Full-space approach to aerodynamic shape optimization

Aerodynamic shape optimization (ASO) involves finding an optimal surface while constraining a set of nonlinear partial differential equations (PDE). The conventional approaches use quasi-Newton methods operating in the reduced-space, where PDE constraints are eliminated at each design step by decoupling flow solver from optimizer. Conversely, full-space Lagrange-Newton-Krylov-Schur (LNKS) approach couples and iteration simultaneously minimizing objective function improving feasibility constraints, which requires fewer iterations forward problem. Additionally, second-order information leads to number cycles independent control variables. We discuss necessary ingredients build efficient LNKS ASO framework as well intricacies their implementation. is then compared reduced-space on benchmark two-dimensional test case using high-order discontinuous Galerkin method discretize constraint.