that this approach is worth pursuing in the future. The presented framework can be used as a redesign tool to enhance the flutter stability of an existing blade.
Highlights
The development of highly loaded, three-dimensional, long compressor and fan blades yields a great challenge in terms of aeroelastic stability of turbomachinery
Four cases are setup to demonstrate the capability of the artificial neural network (ANN): 4 out of 5 input variables are fixed while the remaining one is swept
The surrogate model has been trained on results from a linearised aeroelastic solver and its validity has been tested through evaluation of relative errors
Summary
The development of highly loaded, three-dimensional, long compressor and fan blades yields a great challenge in terms of aeroelastic stability of turbomachinery. Self-excited vibration, or flutter, is among the several aeroelastic phenomena which can limit the operating range of turbomachinery and eventually lead to blade failure. The reduced frequency is addressed here as k = ωc/Urel , where ω is the angular vibration frequency, c is the blade chord and Urel is the inlet relative flow velocity. In an early analytical work [5], Whitehead presents unsteady lift calculations for a two-dimensional cascade of flat plates, emphasising that stability is affected, in different fashions depending on the flow variables (e.g., inlet Mach number), by the nature (cut-on waves propagate without attenuation, whereas cut-off waves decay as they travel) of acoustic waves produced by the vibration. The strong correlation between mean flow incidence and stability is shown in [6], using results from a complete analytical model
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