航空气动噪声机器学习研究进展

Research progress in machine learning of aviation aerodynamic noise

气动噪声源自气体流动过程中的压力脉动,可能引发声疲劳和声振耦合,是航空器安全性和舒适性的重要影响因素,其研究方法主要包括理论方法、风洞试验测量和数值模拟。然而,这些方法存在测量结果单一、难以与流动结构建立有效关联、高精度噪声数据获取困难等问题。机器学习方法具有高效、快速、低成本等优势,在航空气动噪声领域展现出巨大潜力。本文概述了机器学习在航空气动噪声领域的最新研究进展,重点综述了其在稀疏测点下声场重构和气动噪声预测方面的应用。最后,分析了目前机器学习方法在气动噪声研究中泛化性弱、预测精度不足、缺乏物理解释性等共性问题,并展望了其未来发展趋势,为基于机器学习方法进行气动噪声研究提供参考。

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.