Material Identification On Thin Shells Using the Virtual Fields Method, Demonstrated On the Human Eardrum

Characterization of material parameters from experimental data remains challenging, especially on biological structures. One of such techniques allowing for the inverse determination of material parameters from measurement data is the Virtual Fields Method (VFM). However, application of the VFM on general structures of complicated shape has not yet been extensively investigated. In this paper, we extend the framework of the VFM method to thin curved solids in 3D, commonly denoted shells. Our method is then used to estimate theYoung's modulus and hysteretic damping of the human eardrum. By utilizing Kirchhoff plate theory, we assume that the behavior of the shell varies linearly through the thickness. The total strain of the shell can then be separated in a bending and membrane strain. This in turn allowed for an application of the VFM based only on data of the outer surface of the shell. We validated our method on simulated and experimental data of a human eardrum made to vibrate at certain frequencies. It was shown that the identified material properties were accurately determined based only on data from the outer surface and are in agreement with literature. Additionally, we observed that neither the bending nor the membrane strain in an human eardrum can be neglected and both contribute significantly to the total strain found experimentally.

General Finite-Element Framework of the Virtual Fields Method in Nonlinear Elasticity

This paper presents a method to derive the virtual fields for identifying constitutive model parameters using the Virtual Fields Method (VFM). The VFM is an approach to identify unknown constitutive parameters using deformation fields measured across a given volume of interest. The general principle for solving identification problems with the VFM is first to derive parametric stress field, where the stress components at any point depend on the unknown constitutive parameters, across the volume of interest from the measured deformation fields. Applying the principle of virtual work to the parametric stress fields, one can write scalar equations of the unknown parameters and solve the obtained system of equations to deduce the values of unknown parameters. However, no rules have been proposed to select the virtual fields in identification problems related to nonlinear elasticity and there are multiple strategies possible that can yield different results. In this work, we propose a systematic, robust and automatic approach to reconstruct the systems of scalar equations with the VFM. This approach is well suited to finite-element implementation and can be applied to any problem provided that full-field deformation data are available across a volume of interest. We also successfully demonstrate the feasibility of the novel approach by multiple numerical examples. Potential applications of the proposed approach are numerous in biomedical engineering where imaging techniques are commonly used to observe soft tissues and where alterations of material properties are markers of diseased states.

General Finite-Element Framework of the Virtual Fields Method in Nonlinear Elasticity

This paper presents a method to derive the virtual fields for identifying constitutive model parameters using the Virtual Fields Method (VFM). The VFM is an approach to identify unknown constitutive parameters using deformation fields measured across a given volume of interest. The general principle for solving identification problems with the VFM is first to derive parametric stress field, where the stress components at any point depend on the unknown constitutive parameters, across the volume of interest from the measured deformation fields. Applying the principle of virtual work to the parametric stress fields, one can write scalar equations of the unknown parameters and solve the obtained system of equations to deduce the values of unknown parameters. However, no rules have been proposed to select the virtual fields in identification problems related to nonlinear elasticity and there are multiple strategies possible that can yield different results. In this work, we propose a systematic, robust and automatic approach to reconstruct the systems of scalar equations with the VFM. This approach is well suited to finite-element implementation and can be applied to any problem provided that full-field deformation data are available across a volume of interest. We also successfully demonstrate the feasibility of the novel approach by multiple numerical examples. Potential applications of the proposed approach are numerous in biomedical engineering where imaging techniques are commonly used to observe soft tissues and where alterations of material properties are markers of diseased states.