Robust Optimization of Acoustic Liners

Abstract

The present chapter documents the innovative methodology for the automatic multi-objective robust design optimization of engine nacelle acoustic liners. These devices are typically installed inside nacelles of turbofan aircraft to attenuate the noise produced by the engines as aircraft noise pollution is harmful to people onboard but mostly to those on ground near airports and is regulated by ICAO standards constantly revised. The targeted test case analyzed within the UMRIDA project is the mathematical model of a real regional jet engine nacelle selected from Leonardo Finmeccanica vast production. This 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, starting-point to quantify the geometrical uncertainties of the acoustic panels, through the application of a dedicated tool of modeFRONTIER software from ESTECO, i.e., the distribution fitting tool. This allows to find the statistical distribution which better fits the experimental data. Then, to accurately quantify the performance distributions, a numerical model of the liner incorporating proprietary semi-empirical impedance model is integrated in modeFRONTIER workflow, allowing the automatic execution of a series of acoustic simulations with MSC ACTRAN software developed by Free Field Technology. The results are processed with a Python script to have the PLTViewer extract the concise performance index overall sound pressure level (OASPL). Since the number of uncertainties considered is not large, using the efficient adaptive sparse Polynomial Chaos methodology, it is possible to perform an accurate UQ keeping the number of sampling points low and increasing accuracy by avoiding overfitting. We based the final robust design optimization on efficient single-objective reliability-based formulation, using two optimization algorithms SIMPLEX and MOGAII to compare against in terms of effectiveness versus computational cost.