Gaussian-Based Machine Learning Algorithm for the Design and Characterization of a Porous Meta-Material for Acoustic Applications

The scope of this work is to consolidate research dealing with the vibroacoustics of periodic media. This investigation aims at developing and validating tools for the design and characterization of global vibroacoustic treatments based on foam cores with embedded periodic patterns, which allow pass...

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Bibliographic Details
Published in:Applied sciences 2022-01, Vol.12 (1), p.333
Main Authors: Casaburo, Alessandro, Magliacano, Dario, Petrone, Giuseppe, Franco, Francesco, De Rosa, Sergio
Format: Article
Language:English
Subjects:
Acoustics
Air flow
Algorithms
Artificial intelligence
equivalent fluid
Gaussian process
Inclusions
Learning algorithms
Machine learning
meta-material
Metamaterials
Neural networks
Passive control
porous foam
Porous materials
Preliminary designs
Transmission loss
Unit cell
Vibroacoustics
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Summary:The scope of this work is to consolidate research dealing with the vibroacoustics of periodic media. This investigation aims at developing and validating tools for the design and characterization of global vibroacoustic treatments based on foam cores with embedded periodic patterns, which allow passive control of acoustic paths in layered concepts. Firstly, a numerical test campaign is carried out by considering some perfectly rigid inclusions in a 3D-modeled porous structure; this causes the excitation of additional acoustic modes due to the periodic nature of the meta-core itself. Then, through the use of the Delany–Bazley–Miki equivalent fluid model, some design guidelines are provided in order to predict several possible sets of characteristic parameters (that is unit cell dimension and foam airflow resistivity) that, constrained by the imposition of the total thickness of the acoustic package, may satisfy the target functions (namely, the frequency at which the first Transmission Loss (TL) peak appears, together with its amplitude). Furthermore, when the Johnson–Champoux–Allard model is considered, a characterization task is performed, since the meta-material description is used in order to determine its response in terms of resonance frequency and the TL increase at such a frequency. Results are obtained through the implementation of machine learning algorithms, which may constitute a good basis in order to perform preliminary design considerations that could be interesting for further generalizations.
ISSN:2076-3417
2076-3417
DOI:10.3390/app12010333