Modeling acoustic metamaterials based on reused buttons using data fitting with neural network

Deep Learning-Based Design Method for Acoustic Metasurface Dual-Feature Fusion

Existing research in metasurface design was based on trial-and-error high-intensity iterations and requires deep acoustic expertise from the researcher, which severely hampered the development of the metasurface field. Using deep learning enabled the fast and accurate design of hypersurfaces. Based on this, in this paper, an integrated learning approach was first utilized to construct a model of the forward mapping relationship between the hypersurface physical structure parameters and the acoustic field, which was intended to be used for data enhancement. Then a dual-feature fusion model (DFCNN) based on a convolutional neural network was proposed, in which the first feature was the high-dimensional nonlinear features extracted using a data-driven approach, and the second feature was the physical feature information of the acoustic field mined using the model. A convolutional neural network was used for feature fusion. A genetic algorithm was used for network parameter optimization. Finally, generalization ability verification was performed to prove the validity of the network model. The results showed that 90% of the integrated learning models had an error of less than 3 dB between the real and predicted sound field data, and 93% of the DFCNN models could achieve an error of less than 5 dB in the local sound field intensity.

An Investigation of Modular Composable Acoustic Metamaterials with Multiple Nonunique Chambers

To make the sound absorber easy to fabricate and convenient for practical application, a modular composable acoustic metamaterial with multiple nonunique chambers (MCAM-MNCs) was proposed and investigated, which was divided into a front panel with the same perforated apertures and a rear chamber with a nonunique grouped cavity. Through the acoustic finite element simulation, the parametric studies of the diameter of aperture d, depth of chamber T0, and thickness of panel t0 were conducted, which could tune the sound absorption performances of MCAM-MNCs-1 and MCAM-MNCs-2 for the expected noise reduction effect. The effective sound absorption band of MCAM-MNCs-1 was 556 Hz (773-1329 Hz), 456 Hz (646-1102 Hz), and 387 Hz (564-951 Hz) for T = 30 mm, T = 40 mm, and T = 50 mm, respectively, and the corresponding average sound absorption coefficient was 0.8696, 0.8854, and 0.8916, accordingly, which exhibited excellent noise attenuation performance. The sound absorption mechanism of MCAM-MNCs was investigated by the distributions of the total sound energy density (TSED). The components used to assemble the MCAM-MNCs sample were fabricated by additive manufacturing, and its actual sound absorption coefficients were tested according to the transfer matrix method, which demonstrated its feasibility and promoted its actual application.

Longitudinal Wave Locally Resonant Band Gaps in Metamaterial-Based Elastic Rods Comprising Multi-Degree-of-Freedom DAVI Resonators

The wave propagation and vibration transmission in metamaterial-based elastic rods with periodically attached multi-degree-of-freedom (MDOF) dynamic anti-resonant vibration isolator (DAVI) resonators are investigated. A methodology based on a combination of the transfer matrix (TM) method and the Bloch theorem is developed, yielding an explicit formulation for the complex band structure calculation. The bandgap behavior of the periodic structure arrayed with single-degree-of-freedom (SDOF) DAVI resonators and two-degree-of-freedom (2DOF) DAVI resonators are investigated, respectively. A comparative study indicates that the structure consisting of SDOF DAVI resonators periodically jointed on the metamaterial-based elastic rod can obtain an initial locally resonant band gap 500 Hz smaller than the one given in the published literature. The periodic structure containing 2DOF DAVI resonators has an advantage over the periodic structure with SDOF DAVI resonators in achieving two resonance band gaps. By analyzing the equivalent dynamic mass of a DAVI resonator, the underlying mechanism of achieving a lower initial locally resonant band gap by this periodic structure is revealed. The parameters of the 2DOF DAVI resonator are optimized to obtain the lowest band gap of the periodic structure. The numerical results show that, with the optimal 2DOF DAVI parameters, the periodic structure can generate a much lower initial locally resonant band gap compared with the case before the optimization.

Design Optimization of Three-Layered Metamaterial Acoustic Absorbers Based on PVC Reused Membrane and Metal Washers

Waste management represents a critical issue that industrialized countries must necessarily deal with. Sustainable architecture involves the reuse of materials with the aim of significantly reducing the amount of waste produced. In this study, a new layered membrane metamaterial was developed based on three layers of a reused PVC membrane and reused metal washers attached. The membranes were fixed to a rigid support, leaving a cavity between the stacked layers. The samples were used to measure the sound absorption coefficient with an impedance tube. Different configurations were analyzed, changing the number of masses attached to each layer and the geometry of their position. These measurements were subsequently used to train a model based on artificial neural networks for the prediction of the sound absorption coefficient. This model was then used to identify the metamaterial configuration that returns the best absorption performance. The designed metamaterial behaves like an acoustic absorber even at low frequencies.

Low-Frequency Low-Reflection Bidirectional Sound Insulation Tunnel with Ultrathin Lossy Metasurfaces

We report both numerical and experimental constructions of a tunnel structure with low-frequency low-reflection bidirectional sound insulation (BSI). The designed tunnel was constructed from a pair of lossy acoustic metasurfaces (AMs), which consists of six ultrathin coiled unit cells, attached on both sides. Based on the generalized Snell′s law and phase modulations for both AMs, the tunnel with the low-frequency BSI was constructed based on sound reflections and acoustic blind areas created by the AMs. The obtained transmittances were almost the same for sound incidences from both sides and were lower than −10 dB in the range 337–356 Hz. The simulated and measured results agreed well with each other. Additionally, we show that the low-reflection characteristic of the tunnel can be obtained simultaneously by thermoviscous energy loss in coiled channels of the unit cells. Finally, an interesting application of the designed tunnel in an open-window structure with low-frequency low-reflection BSI is further simulated in detail. The proposed tunnel based on the ultrathin lossy AMs has the advantages of ultrathin thickness (about λ/35), low-frequency low-reflection BSI, and high-performance ventilation, which may have potential applications in architectural acoustics and noise control.

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 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.

On the Determination of Acoustic Properties of Membrane Type Structural Skin Elements by Means of Surface Displacements

The article focuses on the determination of the acoustic properties (sound transmission loss, sound absorption and transmission coefficient under acoustic plane wave excitation) of membrane-type of specimens by means of a combination of incident plane wave sound pressure and membrane surface displacement information, measuring the sound pressure with a microphone and the membrane displacement by means of a laser Doppler vibrometer. An overview of known measurement methods and the theoretical background of the proposed so-called mobility-based method (MM) is presented. The proposed method was compared with the conventional methods for sound transmission loss and absorption measurement in the impedance tube, both numerically and experimentally. Finite element model (FEM) simulation results of two single layer membrane samples of different shape configurations were compared, amongst which six different variations of the backing wall termination. Four different approaches to determine the sound transmission loss and two methods to determine sound absorption properties of the membranes were compared. Subsequently, the proposed method was tested in a laboratory environment. The proposed MM method can be possibly used to measure the vibro-acoustic properties of building parts in situ.

Design, Manufacturing, and Acoustical Analysis of a Helmholtz Resonator-Based Metamaterial Plate

Acoustic metamaterials are materials artificially engineered to control sound waves, which is not possible with conventional materials. We have proposed a design of an acoustic metamaterial plate with inbuilt Helmholtz resonators. The plate is made of Polylactic acid (PLA) which is fabricated using an additive manufacturing technique. It consists of Helmholtz resonator-shaped cavities of different sizes. In this paper, we have analyzed the acoustic properties of the Helmholtz resonators-based metamaterial plate experimentally as well as numerically. The experimental results are in good agreement with the numerical results. These types of 3D-printed metamaterial plates can find their application where high sound transmission loss is required to create a quieter ambience. There is an additional advantage of being lightweight because of the Helmholtz resonator-shaped cavities built inside the plate. Thus, these types of metamaterial plates can find their application in the design sector requiring lighter materials with high sound transmission loss.

Introduction to the special issue on machine learning in acoustics

The use of machine learning (ML) in acoustics has received much attention in the last decade. ML is unique in that it can be applied to all areas of acoustics. ML has transformative potentials as it can extract statistically based new information about events observed in acoustic data. Acoustic data provide scientific and engineering insight ranging from biology and communications to ocean and Earth science. This special issue included 61 papers, illustrating the very diverse applications of ML in acoustics.