In this paper, we propose a multi-layer micro-perforated panel structure based on a curled space for broadband sound absorption at low frequencies, which increases the number of perforated panel layers in a limited space using a curled space. The absorption coefficients of the structure under plane wave conditions were calculated using the transfer matrix method and the finite element method. It is demonstrated that the multilayer micro-perforated panel structure can ensure high absorption (consistently over 90%) in the frequency range of 400~5000 Hz. The sound absorption mechanism of the multi-layer micro-perforated panel structure is investigated by using the acoustic impedance along with the reflection coefficient of the complex frequency surface. In addition, we also discuss the effects of the micro-perforated panel parameters on the structural sound absorption coefficient. The results show that the proposed multi-layer micro-perforated panel structure provides an excellent solution for sound absorption in a limited space.

There is no doubt that traffic noise has become one of the main sources of urban noise, and the electric bus, as an important means of transport frequently used by people in daily life, has a direct impact on the psychological and auditory health of passengers due to its interior noise characteristics. Consequently, studying electric bus sound quality is an important way to improve vehicle performance and comfort. In this paper, eight electric buses were selected and 64 noise samples were measured. Acoustic comfort was taken as an evaluation index, professionals were organized to complete the subjective evaluation tests for all noise samples based on rank score comparison (RSC). And nine psycho-acoustic objective parameters such as loudness, sharpness and roughness were calculated using ArtemiS software to establish the sound quality database of electric buses. Aiming at the practical application requirements of high-precision modeling of acoustic comfort in vehicles, this paper presented two improved extreme gradient boosting (XGBoost) algorithms based on grid search (GS) method and particle swarm optimization (PSO), respectively, with objective parameters and acoustic comfort as input and output variables, and established three regression models of standard XGBoost, GS-XGBoost and PSO-XGBoost through data training. Finally, the calculation results of three indexes of average relative error, square root error and correlation coefficient indicate that the proposed PSO-XGBoost model is significantly better than GS-XGBoost and standard XGBoost, with its prediction accuracy as high as 97.6 %. This model is determined as the evaluation model of interior acoustic comfort for this case, providing a key technical support for future sound quality optimization of electric buses.