The work proposes a methodology for the assessment of the performances of Passive Noise Control (PNC) for passenger aircraft headrests with the aim of enhancing acoustic comfort. Two PNC improvements of headrests were designed to reduce the Sound Pressure Level (SPL) at the passengers’ ears in an aircraft cabin during flight; the first was based on the optimization of the headrest shape, whereas the second consisted of partially or fully covering the headrest surface with a new highly sound-absorbing nanofibrous textile. An experimental validation campaign was conducted in a semi-anechoic chamber. A dummy headrest was assembled in different configurations of shape and materials to assess the acoustic performances associated to each set up. In parallel, simulations based on the Boundary Element Method (BEM) were performed for each configuration and an acceptable correlation between experimental and numerical results was obtained. Based on these findings, general guidelines were proposed for the acoustical design of advanced headrests.

Localization and quantification of noise sources are important to fulfill customer and regulation requirements in a such competitive sector like automotive manufacturing. Wind tunnel testing and acoustic mapping techniques based on microphone arrays can provide accurate information on these aspects. However, it is not straightforward to get source positions and strengths in these testing conditions. In fact, the car is a 3D object that radiates noise from different parts simultaneously, involving different noise generation mechanisms such as tire noise and aerodynamic noise. Commonly, acoustic maps are produced on a 3D surface that envelopes the objects. However, this practice produces misleading and/or incomplete results, as acoustic sources can be generated outside the surface. When the hypothesis of sources on the model surface is removed, additional issues arise. In this paper, we propose exploiting an inverse method tailored to a volumetric approach. The aim of this paper is to investigate the issues to face when the method is applied to automotive wind tunnel testing. Two different kinds of problem must be considered: On the one hand, the results of inverse methods are strongly influenced by the problem definition, while, on the other hand, experimental conditions must be taken into account to get accurate results. These aspects have been studied making use of simulated experiments. Such a controlled simulation environment, by contrast to a purely experimental case, enables accurate assessment of both the localization and quantification performance of the proposed method. Finally, a set of scores is defined to evaluate the resulting maps with objective metrics.

Every product is growingly being evaluated in terms of acoustic characteristics. The most accurate way to rate sound quality is by performing jury tests; however, jury tests require a lot of time and human resources. To overcome this problem, jury tests results can be correlated to objective sound quality metrics owing to the fact that objective metrics could be easily obtained from sound data. In this study, advanced techniques for feature identification are explored to correlate objective metrics to subjective perception retrieved from jury tests. The data set refers to the interior noise of a regional propeller aircraft. Artificial Neural Network and two regression models (i.e. linear and quadratic regression models) have been chosen to predict subjective metrics according to the objective data. To obtain the optimized model parameters for the regression models, a Genetic Algorithm has been used as optimization strategy. In each modelling, 85 percent of sound sample data have been utilized to perform the model and remaining 15 percent have been reserved for testing the models. The results showed that the Artificial Neural Network can provide better prediction.

In turboprop aircraft, the low-frequency noise field created by the propellers is the major contributor to the interior vibro-acoustic field, which determines a passenger’s discomfort. This paper deals with the experimental assessment of an active noise control (ANC) system for cabin seat headrests using two loudspeakers placed on both sides of the passenger’s head to create a local zone of quiet around the passenger’s ears. To deal with time-varying disturbances, the developed ANC system utilized a two-input-two-output filtered-X LMS algorithm developed in MATLAB/Simulink and implemented on a DSPACE control board to drive the secondary speakers and cancel the unwanted low-frequency noise components. The performance of the active headrest was investigated through real-time experimentation involving sensors, actuators, and electronic devices. The test results showed that up to approximatively 20 dB of sound attenuation could be realized in the passenger’s ears over a narrowband sound field replicated under laboratory conditions. Such achievements represent an excellent starting point toward practical applications in the design of more comfortable and acoustically efficient aircraft cabin seats.

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