Inverse estimation of viscoelastic material properties for solids immersed in fluids using vibroacoustic techniques

John C. Brigham, Wilkins Aquino, F.G. Mitri, James F. Greenleaf, Mostafa Fatemi

Journal of Applied Physics · 2007 · 57 citations · 30 references

Concepts

TL;DR

Noninvasive characterization of biological materials via medical imaging methods motivates the study. The study aims to inversely determine elastic and viscoelastic material properties of solids immersed in fluids using vibroacoustic steady‑state dynamic response. Acoustic pressure amplitudes measured around a harmonically vibrated solid are combined with finite‑element frequency‑response analysis and an optimization algorithm that blends random search with a surrogate model to estimate material parameters. Numerical and laboratory experiments show that acoustic emissions contain sufficient information to quantify elastic and viscoelastic behaviors, and that the surrogate‑model‑accelerated random‑search algorithm efficiently solves the inverse problem, highlighting its potential for nondestructive evaluation.

Abstract

This work presents an approach to inversely determine material properties for solids immersed in fluids through the use of steady-state dynamic response. The methodology uses measured acoustic pressure amplitudes in the fluid surrounding a structure being vibrated with a harmonic force to determine the parameters for elastic and viscoelastic material models. Steady-state dynamic finite element analysis is used to compute the frequency response function of homogeneous and heterogeneous solids. The frequency response is then used to inversely estimate material parameters. In order to solve the inverse problem, an optimization method is presented which combines the global search capabilities of the random search method with the reduced computational time of a surrogate model approach. Through numerical and laboratory experiments, this work shows that acoustic emissions hold sufficient information for quantifying both elastic and viscoelastic material behaviors. Furthermore, the examples show that the surrogate model accelerated random search algorithm is an efficient and accurate method for solving these types of inverse problems. This research has direct and important implications in nondestructive evaluation of material properties in general. However, the examples shown in this paper are based on applications related to noninvasive characterization of biological materials through methodologies currently applied to medical imaging.

References

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