Zhuang Mo, Guochenhao Song, J. Stuart Bolton, Seungkyu Lee, Tongyang Shi and Yongbeom Seo, “Predicting Acoustical Performance of High Surface Area Particle Stacks with a Poro-Elastic Model,” in Proceedings of InterNoise 2021, 7 pages, 1-5 August, 2021.


Because of the high sound absorption they offer at low frequencies, there is a growing interest in high surface area particles and how they might be applied in noise control. Therefore, a model that can accurately predict the acoustic behavior of this type of materials will be useful in relevant applications. A poro-elastic model based on a combination of Biot theory and an existing rigid model of granular activated carbon (GAC) is introduced in the current work. The input parameters for this model consist of a certain number of properties that are known by measurement, and a set of values obtained by matching the model prediction with acoustic measurements. Measured absorption coefficients and surface impedance of stacks of several types of different activated carbon particles are shown in this paper. A fitting procedure that determines the unknown parameters is also described. It is shown that the model is able to predict the acoustic behavior of the particle stacks, and especially to capture the frame resonances at low frequencies, thus, validating the proposed model. Beyond the activated carbon used in the present tests, it is reasonable to generalize this model to stacks of other high surface area particles.


Activated carbon, Granular, Sound absorption, Poro-elastic, Nanoporosity


Acoustics and Noise Control

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