Tuesday  |  Salon 9  |  09:20 AM–09:40 AM

#16881–Stochastic Model Correction for the Adaptive Vibration Isolation Round-Robin Challenge

Low-fidelity structural dynamics models reveal critical system features, allow for real-time control, and provide ballpark predictions. However, these models are sometimes too simplified to be reliable. In the adaptive vibration isolation round-robin challenge problem, a low-fidelity two-mass oscillator model struggles to capture nonlinear behaviors, resulting in high model-form error and discrepancies between model predictions and experimental data. In this work, we explore how to reduce model-form error without increasing the number of states in the model, which would incur greater computational costs. In particular, we embed a stochastic model correction into the low-fidelity model, yielding an enriched model. The correction is informed by physical theory, calibrated with experimental data, and validated using posterior predictive assessments. While the enriched model does not perfectly capture the experimental data, it improves consistency with data over a range of experimental scenarios

Rileigh Bandy   University of Colorado Boulder
Teresa Portone   Sandia National Labratories
Rebecca Morrison   University of Colorado