关键词: 折叠超材料, 机器学习, 有限元分析, 阻抗管法曰低频宽带, 飞机发动机降噪

Abstract: To address the low-frequency noise of aircraft engines and the inefficiency of manual parameter tuning in tradional noise-reduction structure optimization, this paper firstly designs a spatially folded metamaterial structure and verifies its superior low -frequency characteristics by finite element simulation and the impedance tube method. Secondly, based on experimental and simulation data, machine leamning algorithms are adopted as the optimization method to optimize and analyze the sound absorption characteristics of the spatially folded metamaterial, Finally, by analyzing the machine leaming results, a composite broadband spatially folded metamaterial model that meets the low-frequency broadband noise reduction requirements of aircraft engines is rapidly constructed by parallel coupling. This confirmns the feasibility and efficiency of the machine learning optimization method. This study can provide new methods and ideas for optimizing the sound absorption characteristics of acoustic metamaterials and for the low-frequency noise reduction design of engines

Key words:  ffolded metamaterial, machine learning, finite element analysis, impedance tube method, low-frequency broad原 band, aircraft engine noise reduction