Principal-Component-Analysis-Enhanced Adjoint Aerodynamic and Aeroacoustic Optimization Method

This study presents a principal-component-analysis-enhanced adjoint-based framework for aerodynamic and aeroacoustic multi-objective optimization, aimed at alleviating the aggressive tradeoffs between aerodynamic efficiency and noise reduction commonly encountered in conventional optimization approaches. Principal component analysis (PCA) is employed to analyze the coupling between aerodynamic and aeroacoustic sensitivities, through which a latent favorable search direction is identified that enables simultaneous improvement of both objectives. This direction is incorporated into a unified optimization framework with adjoint-based sensitivity analysis for both aerodynamics and aeroacoustics, while retaining the high-fidelity modeling of unsteady Reynolds-averaged Navier–Stokes equations coupled with the time-domain Ffowcs Williams–Hawkings formulation. The proposed method is validated through multi-objective optimization of a rod–airfoil configuration and an isolated rotor. Comparative results demonstrate that the PCA-enhanced approach achieves a more balanced tradeoff, effectively reducing noise levels without incurring the substantial aerodynamic performance degradation observed in conventional optimization strategies. These results indicate that the proposed method offers a promising tool for practical engineering applications requiring concurrent aerodynamic and aeroacoustic performance improvements.