Design and analysis of Conceptual acoustic metamaterials applied to mitigating cooling fan noise for heavy-duty vehicles

The noise generated from cooling fans in heavy-duty vehicles like trucks poses challenges regarding environmental pollution and overall user comfort. To solve this problem, this study develops an acoustic metamaterial (AMM) that is constructed with small Helmholtz resonators in two layers. This concept can attenuate the noise in a broad frequency range. Furthermore, the particle swarm optimization (PSO) algorithm based on artificial intelligence (AI) is used to optimize the AMM structure. The key design parameters are discussed and analyzed. The installation strategies are put forth to examine the performance of the AMM. The proposed metamaterials, combined with the installation strategies, are numerically simulated using the finite element method. The results show that the current AMM is effective in reducing both tonal and broadband noise of interest. The findings shed light on the development of acoustic metamaterials for cooling-fan noise reduction in heavy-duty vehicles.