Volume 4, Number 3, 2020
|Number of page(s)||13|
|Section||Acoustic Materials and Metamaterials|
|Published online||19 June 2020|
Generation of uncertainty envelopes for thin acoustic screens with uncertain parameters
LAUM UMR CNRS 6613, Le Mans Université, 72085 Le Mans cedex, France
2 MWL, KTH Royal Institute of Technology, 100 44 Stockholm, Sweden
3 Matelys Research Laboratory, 69120 Vaulx-en-Velin, France
* Corresponding author: firstname.lastname@example.org
Accepted: 25 May 2020
The properties of the materials used for building sound proofing systems are known to exhibit large variations. These may lead to significant differences in the acoustic responses within a given material batch, particularly when resistive screens are used as a surface component for a multi-layered absorbing panel. In such thin films, it is mostly the thickness and the flow resistivity, but in some cases also the porosity, that are difficult to control in the production process. All these potential variations influence the acoustic response of the complete panel. In the present contribution, a method to isolate and evaluate the effect of uncertainties in a film is proposed. Using a transmission line approach, it is shown to be possible to predict the modification of the response induced by the uncertainties. The proposed technique is then adapted to determine uncertainty envelopes of the absorption coefficient, for experimentally acquired responses, that are closer to measured envelopes as compared to those generated using Monte Carlo simulations or simplified approaches. The method is tested both on numerical and experimental cases and shows, in both cases, a very good agreement with the reference solutions. Unlike Monte Carlo approaches, the proposed method does not require a massive computational effort which makes it suitable for real life applications.
Key words: Acoustics screens / Uncertainties / Thin layers / Poroelastic media
© M. Gaborit et al., Published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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