Prediction and Improvement of Aircraft Cabin Acoustics using Statistical Energy Analysis and Sound Quality Evaluation

Abstract

Aircraft interior acoustic design is a key influencer for cabin comfort. An essential part of the design is optimization of acoustic insulation systems under weight restrictions to create a pleasant environment for human ear. Considering the complexity of aircraft geometry, noise sources, and transfer paths, computational prediction techniques become invaluable tools for increasing the accuracy in material selection while reducing design time and costs. In this study, a procedure that integrates sound quality evaluation with Statistical Energy Analysis (SEA) to design aircraft acoustic insulation systems is described. SEA is employed to predict the cabin sound pressure levels of a narrow body aircraft insulated with sound absorption and vibration damping materials. Aircraft cabin including under-floor sections is modelled based on 3D airframe and VIP style interior design and the model is validated with flight test data. Transfer functions obtained from SEA model for selected transfer paths are utilized to filter the noise signal recorded with a binaural recording system during flight. Sound quality metrics are computed in order to map perceptive response. An iterative process is introduced to improve acoustic design by investigating the effects of different sound insulation systems and room absorption values on noise levels and sound quality metrics.