A collaborative model calibration framework under uncertainty considering parameter distribution

Abstract

Model calibration is critical to update unknown parameters and model differences between simulation and experiment. While some of the current methods can quantify response uncertainty and estimate the likelihood function of unknown parameters, calibration remains challenging when the parameters are uncertainties of unknown distribution. Considering model uncertainty inversion, we propose a collaborative calibration framework that quantifies and calibrates the distribution of unknown parameters. We used the Nested Stochastic Kriging (NSK) model to estimate the global trend and uncertainty of response data. The unknown parameters are then updated using the optimization-based calibration (OBC) method, where an infill sampling criterion is proposed. Finally, the variance of the parameter is calibrated. The calibrated model can reflect the uncertainty of the response data and can be further used to estimate the distribution of a new design. The proposed approach is tested using two numerical instances, and the bolt simulation model under tension-bending condition is calibrated using 30 experimental data points. The results demonstrate that the proposed method can achieve the best performance in terms of accuracy and efficiency.