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Acoustic liners take an essential role in the noise attenuation in ducts. In this work, the multimodal method based on the finite-difference method is extended to predict the acoustic field in a rectangular duct lined with a finite porous material and validated by the experiment. A modified immersed interface method is developed for the air–porous interface problem in the extended multimodal method without flow. When the flow exists, the treatments of the air–porous interface problem and the continuity of pressure between the wall and liner are also given. Three ways of describing the porous liner using the surface impedance at normal incidence named normal impedance, the surface impedance at grazing incidence named shear impedance and a cavity filled with the equivalent fluid of porous material are introduced. The comparison among the three ways reveals that the extended method of treating liners using a cavity filled with the equivalent fluid is more accurate for the acoustic evaluation of porous liners. The analysis finds that the shear impedance can reasonably present the influence of porous material during the comparisons of transmission loss curves of the liners with different lengths and depths at different Mach numbers. In most cases, the prediction by the shear impedance is closer to that simulated by the cavity filled with porous material than the normal impedance. Normal impedance is hardly utilized to describe the porous material and is only reliable when the cavity is very short and deep. [ABSTRACT FROM AUTHOR]

The study of differential schemes with high accuracy and low dispersion is of great significance for the numerical simulation of aeroacoustics over complex geometries. The midpoint-and-node-to-node explicit finite difference is employed to solve the flux derivatives of the linear Euler equations for improving the robustness. To obtain the numerical flux at the center of the grid element, we adopted a hybrid interpolation method of center and upwind interpolations, combined with a symmetrical conservative metric method, to achieve high-resolution discretization of the acoustic field variables and geometric variables of the structural grid. To suppress spurious oscillations and improve the resolution of discontinuous regions, a family of mapping functions is developed to establish different smoothness indicators and applied to the sixth-order weighted compact nonlinear hybrid scheme (WCNHS), forming a new WCNHS scheme based on piecewise exponential mapping functions (WCNHS-Pe). The approximate dispersion relation shows that the dispersion error and numerical dissipation of WCNHS-Pe are smaller than those of WCNHS with a simple mapping function to the original weights in Jiang and Shu and other mapping function-weighted WCNHS schemes. We have applied various WCNHS schemes to the numerical simulation of the Shu–Osher problem, propagation of Gaussian impulses on two-dimensional wavy grids, sound transmission at discontinuous interfaces, propagation of Gaussian impulses on three-dimensional wavy grids, etc. Numerical results indicate that the WCNHS-Pe scheme has better discontinuity capture capability, higher resolution, and lower dispersion under the same differential stencil, and is suitable for computational aeroacoustics of complex geometries. [ABSTRACT FROM AUTHOR]

The present study numerically investigated a cylinder under oscillating motions at a low Reynolds number. The effects of two oscillation frequencies and amplitudes on the lift drag coefficient, near-field surface pressure fluctuation, and far-field noise were studied. The models were examined at a Mach number of 0.05, corresponding to a Reynolds number of 1.0 × 105. In this paper, the incompressible Navier–Stokes equations (INSE) and linearized perturbed compressible equations (LPCE) were coupled to form a hybrid noise prediction method, which was used to solve the flow field and acoustic radiation field. Based on the simulation results of the acoustic radiation field, the frequency characteristics of the acoustic waves were analyzed by the dynamic modal decomposition (DMD) method. It was observed that when the oscillation amplitude was the same, the variation amplitude and mean value of the lift-drag coefficient increased with the increase in the oscillation frequency. Under the same small oscillation frequency, the oscillation amplitude had little effect on the lift-drag coefficient. However, for the same large oscillation frequency, the variation amplitude of the lift-drag coefficient increased as the oscillation amplitude increased. In addition, both the amplitude and frequency had a significant effect on the directionality of the noise and the intensity of the sound waves. The main energy of the sound field was mainly concentrated on the first and second narrowband frequencies by using the DMD method to analyze the sound pressure level spectrum. [ABSTRACT FROM AUTHOR]

[Display omitted] • A CNN-GA hybrid optimization framework for accelerating the inverse design of lightweight sound-absorbing metaporous. • CNN model with high accuracy and generalization ability is developed and validated. • Customizing various metaporous of 30mm thickness with several simple inclusions on demand. • Desired high sound absorption is achieved in a diffuse sound field. A key challenge in sound-absorbing structure design is the effectively inverse generation of structure capable of achieving high absorption at desired frequencies. As the two of the most commonly used methods in inverse design, trial-and-error and traditional topology optimization suffer extensive computational costs. In this work, we develop a CNN-GA hybrid optimization framework that combines the advantages of the traditional heuristic algorithm with emerging machine learning technology to accelerate the inverse design of a random sound-absorbing structure called metaporous with only 30mm thickness based on the sound absorption in diffuse field. The finite element method (FEM) simulation is applied to calculate the sound absorption coefficients of randomly generated metaporous structures. The numerous data obtained from the FEM simulation are input to the convolutional neural network (CNN) model for training the network. The well-trained CNN model, of which generalization ability is validated by a testing set, is coupled with a genetic algorithm (GA) for accelerating the iterative evaluation of the objective function in inverse design. The proposed inverse design framework leverages the accelerated GA to generate near-optimal metaporous structures of various tailored absorption peaks typically in roughly 5 s to 30 s on average. The numerical results show that the proposed CNN-GA hybrid optimization framework works several orders of magnitude faster than the conventional single algorithms in inverse design of metaporous, on the condition that accuracy is ensured. In addition, an impedance tube measurement is performed to validate the proposed method. This work brings forward a new inverse design method for metaporous materials and brings out the immense potential of combining the meta-heuristic algorithm and machine learning. [ABSTRACT FROM AUTHOR]