Nonlinear Structural Identification Using a Multi-Harmonic Frequency Response Functions
In industrial structural dynamics problems, linear FE-models commonly serve to represent the global structural behavior. However, when test data are available they often show evidence of nonlinear dynamic characteristics. In that case, an initial linear model may be judged insufficient in representing the dynamics of the structure. The causes of the non-linear characteristics may be local in nature whereas the major part of the structure is satisfactorily represented by linear descriptions. Although the initial model can serve as a good foundation, all physical properties required for representing the real structure with high fidelity are most likely not included in the initial model. Therefore, a set of parameterized candidate properties controlling the nonlinear effects have to be added. The selection of the candidates is a delicate task which must be based on insight into the physical processes that control the structure at hand.
The focus of this thesis is on the selection of uncertain model parameters together with the forming of the objective function to be used for calibration. To give precise estimation of parameters in the presence of measurement noise, the objective function data have to be informative with respect to the selected parameters. Also, to get useful test data for calibration, the system stimuli need to be properly designed. A multi-harmonic stationary sinusoidal excitation is here considered since the corresponding steady-state responses at the different harmonic orders are shown to contain valuable information for the calibration process.
multi-harmonic frequency response function
finite element model updating
Fisher information matrix
grey box system identification