叶轮
降噪
声学
还原(数学)
噪音(视频)
扇入
机械风扇
工程类
计算机科学
机械工程
物理
数学
人工智能
几何学
图像(数学)
作者
Yulong Sun,Rui Li,Linbo Wang,Chongrui Liu,Zhibo Yang,Fuyin Ma
标识
DOI:10.1088/1361-6463/ad5024
摘要
Abstract Fans are integral equipment widely employed in both industrial settings and daily life. However, a persistent challenge in fans design lies in the inherent conflict between aerodynamic performance and noise levels. Improving aerodynamic efficiency often results in a compromise of acoustic performance. To tackle this issue, we employed the bionic design method to craft a novel axial fan impeller featuring a bionic curved hub and bionic serrated leading edges. The impact of structural optimization on the aerodynamic and acoustic properties of the impeller, as well as the influence of optimization parameters on these properties, were systematically investigated through numerical simulations. The bionic impeller was then fabricated using 3D printing, and the aerodynamic and noise performance of the impeller were experimentally evaluated by integrating it into an external air conditioner. Comparison of the flow field and sound field data between the optimized and prototype impellers revealed noteworthy outcomes. The curved wall at the bionic hub’s tail effectively diminished the pressure gradient on the hub surface, directing the airflow toward the rear end of the hub. This design enhancement significantly reduced the turbulent area behind the prototype impeller’s hub. Additionally, under the appropriately designed, the bionic serrated structure could effectively reduce the contact area between the blade’s leading edge and incoming flow. This led to the dispersion of stress concentrations and the inhibition of strong turbulence generation. Notably, the experimental results indicated a 3.7% increase in air volume flow rate and a 2.3 dB reduction in noise for the optimized impeller compared to the prototype. This successful mitigation of the trade-off between aerodynamic performance and noise level underscores the effectiveness of our bionic design approach.
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