The scope of this work is to consolidate research dealing with vibroacoustics of periodic media. This investigation aims at developing and validating tools for the design 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 solid (but still non-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, some design guidelines are provided in order to predict several possible sets of characteristic parameters (i.e. 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 (i.e. the frequency at which the first Transmission Loss peak appears, together with its amplitude). 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.

Optimizing the acoustic properties of a meta-material using machine learning techniques

CASABURO ALESSANDRO
Primo
;
MAGLIACANO DARIO
Secondo
;
PETRONE GIUSEPPE;FRANCO FRANCESCO
Penultimo
;
DE ROSA SERGIO
Ultimo
2021

Abstract

The scope of this work is to consolidate research dealing with vibroacoustics of periodic media. This investigation aims at developing and validating tools for the design 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 solid (but still non-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, some design guidelines are provided in order to predict several possible sets of characteristic parameters (i.e. 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 (i.e. the frequency at which the first Transmission Loss peak appears, together with its amplitude). 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.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/856346
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