Semi-active control of smart porous structure for sound absorption enhancement
- Authors
- Kim, Bo-Seung; Kwon, Semin; Jeong, Seongyoung; Park, Junhong
- Issue Date
- Oct-2019
- Publisher
- SAGE PUBLICATIONS LTD
- Keywords
- porous absorber; helical shape; magnetic material; semi-active noise control
- Citation
- JOURNAL OF INTELLIGENT MATERIAL SYSTEMS AND STRUCTURES, v.30, no.17, pp.2575 - 2580
- Indexed
- SCIE
SCOPUS
- Journal Title
- JOURNAL OF INTELLIGENT MATERIAL SYSTEMS AND STRUCTURES
- Volume
- 30
- Number
- 17
- Start Page
- 2575
- End Page
- 2580
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/12430
- DOI
- 10.1177/1045389X19862371
- ISSN
- 1045-389X
- Abstract
- For sound absorption of conventional porous materials, its noise-reducing ability in low frequencies is less than that in high frequency bands. A porous layer offers effective sound absorption when the thickness of layer is a quarter wavelength of the sound wave. Increasing the layer thickness is not an effective solution. Adjustment of rear air cavity volume for the porous medium has been used in practical applications to increase low frequency sound absorption. Noise reduction using only passive sound absorption characteristics is a challenge, especially when the operation conditions of noise source changes. In this study, we propose a smart porous material having noise reduction ability in the broad frequency band. We implement semi-active control by applying a magnetic reactive material to a porous medium. The smart foam proposed in this study reduced noise with adjustment of the magnitude and polarity of the magnetic field. Evaluation of its noise reduction performance is performed with the impedance tube method. Experiments are conducted for semi-active noise control of the proposed smart foam. The sound absorption characteristics are controlled efficiently with relatively low electric power consumption. A theoretical model to predict absorption characteristics is proposed using the transfer-matrix method, and its results show good agreement with measured behaviors.
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