Acoustic metamaterials (AMs) composed of periodic artificial structures have extraordinary sound wave manipulation capabilities compared with traditional acoustic materials, and they have attracted widespread research attention. The sound insulation performance of thin-walled structures commonly used in engineering applications with restricted space, for example, vehicles’ body structures, and the latest studies on the sound insulation of thin-walled metamaterial structures, are comprehensively discussed in this paper. First, the definition and math law of sound insulation are introduced, alongside the primary methods of sound insulation testing of specimens. Secondly, the main sound insulation acoustic metamaterial structures are summarized and classified, including membrane-type, plate-type, and smart-material-type sound insulation metamaterials, boundaries, and temperature effects, as well as the sound insulation research on composite structures combined with metamaterial structures. Finally, the research status, challenges, and trends of sound insulation metamaterial structures are summarized. It was found that combining the advantages of metamaterial and various composite panel structures with optimization methods considering lightweight and proper wide frequency band single evaluator has the potential to improve the sound insulation performance of composite metamaterials in the full frequency range. Relative review results provide a comprehensive reference for the sound insulation metamaterial design and application.

In this study, porous sound-absorbing materials used as a lining in double-panel structure applications (such as high-speed train body structures) to limit flow-induced vibration interior noise were studied, and acoustic optimization design was performed. First, in the wavenumber domain, the cross-spectrum Corcos model was used to characterize the dynamic hydrodynamic pressure of turbulence. Biot’s theory is used to model the porous materials. The transmission loss (TL) of the sandwich panel were also determined based on the model superposition method. Three types of sandwich panel structures were considered: air–air (A–A), bonded-bonded (B–B), and bonded-air (B–A). The TL of the three structure types under hydrodynamic pressure was used to evaluate the suppression of flow-induced vibration interior noise in porous materials. The effects of flow velocity, thickness and density of the porous material, and three types of polyimide foam on the TL characteristics of the sandwich panel were investigated. The results show that the flow velocity has a significant influence on the TL of the sandwich panel. The TL of the sandwich panel decreases by 3–4 dB when the flow velocity increases by 100 km/h The B–A configuration has satisfactory sound insulation performance at most frequencies. With an increase in material thickness, the TL of the sandwich panel structure first increases and then decreases, and the material density mainly affects the TL of the structure at intermediate and high frequencies. Based on the objectives of maximizing the average transmission loss (TL_avg) and minimizing the structural weight, the acoustic optimization design of the B–A structure was performed, and the balance between the two objective functions was achieved by a nondominated sorting genetic algorithm (NSGA-Ⅱ). The TL_avg s of the sandwich panel structure increased by 5.2 dB when the total mass of the structure was decreased by 0.2 kg.

Increasing the speed of high-speed trains requires the lightweight design of vehicles to meet the economic and ecological efficiency requirements of such trains. However, these objectives conflict with the interior noise control in high-speed trains because the sound insulation of panel structures follows the mass law principle. The train floor, the main train body structure of the high-speed train, is vital for interior noise control because its sound insulation performance directly affects the interior noise levels. Owing to the complexity of the composite floor system, reliable measurement and accurate estimation of its sound insulation performance are often time-consuming and laborious. To address this situation, this study proposes an artificial neural network (ANN)-based model to predict the sound insulation characteristics of a composite floor. First, a sound insulation model of the composite floor is built based on statistical energy analysis (SEA). The sound insulation performance of 200 cases of composite floors is calculated by varying the dimensions of the extruded floor, thickness of the webs, sound-absorbing material, and wooden floor to formulate a sound insulation database of composite floors. Next, an ANN model is introduced and trained on the sound insulation database. The sound insulation prediction results obtained using the ANN model are compared to the prediction results obtained using the experiment to validate its effectiveness. Subsequently, the NSGA-II optimization method is used to optimize the sound insulation structure of the composite floor. Compared with the regular composite floor structure, the optimized structure reduced the mass of the composite floor by 10.93 kg and increased the weight of the sound insulation ( R_w) by 6.3 dB. The proposed method can be an effective, economical, and efficient tool for vehicle designers and can help promote the sound insulation optimization design of high-speed train composite floors.

A theoretical model of the normal sound insulation of a rectangular locally resonant (LR) plate with simply supported boundary conditions is established. The accuracy of the model is verified through comparison with finite element simulation results. Considering a 1-m² aluminum plate as the research object, a non-dominated genetic algorithm is introduced. The algorithm optimizes the lightweight sound insulation of an LR finite plate with a center frequency band of 20–800 Hz as the optimization objective of maximum average sound insulation and minimum mass. The optimization parameters are substrate thickness and local resonance parameters (including resonator target frequency ( f_obj), number of resonators, additional mass ratio, and resonator damping). Optimization results indicate that the average sound insulation fluctuates at approximately 2 dB due to changes in f_obj. The results also provide an optimal solution to the algorithm. The optimal sound insulation value curve of LR plates corresponding to different surface densities of intermediate improved cases is fitted and compared with the sound insulation value of an equal-mass bare plate. Under lightweight optimization, the resonators are deemed capable of improving the local frequency band sound insulation. In contrast, the wideband average sound insulation can approximate but not surpass the insulation provided by the equal-mass bare plate. In practical applications, an LR plate that can improve the local frequency band sound insulation and ensure the overall lightweight sound insulation performance of the wide frequency band can be obtained. This can be achieved by analyzing the frequency spectrum of optimal points in the lightweight sound insulation optimization process. Relevant research can provide a basis for the evaluation and design of the low-frequency sound insulation of plates for transportation equipment, such as airplanes, high-speed trains, and cars.

The wall pressure fluctuation caused by a turbulent boundary layer is an important source of noise and vibration in high-speed trains. Considering that railway vehicles are usually made of extruded profiles, a simple and reliable model is needed to predict the vibroacoustic characteristics of extruded profiles excited by a TBL in the early stages of design. A hybrid finite element and statistical energy analysis model is established for computing the vibroacoustic response of finite-sized panels excited by a TBL. In the hybrid model, the wall pressure fluctuation of the TBL is modelled in the wavenumber domain. The panels are modelled using FEs, and acoustic waves in the fluid space below the panels are modelled using SEA. The TBL wall pressure acting on the top surface of the panels is resolved into equivalent nodal forces with discretization of the structure using FEs, achieved by calculating the cross-spectrum matrix of the wall pressure in the spectral domain and using jinc functions with smooth boundary and symmetric properties as shape functions. The reciprocity between direct and reverberant fields couples the acoustic fields to the structure. Once the response of the hybrid model is determined, sound transmission through the panels can be calculated. By following the procedures outlined in this paper, the range of wall pressure model selection for TBL has extended and reduced computational time and provides a basis for the acoustic optimization design of extruded profiles.