Abstract
Porous materials have been widely used in the field of noise control. The non-acoustical parameters involved in the sound absorption model have an important effect on the sound absorption performance of porous materials. How to identify these non-acoustical parameters efficiently and accurately is an active research area and many researchers have devoted contributions on it. In this study, a modified particle swarm optimization algorithm is adopted to identify the non-acoustical parameters of the jute fiber felt. Firstly, the sound absorption model used to predict the sound absorption coefficient of the porous materials is introduced. Secondly, the model of non-acoustical parameter identification of porous materials is established. Then the modified particle swarm optimization algorithm is introduced and the feasibility of the algorithm applied to the parameter identification of porous materials is investigated. Finally, based on the sound absorption coefficient measured by the impedance tube the modified particle swarm optimization algorithm is adopted to identify the non-acoustical parameters involved in the sound absorption model of the jute fiber felt, and the identification performance and the computational performance of the algorithm are discussed. Research results show that compared with other identification methods the modified particle swarm optimization algorithm has higher identification accuracy and is more suitable for the identification of non-acoustical parameters of the porous materials. The sound absorption coefficient curve predicted by the modified particle swarm optimization algorithm has good consistency with the experimental curve. In the aspect of computer running time, compared with the standard particle swarm optimization algorithm, the modified particle swarm optimization algorithm takes shorter running time. When the population size is larger, modified particle swarm optimization algorithm has more advantages in the running speed. In addition, this study demonstrates that the jute fiber felt is a good acoustical green fibrous material which has excellent sound absorbing performance in a wide frequency range and the peak value of its sound absorption coefficient can reach 0.8.
Highlights
Nowadays porous materials have been widely used in the field of noise control
The modified particle swarm optimization (MPSO) algorithm is adopted to identify the non-acoustical parameters involved in the JCA model of the jute fiber felt based on the sound absorption coefficient (SAC) measured by the impedance tube
Based on the experimental SAC and the porosity of the jute fiber felt, the standard particle swarm optimization (PSO) algorithm and the MPSO algorithm are adopted to identify the non-acoustical parameters involved in the JCA model of the jute fiber felt
Summary
Nowadays porous materials have been widely used in the field of noise control. Many researchers have devoted contributions on the sound absorption performance of these materials. The SAC of materials can be experimentally evaluated using the impedance tube [1] or be predicted using acoustic transfer analysis method along with experimental measurements [2]. The phenomenological model takes the influence of micro-factors on the acoustical properties of the materials into account. They consider the frame of a porous material as rigid and involve five non-acoustical parameters for the surface impedance calculation, namely porosity, tortuosity, air flow resistivity, viscous and thermal characteristic lengths [7]. The phenomenological model establishes a relationship between the microstructure and the acoustic performance through characterizing porous materials with equivalent fluid, which makes them have higher prediction accuracy
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