Interior Active Noise Control in Turbofan Aircraft: Optimal Actuators Positioning Using Dedicated Genetic Algorithms

Abstract

This paper presents the activities developed by the authors within the first year of the research project named M.E.S.E.M.A. (Magnetoelastic Energy Systems for Even More Electric Aircraft) funded by the European Commission within the 6th Framework Program and coordinated by the “Dipartimento di Ingegneria Aerospaziale” of the University of Naples “Federico II” (DPA). One of the main targets for the MESEMA Consortium consists in reducing the level of disturbance noise in turbofan aircraft; a noise & vibration control system using magnetostrictive actuators has been designed and produced, with the goal of controlling noise & vibrations in a wide band frequency range between 150 – 500 Hz. The environmental noise & vibration excitations was representative of a small/medium turbofan aircraft case. Final results of the task will be represented by a system made up of 30 actuation/sensing devices connected to a system performing control of external disturbances as well as of the devices’ intrinsic non linearity. As experimental test article a fuselage mockup of the ATR42/72 aircrafts family has been chosen available at the acoustic laboratory in the Alenia plant; due to its geometry and overall dimensions it well represents a fuselage section of an hypothetic regional jet. Within this paper the authors present most of the numerical activities developed in the project in order to design the actuation system as well as to optimize the location of the control devices. The numerical (finite element) model of the mock-up has been developed, correlated with experimental modal analysis results and updated in order to match the best way possible the experimental reality. This model has then been employed to carry out a deep simulation activity aimed at the evaluation of the required control actuators performances in terms of force spectra as far as their optimal placements for control purposes. In order to select among the many possible set of control actuator configurations an optimisation activity was required. The used optimisation method is based on “genetic” algorithms. For this analysis 126 actuators potential locations were selected on frames or stiffeners of the two middle bays of the mock-up. The authors developed the genetic algorithm code in MATLAB framework. The paper presents in the end the experimental characterization of the control actuators demonstrating how a good correlation between numerical and experimental activities has been achieved.