A better understanding of model updating strategies in validating engineering models

There is a growing recognition that model validation is not merely a process of assessing the accuracy of a computer model, but also a process to improve the model by possible means suggested by the validation results. Our interest in this work is to achieve a better understanding of the various model updating strategies, which utilize mathematical means to update a computer model based on both physical and computer observations. We examine different model updating formulations, e.g., calibration and bias correction, as well as different solution methods. As an alternative to the existing Bayesian approaches, we propose in this work a new solution method that reformulates a computer model to better interpret the observed dispersion of experimental data using the Maximum Likelihood Estimation (MLE) method. Uncertainty in model prediction is quantified to account for various sources of uncertainty in a validation process. Furthermore, we employ the newly developed u-pooling method (by Ferson et al.) as a validation metric to assess the accuracy of an updated model over a region of interest. Using the benchmark thermal challenge problem as an example, we study several possible model updating formulations using the proposed methodology. The effectiveness of the various formulations is examined. The benefits and limitations of using the MLE method versus the Bayesian approach are presented. Insights into various model updating strategies are provided through this study. Copyright © 2008 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.