FEM-BEM analysis of acoustic interaction with submerged thin-shell structures under seabed reflection conditions

Chen, Leilei; Lian, Haojie; Pei, Qingxiang; Meng, Zhuxuan; Jiang, Shujie; Dong, Hao-Wen; Yu, Peng

doi:10.1016/j.oceaneng.2024.118554

Ocean Engineering

2024

DOI: 10.1016/j.oceaneng.2024.118554

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FEM-BEM analysis of acoustic interaction with submerged thin-shell structures under seabed reflection conditions

Leilei Chen,

Haojie Lian,

Qingxiang Pei

et al.

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Uncertainty quantification of 3D acoustic shape sensitivities with generalized nth-order perturbation boundary element methods

Chen,

Huo,

Lian

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Computer Methods in Applied Mechanics and Engineering

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Uncertainty quantification of 3D acoustic shape sensitivities with generalized nth-order perturbation boundary element methods

Chen,

Huo,

Lian

et al. 2025

Computer Methods in Applied Mechanics and Engineering

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Second-order Arnoldi accelerated boundary element method for two-dimensional broadband acoustic shape sensitivity analysis

Li,

Zhong,

et al. 2024

Physics of Fluids

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This paper proposes a novel approach for broadband acoustic shape sensitivity analysis based on the direct differentiation approach. Since the system matrices of the boundary element method (BEM) for the analysis of acoustic state and acoustic sensitivity have frequency dependence, repeated calculations are needed at different frequencies. This is very time-consuming, especially for sensitivity calculations used in shape optimization design. The Taylor series expansion of the Hankel function is carried out to separate the frequency-dependent and frequency-independent terms in the acoustic shape sensitivity boundary integral equation to construct a frequency-independent system matrix. In addition, due to the formation of asymmetric full-coefficient matrices in acoustic shape sensitivity equations based on the BEM, repeatedly solving system equations is also extremely time-consuming at broadband frequencies for large scale issues. The second-order Arnoldi approach was employed to create a reduced-order model that maintains the key features of the initial full-order model. The strong singular and supersingular integrals within the sensitivity equations can be calculated directly utilizing the singularity elimination technique. Finally, several numerical examples confirm the accuracy and efficiency of the proposed algorithm.

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Research on the prediction method of wing structure noise based on the combination of conditional generative adversarial neural network and numerical methods

Jiang,

Liang,

Cheng

et al. 2024

Front. Phys.

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This paper presents a technique for predicting noise generated by airfoil structures that combines deep learning techniques with traditional numerical methods. In traditional numerical methods, accurately predicting the noise of airfoil structures requires significant computational resources, making it challenging to perform low-noise optimization design for these structures. To expedite the prediction process, this study introduces Conditional Generative Adversarial Networks (CGAN). By replacing the generator and discriminator of CGAN with traditional regression neural network models, the suitability of CGAN for regression prediction is ensured. In this study, the data computation was accelerated by expanding the kernel function in the traditional boundary element method using a Taylor series. Based on the resulting data, an alternative predictive model for wing structure noise was developed by integrating Conditional Generative Adversarial Networks (CGAN). Finally, the effectiveness and feasibility of the proposed method are demonstrated through three case studies.

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FEM-BEM analysis of acoustic interaction with submerged thin-shell structures under seabed reflection conditions

Cited by 3 publications

References 50 publications

Uncertainty quantification of 3D acoustic shape sensitivities with generalized nth-order perturbation boundary element methods

Uncertainty quantification of 3D acoustic shape sensitivities with generalized nth-order perturbation boundary element methods

Second-order Arnoldi accelerated boundary element method for two-dimensional broadband acoustic shape sensitivity analysis

Research on the prediction method of wing structure noise based on the combination of conditional generative adversarial neural network and numerical methods

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