Quantification of model-form and predictive uncertainty for multi-physics simulation

Traditional uncertainty quantification in multi-physics design problems involves the propagation of parametric uncertainties in input variables such as structural or aerodynamic properties through a single, or series of models constructed to represent the given physical scenario. These models are inherently imprecise, and thus introduce additional sources of error to the design problem. In addition, there often exists multiple models to represent the given situation, and complete confidence in selecting the most accurate model among the model set considered is beyond the capability of the user. Thus, quantification of the errors introduced by this modeling process is a necessary step in the complete quantification of the uncertainties in multi-physics design problems. In this work, a modeling uncertainty quantification framework was developed to quantify to quantify both the model-form and predictive uncertainty in a design problem through the use of existing methods as well as newly developed modifications to existing methods in the literature. The applicability of this framework to a problem involving full-scale simulation was then demonstrated using the AGARD 445.6 Weakened Wing and three different aeroelastic simulation packages to quantify the flutter conditions of the wing.