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.

Internal weapons bay can considerably improve the overall aerodynamic performance of advanced fighters. However, during the bomb dropping, the structural vibration and aerodynamic noise induced by flow fields around internal weapons bay will significantly undermine fighters' safety. This paper first briefly introduces the development of internal weapons bay and summarizes related research regarding cavity-flow-induced vibrations and aerodynamic noise. Later, design requirements for internal weapons bay are proposed based on a systematic analysis of the complex cavity flows related to internal weapons bay. At last, critical technical difficulties and some future research directions are discussed after a thorough study of flow mechanism, numerical simulation methods, wind test techniques, and control strategies for fluid-structure-noise coupled systems. This paper is helpful to the understanding of vital aerodynamic issues related to internal weapons bay. It also provides research directions to the cavity-flow-induced noise and vibrations.