Uncertainty optimization design of airfoil based on adaptive point adding strategy

Abstract

A new and effective uncertainty optimization design framework based on adaptive point adding strategy is proposed to design and optimize the supercritical airfoil which is sensitive to Mach number variation. The new framework combines non-intrusive polynomial chaos (NIPC) method, Kriging model, lower confidence bounding (LCB) infill criterion, and global GlobalSearch algorithm. NIPC method is used to quantify the uncertainty of Mach number. A new class/shape function transformation method is used to parameterize the airfoil shapes and Kriging model is selected to establish the surrogate model of the aerodynamic optimization design. The LCB infill criterion is proposed to update the initial surrogate model by adding the optimal design point. The global GlobalSearch algorithm is used to optimize the surrogate model. The uncertainty optimization results are compared with the deterministic optimization results. It is shown that the proposed uncertainty optimization design based on adaptive point adding strategy has higher robustness under condition of Mach number variation, the optimization expense can be reduced to 20.76%, and the efficiency of optimization design is improved.