On the construction of multiscale surrogates for design optimization of acoustical materials

TL;DR

This paper develops polynomial surrogate models to efficiently predict acoustical material properties, enabling multiscale design optimization with reduced computational effort.

Contribution

It introduces a polynomial metamodel framework for multiscale acoustical material design, demonstrating accurate approximation of sound absorption with lower computational costs.

Findings

Surrogate models accurately predict sound absorption coefficients.

Polynomial series effectively approximate multiscale solution maps.

Framework reduces computational costs for material optimization.

Abstract

This paper is concerned with the use of polynomial metamodels for the design of acoustical materials, considered as equivalent fluids. Polynomial series in microstructural parameters are considered, and allow us to approximate the multiscale solution map in some well-defined sense. The relevance of the framework is illustrated by considering the prediction of the sound absorption coefficient. In accordance with theoretical results provided elsewhere in the literature, it is shown that the surrogate model can accurately approximate the solution map at a reasonable computational cost, depending on the dimension of the input parameter space. Microstructural and process optimization by design are two envisioned applications.