A statistical model for landing gear noise prediction

Abstract

This paper presents the development of a framework for aircraft landing gear noise prediction. A prediction model is derived that decomposes the landing gear noise into three spectral components, for the low, mid and high frequencies, respectively. This corresponds to cataloguing the parts in the landing gear assembly into three groups, namely, the wheels for low frequencies, the main struts for mid frequencies and the small details for high frequencies. The spectral decomposition is demonstrated by experimental data from a full-scale Boeing 737 landing gear test, which show different spectral characteristics of the noise in the three different frequency domains. In each frequency domain, asymptotic results are derived for the farfield noise, by making use of different length scales to simplify the phase behavior of the sources. The derived results require as input only some statistical descriptions of the surface pressure fluctuations and the geometry of the landing gear assembly. Some simple examples are given to demonstrate the features of the predicted noise, which show trends consistent with experimental data. The frequency domain decomposition also points to simple ways of obtaining the surface pressure properties required for noise prediction, which is also discussed in this paper.