Diffraction of acoustic waves by multiple semi-infinite arrays

TL;DR

This paper extends analytical methods to study acoustic wave diffraction by multiple semi-infinite arrays of scatterers, using a discrete Wiener-Hopf approach to handle complex boundary conditions and large-scale problems.

Contribution

It generalizes the Wiener-Hopf method to multiple arrays with arbitrary orientations, enabling analysis of large-scale diffraction problems with thousands of scatterers.

Findings

Successfully modeled diffraction with thousands of scatterers.

Compared and validated results against other numerical methods.

Demonstrated the effectiveness of the discrete Wiener-Hopf technique.

Abstract

Analytical methods are fundamental in studying acoustics problems. One of the important tools is the Wiener-Hopf method, which can be used to solve many canonical problems with sharp transitions in boundary conditions on a plane/plate. However, there are some strict limitations to its use, usually the boundary conditions need to be imposed on parallel lines (after a suitable mapping). Such mappings exist for wedges with continuous boundaries, but for discrete boundaries, they have not yet been constructed. In our previous article, we have overcome this limitation and studied the diffraction of acoustic waves by a wedge consisting of point scatterers. Here, the problem is generalised to an arbitrary number of periodic semi-infinite arrays with arbitrary orientations. This is done by constructing several coupled systems of equations (one for every semi-infinite array) which are treated independently. The derived systems of equations are solved using the discrete Wiener-Hopf technique and the resulting matrix equation is inverted using elementary matrix arithmetic. Of course, numerically this matrix needs to be truncated, but we are able to do so such that thousands of scatterers on every array are included in the numerical results. Comparisons with other numerical methods are considered, and their strengths/weaknesses are highlighted.