Inverse design of structured materials for broadband sound absorption

Wang, Yang; Zhao, Honggang; Yang, Haibin; Zhong, Jie; Yu, Dianlong; Wen, Jihong

doi:10.1088/1361-6463/abf373

Cited by 23 publications

(8 citation statements)

References 48 publications

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“…From the current research on broadband absorption, the trend of sidewall limitation can be observed from most broadband optimization procedures, e.g. [20] and [41]. In contrast, the absorption of sound waves by MAM mainly depends on the high energy dissipation at the connection between the film and the rigid body [33].…”

Section: Resultsmentioning

confidence: 99%

“…air-filled cavities [7][8][9][10][11][12][13][14], glass beads [15], piezoelectric patches [16], or locally resonant phononic crystals [17][18][19], are embedded in rubbery coatings to promote scattering and wave mode conversion, and thus to strengthen the sound energy dissipation. Among them, air-filled cavities, as the earliest utilized scatterer, can be easily fabricated and exhibit excellent underwater sound absorption performance, causing considerable interests in the field of underwater acoustic materials science [20,21]. However, the outstanding sound absorption performance only appear near the resonant frequency of the cavity or in the higher frequency band, and a sound absorption valley usually appears after the first resonant frequency, which limits its application in broadband sound absorption.…”

Section: Introductionmentioning

confidence: 99%

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An effective method to enhance the underwater sound absorption performance by constructing a membrane-type acoustic metamaterial

Sun¹,

Yuan²,

Jin³

et al. 2022

J. Phys. D: Appl. Phys.

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Broadband sound absorption has consistently been a challenge in designing underwater sound absorption structure. Most research of underwater sound absorption structures achieve broadband sound absorption through structural optimization, which curbs the freedom of designing, and commonly alights it at the expense of increased thickness. In this paper, a method is reported to broaden the frequency band of the underwater sound absorption structure by embedding a membrane-type resonator into the cavity, which forming a membrane-type underwater acoustic absorption metamaterial. We demonstrate the mechanism of membrane-type metamaterial by theory, and verify it by simulation and experiment. The experimental results show that the sound absorption coefficient in the frequency range of 2000-10000 Hz is significantly improved after implanting the membrane-type resonator into the cavity. The average sound absorption coefficient is increased by nearly 17%, and the improvement effect of the sound absorption covers to each frequency point, which is consistent with our expectation. As the case of applying membrane-type metamaterials to the design process of underwater acoustic structures, this research possesses great application potential in acoustic wave communication and device compatibility design technologies.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An effective method to enhance the underwater sound absorption performance by constructing a membrane-type acoustic metamaterial

Sun¹,

Yuan²,

Jin³

et al. 2022

J. Phys. D: Appl. Phys.

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“…To reverse engineer acoustic coatings with embedded voids to achieve desired acoustic performance, metaheuristic optimization techniques such as genetic algorithms (GA) (Meng et al 2012a;Yuan et al 2019;Chang et al 2005;Wang et al 2021) and evolutionary algorithms (EA) (Romero-García et al 2009;Zhao et al 2018) are widely used. Interest has been given to GA as it can be used to find a near-optimal solution efficiently (Meng et al 2012a;Yuan et al 2019;Chang et al 2005).…”

Section: Introductionmentioning

confidence: 99%

A machine learning accelerated inverse design of underwater acoustic polyurethane coatings

Weeratunge

Shireen

Sagar

et al. 2022

Struct Multidisc Optim

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Here we propose a detailed protocol to enable an accelerated inverse design of acoustic coatings for underwater sound attenuation application by coupling Machine Learning and an optimization algorithm with Finite Element Models (FEM). The FEMs were developed to obtain the realistic performance of the polyurethane (PU) acoustic coatings with embedded cylindrical voids. The frequency dependent viscoelasticity of PU matrix is considered in FEM models to substantiate the impact on absorption peak associated with the embedded cylinders at low frequencies. This has been often ignored in previous studies of underwater acoustic coatings, where usually a constant frequency-independent complex modulus was used for the polymer matrix. The key highlight of the proposed optimization framework for the inverse design lies in its potential to tackle the computational hurdles of the FEM when calculating the true objective function. This is done by replacing the FEM with an efficiently computable surrogate model developed through a Deep Neural Network. This enhances the speed of predicting the absorption coefficient by a factor of $$4.5 \times 10^3$$ 4.5 × 10 3 compared to FEM model and is capable of rapidly providing a well-performing, sub-optimal solution in an efficient way. A significant, broadband, low-frequency attenuation is achieved by optimally configuring the layers of cylindrical voids. This is accomplished by accommodating attenuation mechanisms, including Fabry–P$$\acute{e}$$ e ´ rot resonance and Bragg scattering of the layers of voids. Furthermore, the proposed protocol enables the inverse and targeted design of underwater acoustic coatings through accelerating the exploration of the vast design space compared to time-consuming and resource-intensive conventional trial-and-error forward approaches.

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“…To reverse engineer acoustic coatings with voids to achieve desired acoustic performance, metaheuristic optimization techniques such as genetic algorithms (GA) [14,25,26,27] and evolutionary algorithms (EA) [28,29] are widely used. Particular interest has been given to GA as it can be used to find a near-optimal solution efficiently [14,25,26].…”

Section: Introductionmentioning

confidence: 99%

A machine learning accelerated inverse design of underwater acoustic polyurethane coatings with cylindrical voids

Weeratunge¹,

Shireen²,

Sagar³

et al. 2022

Preprint

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Here, we report the development of a detailed "Materials Informatics" framework for the design of acoustic coatings for underwater sound attenuation through integrating Machine Learning (ML) and statistical optimization algorithms with a Finite Element Model (FEM). The finite element models were developed to simulate the realistic performance of the acoustic coatings based on polyurethane (PU) elastomers with embedded cylindrical voids. The FEM results revealed that the frequency-dependent viscoelastic behavior of the polyurethane matrix has a significant impact on the magnitude and frequency of the absorption peak associated with the cylinders at low frequencies, which has been commonly ignored in previous studies on similar systems. The data generated from the FEM was used to train a Deep Neural Network (DNN) to accelerate the design process, and subsequently, was integrated with a Genetic Algorithm (GA) to determine the optimal geometric parameters of the cylinders to achieve maximized, broadband, low-frequency waterborne sound attenuation. A significant, broadband, low-frequency attenuation is achieved by optimally configuring the layers of cylindrical voids and using attenuation mechanisms, including Fabry-Pérot resonance and Bragg scattering of the layers of voids. Integration of the machine learning technique into the optimization algorithm further accelerated the exploration of the high dimensional design space for the targeted performance. The developed DNN exhibited significantly increased speed (by a factor of 4.5 × 10 3 ) in predicting the absorption coefficient compared to the conventional FEM(s). Therefore, the acceleration brought by the materials informatics framework brings a paradigm shift to the design and development of acoustic coatings compared to the conventional trial-and-error practices.

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Inverse design of structured materials for broadband sound absorption

Cited by 23 publications

References 48 publications

An effective method to enhance the underwater sound absorption performance by constructing a membrane-type acoustic metamaterial

An effective method to enhance the underwater sound absorption performance by constructing a membrane-type acoustic metamaterial

A machine learning accelerated inverse design of underwater acoustic polyurethane coatings

A machine learning accelerated inverse design of underwater acoustic polyurethane coatings with cylindrical voids

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