Optimization under turbulence model uncertainty for aerospace design

The computational economy of Reynolds-Averaged Navier-Stokes solvers encourages their widespread use in the optimization of aerospace designs. Unfortunately, the real-world performance of the resulting optimized designs may have shortcomings. A common contributor to this shortfall is a lack of adequately accounting for the uncertainty introduced by the structure of the turbulence model. We investigate whether including measures of turbulence-based uncertainty, as predicted by the eigenspace perturbation method, in an optimization under uncertainty framework can result in designs that are more robust with respect to turbulence model-form uncertainty. In an asymmetric diffuser design problem and a transonic airfoil design problem, our optimization formulation taking account of turbulence-based uncertainty obtained designs that were more robust to turbulence model uncertainty than optimal designs obtained via deterministic approaches.