Archimedean spiral channel-based acoustic metasurfaces suppressing wide-band low-frequency noise at a deep subwavelength
Materials & Design, ISSN: 0264-1275, Vol: 238, Page: 112703
2024
- 10Citations
- 8Captures
- 1Mentions
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Article Description
Suppression of low-frequency noise suppression is of high engineering importance. However, representative solution micro-perforated panels have limitations, including narrow effective sound absorption bands at low frequencies and excessive thickness. In this study, we design a novel Archimedean spiral channel-based acoustic metasurface (ASCBAM) founded on the theories of the coiling up space and perforated panel with extended tube. An analytical model is developed to formulate the sound absorption coefficient of the proposed ASCBAM. The sound absorption properties of the proposed ASCBAM are investigated by using analytical, computational and experimental methods. The corresponding sound absorption mechanism are revealed. A structural design optimization strategy is subsequently proposed to improve sound absorption performance in wide frequency range. Results suggest that the optimized ASCBAM can effectively achieve the sound absorption in the frequency range of 347–500 Hz at a deep sub-wavelength scale with a thickness-to-wavelength ratio of approximately 1/31.6. We finally apply the concept of the proposed ASCBAM to reduce the noise of actual composite flexible machining equipment, verifying its excellent low-frequency sound absorption capacity and confirming its strong ability to solve real-world engineering problems. The proposed acoustic metasurface offers unprecedented capabilities for low-frequency noise suppression in engineering fields with lightweight requirements.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S0264127524000753; http://dx.doi.org/10.1016/j.matdes.2024.112703; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85185159932&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S0264127524000753; https://dx.doi.org/10.1016/j.matdes.2024.112703
Elsevier BV
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