The underwater anechoic coating technology,which considers pressure resistance and low-frequency broadband sound absorption,has become a research hotspot in underwater acoustics and has received wide attention to addr...The underwater anechoic coating technology,which considers pressure resistance and low-frequency broadband sound absorption,has become a research hotspot in underwater acoustics and has received wide attention to address the increasingly advanced low-frequency sonar detection technology and adapt to the working environment of underwater vehicles in deep submergence.One the one hand,controlling low-frequency sound waves in water is more challenging than in air.On the other hand,in addition to initiating structural deformation,hydrostatic pressure also changes material parameters,both of which have a major effect on the sound absorption performance of the anechoic coating.Therefore,resolving the pressure resistance and acoustic performance of underwater acoustic coatings is difficult.Particularly,a bottleneck problem that must be addressed in this field is the acoustic structure design with low-frequency broadband sound absorption under high hydrostatic pressure.Based on the influence of hydrostatic pressure on underwater anechoic coatings,the research status of underwater acoustic structures under hydrostatic pressure from the aspects of sound absorption mechanisms,analysis methods,and structural designs is reviewed in this paper.Finally,the challenges and research trends encountered by underwater anechoic coating technology under hydrostatic pressure are summarized,providing a reference for the design and research of low-frequency broadband anechoic coating.展开更多
Research on the acoustic performance of an anechoic coating composed of cavities in a viscoelastic material has recently become an area of great interest.Traditional forward research methods are unable to manipulate s...Research on the acoustic performance of an anechoic coating composed of cavities in a viscoelastic material has recently become an area of great interest.Traditional forward research methods are unable to manipulate sound waves accurately and effectively,are difficult to analyse,have time-consuming solution processes,and have large optimization search spaces.To address these issues,this paper proposes a deep learning-based inverse research method to efficiently invert the material parameters of Alberich-type sound absorption coatings and rapidly predict their acoustic performance.First,an autoencoder(AE)model is pretrained to reconstruct the viscoelastic material parameters of an Alberich-type sound absorption coating,the material parameters are extracted into the latent feature space by the encoder,and the decoder model is saved.The internal relationship between the reflection coefficient and latent feature space is trained to establish a multilayer perceptron(MLP).Then,the reflection coefficients in the test set are input to the trained MLP and decoder models to automatically invert the material parameters.The accuracy of the inversion result is 95.08%.Finally,a predictive model is trained to rapidly predict the acoustic performance of the inverted material parameters.The speed of a single test target is 80 times faster than that of the finite element method(FEM).Furthermore,sound absorber material parameters with the best sound absorption performance and a three-band sound absorber are inverted,and their actual sound absorption performance is predicted by the proposed method.The proposed deep learning-based inversion research method provides a solution for low-frequency,wide-band,strong attenuation,and precisely controlled sound waves.It achieves an efficient inversion of material parameters and the rapid forecasting of acoustic performance.The training model can be used for a sound absorbing coating composed of irregular cavities in a viscoelastic material and predict its acoustic performance by only modifying the dataset.展开更多
基金Supported by the National Natural Science Foundation of China(Grant No.52271309)Natural Science Foundation of Heilongjiang Province of China(Grant No.YQ2022E104)Doctoral Science and Technology Innovation Fund of Harbin Engineering University(Grant No.3072023GIP0302).
文摘The underwater anechoic coating technology,which considers pressure resistance and low-frequency broadband sound absorption,has become a research hotspot in underwater acoustics and has received wide attention to address the increasingly advanced low-frequency sonar detection technology and adapt to the working environment of underwater vehicles in deep submergence.One the one hand,controlling low-frequency sound waves in water is more challenging than in air.On the other hand,in addition to initiating structural deformation,hydrostatic pressure also changes material parameters,both of which have a major effect on the sound absorption performance of the anechoic coating.Therefore,resolving the pressure resistance and acoustic performance of underwater acoustic coatings is difficult.Particularly,a bottleneck problem that must be addressed in this field is the acoustic structure design with low-frequency broadband sound absorption under high hydrostatic pressure.Based on the influence of hydrostatic pressure on underwater anechoic coatings,the research status of underwater acoustic structures under hydrostatic pressure from the aspects of sound absorption mechanisms,analysis methods,and structural designs is reviewed in this paper.Finally,the challenges and research trends encountered by underwater anechoic coating technology under hydrostatic pressure are summarized,providing a reference for the design and research of low-frequency broadband anechoic coating.
基金supported by the National Natural Science Foundation of China(Nos.51765008,11304050)the High-Level Innovative Talents Project of Guizhou Province(No.20164033)+1 种基金the Science and Technology Project of Guizhou Province(No.2020–1Z048)the Open Project of the Key Laboratory of Modern Manufacturing Technology of the Ministry of Education(No.XDKFJJ[2016]10).
文摘Research on the acoustic performance of an anechoic coating composed of cavities in a viscoelastic material has recently become an area of great interest.Traditional forward research methods are unable to manipulate sound waves accurately and effectively,are difficult to analyse,have time-consuming solution processes,and have large optimization search spaces.To address these issues,this paper proposes a deep learning-based inverse research method to efficiently invert the material parameters of Alberich-type sound absorption coatings and rapidly predict their acoustic performance.First,an autoencoder(AE)model is pretrained to reconstruct the viscoelastic material parameters of an Alberich-type sound absorption coating,the material parameters are extracted into the latent feature space by the encoder,and the decoder model is saved.The internal relationship between the reflection coefficient and latent feature space is trained to establish a multilayer perceptron(MLP).Then,the reflection coefficients in the test set are input to the trained MLP and decoder models to automatically invert the material parameters.The accuracy of the inversion result is 95.08%.Finally,a predictive model is trained to rapidly predict the acoustic performance of the inverted material parameters.The speed of a single test target is 80 times faster than that of the finite element method(FEM).Furthermore,sound absorber material parameters with the best sound absorption performance and a three-band sound absorber are inverted,and their actual sound absorption performance is predicted by the proposed method.The proposed deep learning-based inversion research method provides a solution for low-frequency,wide-band,strong attenuation,and precisely controlled sound waves.It achieves an efficient inversion of material parameters and the rapid forecasting of acoustic performance.The training model can be used for a sound absorbing coating composed of irregular cavities in a viscoelastic material and predict its acoustic performance by only modifying the dataset.
