航空气动噪声机器学习研究进展

Aerodynamic noise originates from pressure fluctuations during gas flow, which can lead to acoustic fatigue and acoustic-structural coupling, and is a significant factor affecting the safety and comfort of aircraft. Research methods for aerodynamic noise primarily include theoretical approaches, wind tunnel testing, and numerical simulation. However, these methods suffer from limitations such as singular measurement results, difficulty in establishing effective correlations with flow structures, and challenges in obtaining high-precision noise data. Machine learning methods, characterized by their efficiency, speed, and low cost, have shown great potential in the field of aeronautical aerodynamic noise. This paper provides an overview of the latest research progress in machine learning applied to aeronautical aerodynamic noise, with a focus on the reconstruction of sound fields under sparse measurement points and the prediction of aerodynamic noise. Finally, the paper analyzes common issues in machine learning methods for aerodynamic noise research, such as weak generalizability, insufficient prediction accuracy, and lack of physical interpretability, and looks forward to future development trends, offering a reference for aerodynamic noise research based on machine learning methods.