Uncertainty quantification of 3D acoustic shape sensitivities with generalized <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si5.svg" display="inline" id="d1e1526"><mml:mrow><mml:mi>n</mml:mi><mml:mi mathvariant="normal">th</mml:mi></mml:mrow></mml:math>-order perturbation boundary element methods

Chen, Leilei; Huo, Ruijin; Lian, Haojie; Yu, Bo; Zhang, Mengxi; Natarajan, Sundararajan; Bordas, Stéphane P.A.

doi:10.1016/j.cma.2024.117464

Computer Methods in Applied Mechanics and Engineering

2025

DOI: 10.1016/j.cma.2024.117464

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

Leilei Chen,

Ruijin Huo,

Haojie Lian

et al.

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Cited by 2 publications

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Bayesian uncertainty analysis for underwater 3D reconstruction with neural radiance fields

Lian,

Li,

et al. 2025

Applied Mathematical Modelling

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Bayesian uncertainty analysis for underwater 3D reconstruction with neural radiance fields

Lian,

Li,

et al. 2025

Applied Mathematical Modelling

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A reduced-order boundary element method for two-dimensional acoustic scattering

Zhong,

Jiang,

et al. 2024

Front. Phys.

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This study presents a novel method for wideband acoustic analysis using the Boundary Element Method (BEM), addressing significant computational challenges. Traditional BEM requires repetitive computations across different frequencies due to the frequency-dependent system matrix, resulting in high computational costs. To overcome this, the Hankel function is expanded into a Taylor series, enabling the separation of frequency-dependent and frequency-independent components in the boundary integral equations. This results in a frequency-independent system matrix, improving computational efficiency. Additionally, the method addresses the issue of full-rank, asymmetric coefficient matrices in BEM, which complicate the solution of system equations over wide frequency ranges, particularly for large-scale problems. A Reduced-Order Model (ROM) is developed using the Second-Order Arnoldi (SOAR) method, which retains the key characteristics of the original Full-Order Model (FOM). The singularity elimination technique is employed to directly compute the strong singular and super-singular integrals in the acoustic equations. Numerical examples demonstrate the accuracy and efficiency of the proposed approach, showing its potential for large-scale applications in noise control and acoustic design, where fast and precise analysis is crucial.

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

Cited by 2 publications

References 52 publications

Bayesian uncertainty analysis for underwater 3D reconstruction with neural radiance fields

Bayesian uncertainty analysis for underwater 3D reconstruction with neural radiance fields

A reduced-order boundary element method for two-dimensional acoustic scattering

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