A quantitative assessment of the model form error of friction models across different interface representations for jointed structures. (15th January 2022) - Porter, Justin H.; Balaji, Nidish Narayanaa; Little, Clayton R.; Brake, Matthew R.W.
Highlights: Model form error is considered for different model strategies for jointed structures. Twenty-six models (ten friction models and two representations) are considered. The suitability of the Masing assumptions for joint dynamics are evaluated. In addition to frictional damping, material damping is necessary for quality fits. Fits based on the Masing assumptions have additional errors for transient analyses. Abstract: Hysteretic models are widely used to model frictional interactions in joints to recreate experimental behavior. However, it is unclear which models are best suited for fitting or predicting the responses of structures. The present study evaluates 26 friction model/interface representation combinations to quantify the model form error. A Quasi-Static Modal Analysis approach (termed Rayleigh Quotient Nonlinear Modal Analysis) is adopted to calculate the nonlinear system response, and a Multi-Objective Optimization is solved to fit experimental data of the first mode of the Brake-Reuß Beam. Optimized parameters from the first mode are applied to the second and third bending modes to quantify the predictive ability of the models. Formulations for both tracing full hysteresis loops and recreating hysteresis loops from a single loading curve (Masing assumptions) are considered. Smoothly varying models applied to a five patch representation showed the highest flexibility (for fitting mode 1) and good predictive potential (for modes 2 and 3). For a second formulation, which uses 152 frictional elements to represent the interface, the physically motivated spring in series with a Coulomb slip model (elastic dry friction) has high error for fitting mode 1 and performs near the middle for predicting higher modes. For both interface representation, the best fit models are not the most physical, but rather the ones with the most parameters (as expected); however, the more physical models perform somewhat better for predicting the higher modes.