Global convergence of unconstrained and bound constrained surrogate-assisted evolutionary search in aerodynamic shape design

In this paper, we present an evolutionary framework for efficient aerodynamic shape design. The approach suggests employing hybrid evolutionary algorithm with gradient-based local search method in the spirit of Lamarckian and surrogate models that approximates the computationally expensive adjoint computational fluid dynamics during design search. In particular, we reveal that the proposed framework guarantees global convergence by inheriting the properties of trust-region method to interleave use of the exact solver for the objective function with computationally cheap surrogate models during local search. Empirical results on 2D airfoil shape design using an adjoint inverse pressure design problem indicates that the approaches global convergences on a limited computational budget.