Identification of a Global Model Describing the Tempera- ture Effects on the Dynamics of a Smart Composite Beam

The identification of a global model describing the dynamics of a smart composite beam under various temperatures is addressed. The problem is treated within a novel Statistical Functional Pooling Framework featuring global, stochastic Functionally Pooled (FP) models with explicit functional dependence on temperature. This framework circumvents the disadvantages associated with conventional multi-model approaches in which a customary model is identified for each temperature, with no explicit dependence on temperature being directly provided. In addition it offers a compact global model and optimal statistical accuracy. A global Functionally Pooled Vector AutoRegressive with eXogenous excitation model (FP-VARX model) describing the dynamics of the considered beam is then identified using experimental data records. Its analysis indicates that the beam’s natural frequencies decrease with increasing temperature in a somewhat nonlinear or approximately linear fashion, while the dependence on temperature seems weaker, but of potentially more complicated nature, for the damping factors. The global model characteristics are confirmed as being in good agreement with those obtained by conventional multi-model analysis.