Aerodynamic–Stealth Optimization of an S-Shaped Inlet Based on Co-Kriging and Parameter Dimensionality Reduction

Aiming at the challenges of high dimensionality in both design variables and optimization objectives, along with high computational resource consumption in the multi-disciplinary optimization of aerodynamic and stealth performance for an unmanned aerial vehicle (UAV) S-shaped inlet, this paper proposes a multi-objective optimization method that integrates design variable dimensionality reduction and a Co-Kriging multi-fidelity surrogate model. First, the S-shape inlet was defined by utilizing parametric modeling with a total of 11 design variables. Simulations were performed to obtain a subset of samples, and Sobol’ sensitivity analysis was applied to eliminate parameters with minor influence on performance, thereby achieving design variable dimensionality reduction. Subsequently, a Co-Kriging surrogate model was constructed. Based on the Multi-Objective Evolutionary Algorithm Based on Decomposition (MOEA/D) algorithm, multi-objective optimization was carried out with the total pressure recovery coefficient, total pressure distortion coefficient, and the average forward radar cross-section (RCS) as the optimization objectives, yielding a Pareto front solution set. Finally, three optimized inlets were selected from the Pareto front and compared with the original inlet to evaluate their aerodynamic and stealth performance. The results demonstrate that the proposed optimization method balances efficiency and accuracy effectively, significantly increasing the total pressure recovery coefficient while markedly reducing the total pressure distortion coefficient and RCS of the optimized inlet.