Computing leaky waves in semi-analytical waveguide models by exponential residual relaxation

TL;DR

This paper introduces an efficient method using exponential residual relaxation, based on Zhang Neural Networks, to compute leaky wave dispersion curves in waveguide models, especially when nonlinear boundary conditions complicate traditional approaches.

Contribution

It applies the ZNN approach to solve frequency-dependent eigenvalue problems for guided waves, enabling better handling of nonlinear boundary conditions in waveguide models.

Findings

Method efficiently computes dispersion curves for complex waveguide structures.

Approach handles nonlinear boundary conditions that hinder traditional solvers.

Demonstrates improved computational performance in ultrasonic measurement applications.

Abstract

Semi-analytical methods for the modeling of guided waves in structures of constant cross-section lead to frequency-dependent polynomial eigenvalue problems for the wavenumbers and mode shapes. Solving these eigenvalue problems for a range of frequencies results in continuous eigencurves that are of relevance in practical applications of ultrasonic measurement systems. Recent research has shown that eigencurves of parameter-dependent eigenvalue problems can alternatively be computed as solutions of a system of ordinary differential equations, which are obtained by postulating an exponentially decaying residual of a modal solution. This general concept for solving parameter-dependent matrix equations is, in this context, known as Zeroing Neural Networks or Zhang Neural Networks (ZNN). We exploit this idea to develop an efficient method for computing the dispersion curves of plate structures coupled to unbounded solid or fluid media. In these scenarios, the alternative formulation is particularly useful since the boundary conditions give rise to nonlinear terms that severely hinder the application of traditional solvers.