Geometric effects on aerodynamic and aeroacoustic performance of a three-element airfoil and data-driven optimization

With the increasing demand for low noise and high efficiency in civil aviation, the integrated aerodynamic and aeroacoustic design of typical high-lift devices has become particularly crucial, while most existing studies primarily focus on either aerodynamic or acoustic characteristics alone. In this paper, the classical 30P30N three-element wing was studied using high-fidelity DES coupled with the FW-H acoustic analogy to investigate the effects of geometric parameters of the leading-edge slat and trailing-edge flap on both aerodynamic and acoustic characteristics. Based on an extensive CFD database, MLP neural network models were developed for intelligent prediction of aerodynamic and acoustic responses, and a generative multi-objective genetic optimization framework was established. The proposed approach enables intelligent prediction and optimization design under typical flow conditions. Results demonstrate that the proposed approach achieves a prediction accuracy exceeding 95%; compared with the baseline configuration, the optimized configuration improves the lift-to-drag ratio by approximately 5%–8% and reduces noise levels by about 3–5 dB . This study provides new insights and methodological support for the intelligent integrated design of three-element high-lift devices.