Academic Journal

Design of ultra-thin broadband sound-absorbing metamaterials based on Helmholtz resonators.

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: Design of ultra-thin broadband sound-absorbing metamaterials based on Helmholtz resonators.
Συγγραφείς: Yang, Xiaotong, Zhao, Caiyou, Liu, Rui, Zhang, Xinhao, Wang, Liuchong, Wang, Ping
Πηγή: Journal of Applied Physics; 7/21/2026, Vol. 140 Issue 3, p1-12, 12p
Θεματικοί όροι: Metamaterials, Helmholtz resonators, Acoustical engineering, Absorption of sound, Numerical analysis
Περίληψη: Achieving efficient low-frequency sound absorption within limited thickness remains a persistent challenge in acoustic engineering. In this work, an ultra-thin broadband absorber is developed by exploiting the weak coupling interaction among Helmholtz resonant units. Instead of relying on bulky cavity enlargement, the proposed design establishes a gradient distribution of resonant frequencies through deliberate modulation of geometric parameters, enabling broadband performance via spatially compact arrangements. Numerical simulations reveal that the two configurations exhibit high absorption efficiency (sound absorption coefficient > 0.8) over 480–1023 and 300–500 Hz, respectively. Despite their compact geometries, the overall thicknesses are restricted to 32 and 42 mm, corresponding to only 1/10.6 and 1/27 of the minimum operating wavelengths. To verify the predictive model, impedance tube measurements were carried out independently for both structures. The experimental results closely follow the simulated trends, confirming the reliability of the analytical and numerical framework. The proposed designs, therefore, demonstrate effective subwavelength absorption capability across targeted mid-low and low-frequency ranges. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Applied Physics is the property of American Institute of Physics and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Βάση Δεδομένων: Complementary Index