Approximation and model management in aerodynamic optimization with variable-fidelity models

This work discusses an approach, first-order approximation and model management optimization (ANIMO), for solving design optimization problems that involve computationally expensive simulations. AMMO maximizes the use of lower-fidelity, cheaper models in iterative procedures with occasional, but systematic, recourse to higher-fidelity, more expensive models for monitoring the progress of design optimization. A distinctive feature of the approach is that it is globally convergent to a solution of the original, high-fidelity problem. Variants of ANIMO based on three nonlinear programming algorithms are demonstrated on a three-dimensional aerodynamic wing optimization problem and a two-dimensional airfoil optimization problem. Euler analysis on meshes of varying degrees of refinement provides a suite of variable-fidelity models. Preliminary results indicate threefold savings in terms of high-fidelity analyses for the three-dimensional problem and twofold savings for the two-dimensional problem.