Machine-Learning-Assisted Acoustic Consecutive Fano Resonances: Application to a Tunable Broadband Low-Frequency Metasilencer

Xu, Zi-Xiang; Zheng, Bin; Yang, Jing; Liang, Bin; Cheng, Jianchun

doi:10.1103/physrevapplied.16.044020

Cited by 25 publications

(9 citation statements)

References 50 publications

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“…Fano resonance is implemented in a waveguide for reducing transmission, as shown in Figure 11A and B [61,62]. Interference for Fano resonance occurs between non-resonant background scattering and resonant scattering, as illustrated in 2L with L being the length.…”

Section: Fano Resonance In Weakly Coupling Regimementioning

confidence: 99%

Coupled acoustic resonance for wave control and sensing

Lee

et al. 2022

Front. Phys.

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Coupled resonance enables many intriguing physical phenomena, leading to wave control and sensing. This review discusses fundamental understanding of coupled resonance by providing detailed comparison between lumped parameter-based models including coupled mode theory (CMT) and harmonic oscillator model (HOM). While reviewing recent progress in research concerning coupled resonance, emerging research areas related to coupled resonance are discussed.

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Section: Fano Resonance In Weakly Coupling Regimementioning

confidence: 99%

Coupled acoustic resonance for wave control and sensing

Lee

et al. 2022

Front. Phys.

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“…To overcome it, a variety of ventilation structures with sound insulation and absorption based on different mechanisms have been realized sequentially, [43][44][45][46][47][48] which mainly include sound absorber based on weak coupling of two identical oppositely oriented split tube resonators, [43] open sound silencer based on Fano-like interference, [45][46][47] and ultra-sparse open sound-insulation wall based on artificial Mie resonances. [48] Beyond that, ultrathin metasurfacebased structures [49] have been proposed to design venti-lated sound insulation systems based on phased modulation, such as acoustic metacages, [50] open tunnels, [51] and windows.…”

Section: Introductionmentioning

confidence: 99%

Ultra-broadband acoustic ventilation barrier based on multi-cavity resonators

Xu 许,

Guan 管,

Wu 吴

et al. 2023

Chinese Phys. B

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We report the numerical and experimental realization of an ultra-broadband acoustic ventilation barrier composed of periodic unit cells. Based on multiple mechanisms, including sound absorption by eigenmodes of the unit cell and sound reflection by a plate structure on upper surface of the unit cell, we design a single-layer ventilation barrier with broadband sound reduction, and the working bandwidth can reach about 1560 Hz. The experimental results agree well with the simulation ones. Furthermore, we design and demonstrate two types of three-layer ventilation barriers by using the unit cells with different values of a (the length of the hollow square region) and w (the width of the channel between the adjacent cavities), and the bandwidths of both ventilation barriers can be increased to 3160 and 3230 Hz, respectively. The designed barrier structures have the advantages of ultra-broadband sound reduction and ventilation, which paves a way to design high-performance ventilation barriers for the application in environmental protection and architectural acoustics.

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“…So far, machine learning has been widely used in the fields of fluid mechanics, [7][8][9][10][11][12][13] nonlinear dynamics, [14][15][16][17] and materials science and engineering. [18][19][20][21][22] Sekar et al [10] presented a data driven approach for the prediction of incompressible laminar steady flow field over airfoils based on the combination of deep Convolutional Neural Network and deep Multilayer Perceptron. Pathak et al [15] used a parallel reservoir computing approach to predict large spatiotemporal chaotic systems.…”

Section: Introductionmentioning

confidence: 99%

Fast prediction of aerodynamic noise induced by the flow around a cylinder based on deep neural network

Meng

Yang

et al. 2022

Chinese Phys. B

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Accurate and fast prediction of aerodynamic noise has always been a research hotspot in fluid mechanics and aeroacoustics. The conventional prediction methods based on numerical simulation often demand huge computational resources, which are difficult to balance between accuracy and efficiency. Here, we present a data-driven deep neural network (DNN) method to realize fast aerodynamic noise prediction while maintaining accuracy. The proposed deep learning method can predict the spatial distributions of aerodynamic noise information under different working conditions. Based on the large eddy simulation turbulence model and the Ffowcs Williams-Hawkings acoustic analogy theory, a dataset composed of 1216 samples is established. With reference to the deep learning method, a DNN framework is proposed to map the relationship between spatial coordinates, inlet velocity and overall sound pressure level. The root-mean-square-errors of prediction are below 0.82dB in the test dataset, and the directivity of aerodynamic noise predicted by the DNN framework are basically consistent with the numerical simulation. This work paves a novel way for fast prediction of aerodynamic noise with high accuracy and has application potential in acoustic field prediction.

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Machine-Learning-Assisted Acoustic Consecutive Fano Resonances: Application to a Tunable Broadband Low-Frequency Metasilencer

Cited by 25 publications

References 50 publications

Coupled acoustic resonance for wave control and sensing

Coupled acoustic resonance for wave control and sensing

Ultra-broadband acoustic ventilation barrier based on multi-cavity resonators

Fast prediction of aerodynamic noise induced by the flow around a cylinder based on deep neural network

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