Manufacturing Uncertainties for Acoustic Liners

Abstract

The present chapter investigates an uncertainty quantification (UQ) approach for the simulations of aircraft noise (overall sound pressure level—OASPL) attenuation performed by acoustic liners with respect to manufacturing tolerances. They are devices typically installed inside engine nacelles of turbofan airliners to mitigate the noise produced by the engines. Aircraft noise pollution is harmful to people onboard but mostly to those on ground near airports and is constantly addressed by stringent ICAO standards updates. The targeted test case within the UMRIDA project is the mathematical representation of a real regional jet engine nacelle from Leonardo Finmeccanica production. An acoustic liner in the simplest form consists of a sandwich panel with a top perforated sheet, an interior honeycomb structure, and a rigid back plate, and at a minimum, it can be characterized by four main independent geometrical uncertainties due to manufacturing tolerances. The methodology proposed consists first of all in the execution of experimental tests, needed for the determination of a database of measurements. The database is then used to quantify the geometrical uncertainties of the acoustic panel through the application of a dedicated tool of modeFRONTIER software developed by ESTECO: the distribution fitting tool to find the statistical distribution which better fits the experimental data. Subsequently, in order to quantify accurately the performance distributions, a numerical model of the liner, provided by FNM, is integrated into modeFRONTIER, allowing the automatic execution of a series of computational acoustic simulations with MSC ACTRAN software developed by Free Field Technology. The results are therefore interpreted by modeFRONTIER, accordingly to the tools developed during UMRIDA project, to obtain the UQ of the acoustic performance following two criteria: highest accuracy and lowest number of sampling points. Since the number of uncertainties considered is not large, the use of a SS-ANOVA screening to determine the most important ones is not necessary. We will therefore apply a modified version of regression analysis, called adaptive sparse polynomial chaos methodology (Blatman and Sudret, Adaptive Sparse Polynomial Chaos Expansion Based on Least Angle Regession, 2010) [1], which aims not to reduce the number of uncertainties to apply polynomial chaos expansion to, but rather to reduce the number of the terms of the same polynomial chaos expansion to avoid overfitting. Thus, it is possible to perform an accurate UQ by a reduced number of sampling points (making the subsequent RDO more feasible), without completely discarding any of the uncertain parameters.