Multifidelity data fusion via bayesian inference

We consider the fusion of two aerodynamic data sets originating from differing fidelity physical or computer experiments. We specifically address the fusion of: 1) noisy and incomplete field from wind tunnel measurements and 2) deterministic but biased fields from numerical simulations. These two data sources are fused in order to estimate the true field that best matches measured quantities of interest that are a function of the field itself. For example, two sources of pressure fields about an aircraft are fused based on measured forces and moments from a wind-tunnel experiment. We employ a Bayesian framework to infer the true fields conditioned on measured values of certain quantities of interest. Essentially we perform a statistical correction to the fields obtained from physical and computer experiments. Additionally, we also show how to propagate the uncertainty in the original data into the fused data. The formulation of the methodology and its demonstration on the flow past the RAE2822 airfoil and the Common Research Model wing at transonic conditions are discussed.