Multi-fidelity approach to dynamics model calibration

This paper investigates the use of structural dynamics computational models with multiple levels of fidelity in the calibration of system parameters. Different types of models may be available for the estimation of unmeasured system properties, with different levels of physics fidelity, mesh resolution and boundary condition assumptions. In order to infer these system properties, Bayesian calibration uses information from multiple sources (including experimental data and prior knowledge), and comprehensively quantifies the uncertainty in the calibration parameters. Estimating the posteriors is done using Markov Chain Monte Carlo sampling, which requires a large number of computations, thus making the use of a high-fidelity model for calibration prohibitively expensive. On the other hand, use of a low-fidelity model could lead to significant error in calibration and prediction. Therefore, this paper develops an approach for model parameter calibration with a low-fidelity model corrected using higher fidelity simulations, and investigates the trade-off between accuracy and computational effort. The methodology is illustrated for a curved panel located in the vicinity of a hypersonic aircraft engine, subjected to acoustic loading. Two models (a frequency response analysis and a full time history analysis) are combined to calibrate the damping characteristics of the panel.