Data-Driven Sensitivity Analysis of the Influence of Geometric Parameterized Variables on Flow Fields Under Different Design Spaces

In aerodynamic shape optimization, geometric parameterized variables have a significant impact on the flow field, thereby influencing both the effectiveness and efficiency of the optimization process. This paper utilizes flow field data from computational fluid dynamics (CFD) to develop a data-driven approach for analyzing the influence of geometric parameterized variables on the objective function and flow field across various design spaces. A data-driven method, namely a sensitivity analysis based on the Kriging model, is proposed along with three design space variation schemes (scaling, translation, and their combination) to evaluate the influence of geometric parameters on the objective function under varying design spaces. Furthermore, the study investigates the effects of these design space variation schemes on the sensitivity results using two test functions and a wave drag reduction case. The results of the wave drag reduction case are further analyzed in relation to flow conditions, including the Mach number and shock-wave strength. The findings indicate that design space variations alter the relationship between geometric parameters and the flow field, particularly affecting the shock-wave position and strength, as reflected by the sensitivity indices of the variables. Additionally, the sensitivity results show a strong dependence on the Mach number under varying design space configurations.