Prediction Considering Multi-Model and Model Form Uncertainty in the Parameter Space

In some engineering problems, more than one model can be created for structural behavior simulation. In order to get the reliable results, model selection uncertainty and model form uncertainty can't be ignored. In this research, different models' degree of belief is computed by combining the Bayesian method with the experimental data. The adjustment factor approach is used to propagate the model selection uncertainty into the prediction of a system response quantity (SRQ). The simulation results at the calibration positions are gotten by combining the interval addition algorithm with the confidence interval (CI) of the model form uncertainty and the model selection uncertainty. The 95% CI of SRQ at the interpolation and extrapolation position is calculated by the piecewise cubic hermite interpolating polynomial method. Finally, prediction methodology is used to analyze an aircraft engineering problem for predicting the aerodynamic coefficient in condition of different attack angle. The great agreement between the prediction results and the experimental results shows that the method in the paper is valuable in the engineering simulation.