A Design-Driven Validation Approach Using Bayesian Prediction Models

In most of the existing work, model validation is viewed as verifying the model accuracy, measured by the agreement between computational and experimental results. Due to the lack of resource, accuracy can only be assessed at very limited test points. However, from the design perspective, a good model should be considered the one that can provide the discrimination (with good resolution) between competing design candidates under uncertainty. In this work, a design-driven validation approach is presented. By combining data from both physical experiments and the computer model, a Bayesian approach is employed to develop a prediction model as the replacement of the original computer model for the purpose of design. Based on the uncertainty quantification with the Bayesian prediction and, subsequently, that of a design objective, some decision validation metrics are further developed to assess the confidence of using the Bayesian prediction model in making a specific design choice. We demonstrate that the Bayesian approach provides a flexible framework for drawing inferences for predictions in the intended, but maybe untested, design domain. The applicability of the proposed decision validation metrics is examined for designs with either a discrete or continuous set of design alternatives. The approach is demonstrated through an illustrative example of a robust engine piston design.