  Explicit quantification of uncertainty in engineering simulations is being
increasingly used to inform robust and reliable design practices. In the
aerospace industry, computationally-feasible analyses for design optimization
purposes often introduce significant uncertainties due to deficiencies in the
mathematical models employed. In this paper, we discuss two recent improvements
in the quantification and combination of uncertainties from multiple sources
that can help generate probabilistic aerodynamic databases for use in aerospace
engineering problems. We first discuss the eigenspace perturbation methodology
to estimate model-form uncertainties stemming from inadequacies in the
turbulence models used in Reynolds-Averaged Navier-Stokes Computational Fluid
Dynamics (RANS CFD) simulations. We then present a multi-fidelity Gaussian
Process framework that can incorporate noisy observations to generate
integrated surrogate models that provide mean as well as variance information
for Quantities of Interest (QoIs). The process noise is varied spatially across
the domain and across fidelity levels. Both these methodologies are
demonstrated through their application to a full configuration aircraft
example, the NASA Common Research Model (CRM) in transonic conditions. First,
model-form uncertainties associated with RANS CFD simulations are estimated.
Then, data from different sources is used to generate multi-fidelity
probabilistic aerodynamic databases for the NASA CRM. We discuss the
transformative effect that affordable and early treatment of uncertainties can
have in traditional aerospace engineering practices. The results are presented
and compared to those from a Gaussian Process regression performed on a single
data source.
