Improving acoustic wave propagation models in highly attenuating porous materials

This article presents an improved and extended modeling approach for acoustic wave propagation in rigid porous materials, focusing on examples, such as plastic foams used for noise reduction in automotive applications. We demonstrate that the classical model (Johnson-Champoux-Allard) in the asymptot...

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Bibliographic Details
Published in:The Journal of the Acoustical Society of America 2024-01, Vol.155 (1), p.206-217
Main Authors: Bouchendouka, A., Fellah, Z. E. A., Nguyen, C. T., Ogam, E., Perrot, C., Duval, A., Depollier, C.
Format: Article
Language:English
Subjects:
Acoustics
Engineering Sciences
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Summary:This article presents an improved and extended modeling approach for acoustic wave propagation in rigid porous materials, focusing on examples, such as plastic foams used for noise reduction in automotive applications. We demonstrate that the classical model (Johnson-Champoux-Allard) in the asymptotic high-frequency limit, widely employed in the literature, fails to accurately reconstruct the transmitted acoustic signal through high absorbent porous materials characterized by significant wave attenuation. The study focuses on the airborne ultrasonic frequency range (30–200 kHz). To address this limitation, we introduce new non-acoustic parameters Σ and V for viscous effects, and Σ ′ and V ′ for thermal effects, with surface and volumetric dimensions, respectively, allowing for the reconstruction of the transmitted signal and accurate modeling of the pronounced acoustic attenuation within the material. These parameters are incorporated into the expansion on skin depths of the dynamic tortuosity α(ω) and thermal tortuosity α ′ (ω) response functions, which describe the inertial-viscous and thermal interactions between the fluid and the solid, respectively. This novel modeling approach enables a more comprehensive study of high attenuating porous media, which are crucial for effective noise reduction. Additionally, it opens up new possibilities for characterization beyond the capabilities of current models.
ISSN:0001-4966
1520-8524
DOI:10.1121/10.0024008