On-demand prediction of low-frequency average sound absorption coefficient of underwater coating using machine learning
This study proposes an underwater coating with sound absorption ability in the middle-to-low frequency range and establishes an acoustic theoretical model combining the equivalent medium theory and the transfer matrix method. The sound absorption coefficient, surface characteristic impedance, equiva...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025002518 |
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| author | Nansha Gao Mou Wang Xiao Liang Guang Pan |
| author_facet | Nansha Gao Mou Wang Xiao Liang Guang Pan |
| author_sort | Nansha Gao |
| collection | DOAJ |
| description | This study proposes an underwater coating with sound absorption ability in the middle-to-low frequency range and establishes an acoustic theoretical model combining the equivalent medium theory and the transfer matrix method. The sound absorption coefficient, surface characteristic impedance, equivalent volume longitudinal wave modulus, and equivalent sound velocity are calculated and solved. Using the preset 20 sensitive parameters and the hypercube sampling method, this study establishes 100,000 random sound absorption coefficient curves in the frequency range of 1 Hz–1,000 Hz. Further, deep neural networks are employed to predict the average value of the sound absorption coefficient curve. The overall loss function is derived by combining the mean square error between the expected average sound absorption coefficient and its predicted value and the network-optimized loss function to ensure that the 20 sensitive parameters that meet the acoustic performance can be predicted. Finally, two randomly selected sound absorption curves are used for prediction tests. The verification results indicate that the error between the expected average absorption coefficient and the predicted average absorption coefficient corresponding to the 20 sensitive parameters is only 0.026 % and 0.33 %. The proposed method can be extended to predict the average absorption coefficient value for any acoustic structure, which could be beneficial for the performance development of acoustic functional devices. |
| format | Article |
| id | doaj-art-508d735121774c028bebd52f8a129f7e |
| institution | Kabale University |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-508d735121774c028bebd52f8a129f7e2025-01-28T04:14:51ZengElsevierResults in Engineering2590-12302025-03-0125104163On-demand prediction of low-frequency average sound absorption coefficient of underwater coating using machine learningNansha Gao0Mou Wang1Xiao Liang2Guang Pan3Key Laboratory of Unmanned Underwater Vehicle, School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an 710072, China; Corresponding author.Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, ChinaXiangtan University, School of Mechanical Engineering and Mechanics, Xiangtan 411105, ChinaKey Laboratory of Unmanned Underwater Vehicle, School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an 710072, ChinaThis study proposes an underwater coating with sound absorption ability in the middle-to-low frequency range and establishes an acoustic theoretical model combining the equivalent medium theory and the transfer matrix method. The sound absorption coefficient, surface characteristic impedance, equivalent volume longitudinal wave modulus, and equivalent sound velocity are calculated and solved. Using the preset 20 sensitive parameters and the hypercube sampling method, this study establishes 100,000 random sound absorption coefficient curves in the frequency range of 1 Hz–1,000 Hz. Further, deep neural networks are employed to predict the average value of the sound absorption coefficient curve. The overall loss function is derived by combining the mean square error between the expected average sound absorption coefficient and its predicted value and the network-optimized loss function to ensure that the 20 sensitive parameters that meet the acoustic performance can be predicted. Finally, two randomly selected sound absorption curves are used for prediction tests. The verification results indicate that the error between the expected average absorption coefficient and the predicted average absorption coefficient corresponding to the 20 sensitive parameters is only 0.026 % and 0.33 %. The proposed method can be extended to predict the average absorption coefficient value for any acoustic structure, which could be beneficial for the performance development of acoustic functional devices.http://www.sciencedirect.com/science/article/pii/S2590123025002518Underwater sound absorptionTransfer-matrix methodMachine learningDeep neural networkOn-demand prediction |
| spellingShingle | Nansha Gao Mou Wang Xiao Liang Guang Pan On-demand prediction of low-frequency average sound absorption coefficient of underwater coating using machine learning Results in Engineering Underwater sound absorption Transfer-matrix method Machine learning Deep neural network On-demand prediction |
| title | On-demand prediction of low-frequency average sound absorption coefficient of underwater coating using machine learning |
| title_full | On-demand prediction of low-frequency average sound absorption coefficient of underwater coating using machine learning |
| title_fullStr | On-demand prediction of low-frequency average sound absorption coefficient of underwater coating using machine learning |
| title_full_unstemmed | On-demand prediction of low-frequency average sound absorption coefficient of underwater coating using machine learning |
| title_short | On-demand prediction of low-frequency average sound absorption coefficient of underwater coating using machine learning |
| title_sort | on demand prediction of low frequency average sound absorption coefficient of underwater coating using machine learning |
| topic | Underwater sound absorption Transfer-matrix method Machine learning Deep neural network On-demand prediction |
| url | http://www.sciencedirect.com/science/article/pii/S2590123025002518 |
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