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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.

To facilitate future Hypersonic Flight Vehicle (HFV) implementation with high maneuverability throughout its reentry trajectory, an Active Jets-Matrix (AJM) is designed to serve as the flapless pitch control surface. The AJM consists of four control groups composed in total of 48 supersonic nozzles. The AJM aims to utilize the jet flow-interaction-induced additional control moment to improve the control efficiency during atmospheric entry. A comparative research method is employed to study the eight simulation cases for three different HFV configurations (baseline, mechanical control surface with 30° deflection, and the AJM configuration) and two AJM control moment adjustment strategies (nozzle chamber pressure regulation and discretized nozzle group on–off control). A conventional in-house computational fluid dynamics (CFD) solver with the two-equation SST turbulence model is employed to undertake the simulation tasks. Simulation results indicate that: (a) only the AJM configuration is capable of trimming the HFV in pitch channel; (b) nonlinearity exists between the augmentation moment and the specific control variable from respective adjustment strategies; (c) the chamber pressure regulation strategy bears higher overall efficiency, while the discretized control strategy induces more intense local jet-flow interaction. With a maximum control moment augmentation of 1.58, the AJM presents itself as a competitive candidate for future HFV flapless control methods.