Saed Mousavi

On the use of flexural wave propagation experiments for identification of complex modulus

In this paper, we investigate the nonparametric estimation of the frequency dependent complex modulus of a viscoelastic material. The strains due to flexural wave propagation in a bar specimen are registered at different cross sections. The time domain data is transformed into frequency domain using discrete Fourier transform and a nonlinear least squares algorithm is then employed to estimate the complex modulus at each frequency. Inherent numerical problems due to associated ill-conditioned matrices are treated with special care. An analysis of the quality of the nonlinear least squares estimate is also carried out. The validity of the theoretical results are confirmed by numerical studies and experimental tests.

Optimal sensor locations for nonparametric identification of viscoelastic materials

The problem of optimal sensor locations in nonparametric identification of viscoelastic materials is considered. Different criteria of the covariance matrix, connected to A- and D-optimal experimen ...

Nonparametric identification of complex modulus using a non-equilibrium SHPB procedure

We investigate non-parametric identification of the frequency dependent complex modulus of a viscoelastic material, using a non-equilibrium split Hopkinson pressure bar technique. Three different procedures for estimating the complex modulus from experimental data are considered, and an analysis of the quality of the estimates is carried out. The validity of the theoretical results are confirmed by numerical studies and experimental tests.

Using boundary conditions for estimation of complex modulus from flexural wave experiments

Estimation of the complex Youngs modulus from transverse wave experiment is considered in this brief. The main goal is to modify the usual least squares estimate so that additional information provided by the boundary conditions can be incorporated in the estimation algorithm. As a result, it is required to solve a constrained nonlinear least squares problem instead of an unconstrained one. The analytical results are validated using experimental data, where the modified estimates outperform the conventional nonlinear least squares estimate by a significant margin.