Robust design optimization considering metamodel uncertainty

The application of metamodel techniques greatly reduces the computational cost in robust design. However, metamodel is only an approximation of the original model resulting in metamodel uncertainty. In the traditional robust design, only parameter uncertainty is considered rather than metamodel uncertainty, which may induce design error. To address this issue, a method based on Monte Carlo sampling is proposed to quantify the metamodel uncertainty in robust design. With the proposed method, the synthesize effect of both parameter and metamodel uncertainties are quantified. The proposed method is applied to robust design for a numerical example and aerodynamic optimization of rocket wrap-around fins. Compared to the traditional method, the results are more accurate and reasonable, which demonstrates the effectiveness of the proposed method.