Modelling the acoustic behaviour of tricot fabrics using metaheuristic optimization algorithms

Tricot fabrics, though widely used in household and automotive sectors, have not been investigated for their acoustic behaviour despite structural advantages. This study examines their sound absorption performance and models it using metaheuristic optimisation algorithms. Experimental results showed underlap pitch as the most influential structural variable, linked to reductions in straight pore fraction and increases in areal density. Acoustic modelling was performed with Maa’s micro-perforated panel, Garai-Pompoli’s equivalent fluid, and Johnson-Champoux-Allard (JCA) microstructural models. Microstructural parameters (tortuosity, shape factor, scale factor, and porosity correction factor) were estimated using particle swarm optimisation (PSO), dynamic multi-swarm PSO (MPSO), artificial bee colony optimisation (ABCO), and fish school search optimisation (FSSO). Among these, JCA-PSO achieved the best agreement with experimental sound absorption coefficient (SAC) data, reducing the root mean square error (RMSE) from 0.053 to 0.045 when effective porosity was introduced. While all algorithms gave comparable SAC predictions, MPSO proved the fastest and most stable, and FSSO the least efficient. Overall, the integration of metaheuristic algorithms with microstructural modelling offers a robust and computationally efficient approach for parameter estimation in fibrous media. The findings highlight tricot fabrics as lightweight, high-performance acoustic materials with potential for automotive and architectural noise-control applications.