A Truly Exact and Optimal Perfect Absorbing Layer for Time-harmonic Acoustic Wave Scattering Problems

TL;DR

This paper introduces a new perfect absorbing layer (PAL) technique for acoustic wave scattering that is exact, robust, and easy to implement, outperforming traditional PML methods especially at high frequencies.

Contribution

The paper presents a truly exact and optimal PAL based on complex coordinate transformation, free of singularities, and applicable to general star-shaped domains, improving accuracy and robustness.

Findings

PAL is highly accurate and parameter-free

Outperforms classical PML in high wave-number scenarios

Easy to implement with standard numerical methods

Abstract

In this paper, we design a truly exact and optimal perfect absorbing layer (PAL) for domain truncation of the two-dimensional Helmholtz equation in an unbounded domain with bounded scatterers. This technique is based on a complex compression coordinate transformation in polar coordinates, and a judicious substitution of the unknown field in the artificial layer. Compared with the widely-used perfectly matched layer (PML) methods, the distinctive features of PAL lie in that (i) it is truly exact in the sense that the PAL-solution is identical to the original solution in the bounded domain reduced by the truncation layer; (ii) with the substitution, the PAL-equation is free of singular coefficients and the substituted unknown field is essentially non-oscillatory in the layer; and (iii) the construction is valid for general star-shaped domain truncation. By formulating the variational formulation in Cartesian coordinates, the implementation of this technique using standard spectral-element or finite-element methods can be made easy as a usual coding practice. We provide ample numerical examples to demonstrate that this method is highly accurate, parameter-free and robust for very high wave-number and thin layer. It outperforms the classical PML and the recently advocated PML using unbounded absorbing functions. Moreover, it can fix some flaws of the PML approach.