Publications

Aerodynamic design optimization currently lacks robustness with respect to the starting design and requires trial and error in the flow solver and optimization algorithm settings to get a converged optimal design. We address need issue by developing ways to overcome robustness issues arising from shape parametrization, mesh deformation, and flow solver convergence. Our approach is demonstrated on the Aerodynamic Design Optimization Discussion Group (ADODG) airfoil optimization benchmarks to show the factors that dominate the robustness and efficiency. In the ADODG NACA 0012 benchmark, we address the additional issue of non-unique solutions. In the ADODG RAE 2822 case, we address solver failure due to shock waves, separation, and gradient accuracies due to the frozen turbulence model. Finally, we create a new, challenging aerodynamic shape optimization case that starts with a circle to test the robustness of our aerodynamic shape optimization framework. We use both fixed and adaptive parametrization methods to tackle this problem and show how we can exploit the advantages of adaptive parametrization methods to improve both robustness and efficiency. The combination of flow solver robustness, precision of gradient information, robust mesh deformation, and adaptive parametrization brings us closer to a “push-button” solution for airfoil design.