Assessment of Model Validation, Calibration, and Prediction Approaches in the Presence of Uncertainty

Abstract

Model validation is the process of determining the degree to which a model is an accurate representation of the true value in the real world. The results of a model validation study can be used to either quantify the model form uncertainty or to improve/calibrate the model. However, the model validation process can become complicated if there is uncertainty in the simulation and/or experimental outcomes. These uncertainties can be in the form of aleatory uncertainties due to randomness or epistemic uncertainties due to lack of knowledge. Four different approaches are used for addressing model validation and calibration: 1) the area validation metric (AVM), 2) a modified area validation metric (MAVM) with confidence intervals, 3) the standard validation uncertainty from ASME V&V 20, and 4) Bayesian updating of a model discrepancy term. Details are given for the application of the MAVM for accounting for small experimental sample sizes. To provide an unambiguous assessment of these different approaches, synthetic experimental values is generated from computational fluid dynamics simulations of a multi-element airfoil. A simplified model is then developed using thin airfoil theory. This simplified model is then assessed using the synthetic experimental data. Each of these validation/calibration approaches are assessed for the ability to tightly encapsulate the true value in nature at locations both where experimental results are provided and prediction locations where no experimental data are available.