Component-Based Empirical Model for High-Lift System Noise Prediction

Abstract

This paper presents an empirical model for predicting noise from high lift systems, derived from a large database of airframe noise tests, involving various airplane models at various operating conditions. The model correlates noise not only to gross airplane parameters such as the dimensions of the high lift system and flight Mach number, but also to flow quantities that are physically responsible for the noise generation. Noise data used in the development of the model were acquired by using phased microphone arrays, which enables the decomposition of the total noise into components, relating the noise to the six individual components of the wing/high lift system. The methodology and results of this component-based model is presented, including source identification by source strength maps, component integration to derive far field spectra, validation/calibration of the integrated spectra by conventional free field microphone data, extrapolation of small-scale model test data to full-scale conditions with Reynolds number dependent scaling laws and the correlation between noise and flow quantities. Validations of the predictions with flight test data are also given to show the accuracy of the developed prediction tool.