Development of a metamodel assisted sampling approach to aerodynamic shape optimization problems

A new metamodel-assisted sampling search approach applied to the aerodynamic shape optimization of turbomachinery airfoils is presented in this paper. The proposed methodology integrates a non-uniform rational B-spline (NURBS) geometry representation, a two-dimensional flow analysis, and an improved metamodel driven optimization algorithm named approximated promising region identifier (APRI), which represents a momentous advancement of the existing space exploration techniques specifically for the high-dimensional expensive black-box (HEB) problems. The novel optimization method prospects the whole design space by generating sample points, reporting evaluating information using a surrogate model, and then focusing the search in the most promising region by deploying more agents. Using the integration of these adaptive tools and methods, the optimization results are considerably promising in terms of computational efficiency and performance enhancement of the turbomachinery blade airfoil shape in both design and off-design conditions.