PREDICTION BASED ON MULTI-MODELS IN THE PARAMETRIC SPACE

In some engineering problems, multi-models are used to simulate the structural behavior. In order to obtain reliable prediction results, the model selection uncertainty and the model form uncertainty should be considered. In this paper, different model's degrees of confidence are computed by combining the Bayesian method with the test data. The adjustment factor approach is used to pass the model selection uncertainty into the prediction of a system response, and then the response confidence interval is obtained from the results of the synthesis model. The final simulation results are obtained by combining the confidence interval of the model form uncertainty with the results of the synthesis model. The confidence interval at the prediction position is calculated by the interpolation method. Finally, the methodology is applied to the prediction of the aerodynamic coefficient of some aircraft subjected to different angles of attack. The agreement between the prediction results and the test results shows that the method in the paper is feasible in the engineering simulation.