Physics-based aeroacoustic modelling of bluff-bodies

Abstract

In this work physics-based modelling of bluff-body noise was performed with application to landing gear noise production. The landing gear is a primary contributor to airframe noise during approach. Noise is primarily generated from the unsteady pressures resulting from the turbulent flow around various components. The research was initiated in response to the need for an improved understanding of landing gear noise prediction tools. A computational approach was adopted so that the noise generating physics of the problem could be captured. Governing laws were solved numerically to predict the noise source characteristics and the resulting acoustic far-field. Three-dimensional compressible Navier-Stokes simulations were performed to solve the unsteady turbulent near-field flow and the acoustic analogy was used to predict the resulting far-field acoustic pressure. The flow solver included a high-order computational aeroacoustics code adopting large-eddy simulation, whilst a Ffowcs Williams and Hawkings solver was used for the acoustic prediction. Circular cylinders in various configurations were selected to represent basic landing gear struts and results were used to form a modelling database. Initially, cylinders at various Reynolds numbers were investigated in cross-flow to determine the noise characteristics of a simple model strut. The work was extended to investigate the effect of strut alignment to the flow by simulating cylinders in yaw. The effect of yaw was shown to modify the peak level and frequency of far-field noise spectra. Component interaction effects were then investigated by simulating cylinders in tandem arrangements. The resulting aerodynamic and far-field noise characteristics were shown to be complex and extremely sensitive to the separation distance between the cylinders. Finally, a prediction model was developed and validated by comparing predictions against theory and measurements of the noise radiated by a simple two-wheel landing gear model. The results demonstrated the capability of the model to accurately predict correct spectral and directivity characteristics.

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