A comprehension and prediction of wings in unsteady conditions

Abstract

This thesis presents experimental and computational work that explores the complex dynamics of rigid and aeroelastic wings under the influence of unsteady, turbulent flow conditions pre and post-stall. Such conditions are increasingly relevant to modern aerospace applications, where understanding and predicting the behaviour of wings can significantly enhance aircraft performance and safety.

The study focuses on the impact of varying the integral length scale on the aerodynamic performance of wings at moderate Reynolds numbers. Through experimental investigations using hot wire anemometry, force and moment measurements, particle image velocimetry, and digital image correlation, detailed insights into the response of rigid and aeroelastic wings alongside their surrounding flow fields are obtained. In particular, the dynamics of variations in the integral length scale of high-intensity free-stream turbulence are investigated. This thesis also incorporates data assimilation techniques to refine the predictions of flow behaviour around stalled airfoils subjected to free-stream turbulence. Specifically focusing on the application of data assimilation to high Reynolds number time-averaged flows, and investigating the scaling behaviour for different Reynolds numbers and turbulence intensities.

Key findings from this research include the identification of peak force fluctuations for the rigid wing at integral length scales equal to half the wing's chord and how these scales influence vortex shedding and stall characteristics. For aeroelastic wings, the interaction between structural responses (bending, torsion and surge) and turbulent flow reveals that varying the integral length scale of the incoming turbulence leads to the preferential amplification of certain frequencies over others.

The thesis advances the application of a state observer method to enhance the accuracy of existing turbulence models. The method is capable of improving results where experimental data is known for a range of Reynolds numbers and turbulence intensities. Additionally, by scaling the forcing term or inlet conditions, the method accurately predicts flows where experimental data is unknown.

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Identifiers

ORCID for Bharath Ganapathisubramani: orcid.org/0000-0001-9817-0486

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