Multi-Fidelity Calibration of Input-Dependent Model Parameters

Abstract

The aim of this research is to investigate the use of non-linear structural dynamics computational models with multiple levels of fidelity for the calibration of input dependent system parameters. Non-linear materials often lead to system parameters that are input dependent (function of time, temperature, loading, etc.). Different types of models may also 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 input-dependent model parameter calibration with a low-fidelity model corrected using higher fidelity simulation data.

The methodology is illustrated for a curved panel located near a hypersonic aircraft engine, subjected to acoustic loading, where the damping properties of the panel are dependent on the acoustic loading magnitude. Two models (a frequency response analysis and a full time history analysis) are combined to estimate the damping characteristics of the panel. The aim of this study is to develop a novel approach of fusing information from models of different levels of fidelity in the Bayesian calibration of input dependent model parameters.