Efficient Computation of Acoustic Scattering Resonances for Sound Hard Obstacles Using a Finite Element DtN Method

Core Concepts

The authors propose an efficient finite element DtN method to compute acoustic scattering resonances for sound hard obstacles, and provide a convergence analysis using the abstract approximation theory for holomorphic Fredholm operator functions.

Abstract

The key highlights and insights of the content are:

Scattering resonances are the poles of the meromorphic continuation of the scattering operator to the lower half-plane, and they have important applications in various fields. However, the numerical computation of scattering resonances faces several challenges.

The authors reformulate the scattering resonances as the eigenvalues of a holomorphic Fredholm operator function, and truncate the unbounded domain using the Dirichlet-to-Neumann (DtN) mapping, which is equivalent to the original problem and does not pollute the spectrum.

The linear Lagrange finite element method is used for discretization, and the convergence of the eigenvalues is proved using the abstract approximation theory for holomorphic Fredholm operator functions.

The resulting nonlinear algebraic eigenvalue problem is solved efficiently using the recently developed parallel spectral indicator method (SIM), which can compute multiple poles simultaneously without requiring initial guesses.

Numerical examples for the unit disk and unit square obstacles are provided, demonstrating the effectiveness and convergence properties of the proposed method.

Stats

The authors provide the following key figures and metrics to support their analysis:

The exact scattering poles for the unit disk can be found analytically, and the computed poles on the finest mesh are consistent with the exact values.
The convergence orders of the computed scattering poles for the unit disk and unit square are approximately 2nd order.
The real parts of the eigenfunctions associated with the computed scattering poles are presented for both examples.

Quotes

None.

Key Insights Distilled From

Analysis of a finite element DtN method for scattering resonances of sound hard obstacles

by Yingxia Xi,B... at arxiv.org 04-16-2024

https://arxiv.org/pdf/2404.09300.pdf

Deeper Inquiries

How can the proposed finite element DtN method be extended to handle more complex geometries or obstacles with non-constant coefficients

The proposed finite element DtN method can be extended to handle more complex geometries or obstacles with non-constant coefficients by incorporating advanced meshing techniques and adaptive refinement strategies. For complex geometries, such as irregular shapes or obstacles with varying material properties, the mesh can be refined in regions of interest to capture the details of the geometry accurately. This adaptive mesh refinement can help in improving the resolution of the solution and capturing the scattering resonances more effectively.
Furthermore, for obstacles with non-constant coefficients, the finite element method can be adapted to handle varying material properties by incorporating them into the governing equations. This involves modifying the discretization process to account for the spatial variations in material properties within the domain. By appropriately defining the coefficients in the governing equations based on the material properties, the finite element DtN method can accurately model the scattering resonances for obstacles with non-constant coefficients.

What are the potential limitations or challenges in applying the parallel SIM to compute scattering resonances in higher dimensions or for different types of scattering problems

The parallel Spectral Indicator Method (SIM) used to compute scattering resonances may face limitations or challenges when applied to higher dimensions or different types of scattering problems. Some potential limitations include:

Computational Complexity: As the dimensionality of the problem increases, the computational complexity of the parallel SIM may also increase significantly. Higher-dimensional problems require more computational resources and may lead to longer computation times.

Mesh Generation: In higher dimensions, generating suitable meshes for complex geometries can be challenging. The accuracy and efficiency of the method can be affected by the quality of the mesh, especially in higher dimensions where mesh generation becomes more complex.

Convergence Issues: The convergence of the method in higher dimensions or for different types of scattering problems may not be as straightforward as in lower dimensions. Ensuring convergence and accuracy in complex scenarios can be a challenging task.

Adaptability: The parallel SIM may need to be adapted or modified to handle specific characteristics of different types of scattering problems, such as those involving anisotropic materials or complex boundary conditions.

Are there any other numerical techniques or approaches that could be combined with the finite element DtN method to further improve the efficiency and accuracy of computing scattering resonances

To further improve the efficiency and accuracy of computing scattering resonances, the finite element DtN method can be combined with other numerical techniques or approaches. Some potential methods that could be integrated include:

Domain Decomposition Methods: By utilizing domain decomposition techniques, the computational domain can be divided into subdomains, allowing for parallel processing and efficient solution of the scattering problem. This can help in reducing the computational burden and improving the scalability of the method.

Model Order Reduction: Techniques like model order reduction can be employed to reduce the computational complexity of the problem while preserving the essential dynamics. This can lead to faster computations and more efficient handling of large-scale scattering resonance problems.

Machine Learning Algorithms: Incorporating machine learning algorithms for data-driven modeling and prediction of scattering resonances can provide insights into the behavior of the system and help in accelerating the computation process. Machine learning can be used to optimize the parameters of the finite element method and improve its performance.

By integrating these additional numerical techniques with the finite element DtN method, the overall efficiency, accuracy, and scalability of computing scattering resonances can be enhanced.