航空气动噪声机器学习研究进展  

Research progress in machine learning of aviation aerodynamic noise

作　　者：张巧 杨党国[4] 吴德松 张伟伟[1,2,3] ZHANG Qiao;YANG Dangguo;WU Desong;ZHANG Weiwei(School of Aeronautics,Northwestern Polytechnical University,Xi'an 710072,China;International Joint Institute of Artificial Intelligence on Fluid Mechanics,Northwestern Polytechnical University,Xi'an 710072,China;National Key Laboratory of Aircraft Configuration Design,Xi'an 710072,China;China Aerodynamics Research and Development Center,Mianyang 621000,China)

机构地区：[1]西北工业大学航空学院,西安710072 [2]西北工业大学流体力学智能化国际联合研究所,西安710072 [3]飞行器基础布局全国重点实验室,西安710072 [4]中国空气动力研究与发展中心,绵阳621000

出　　处：《空气动力学学报》2024年第11期1-17,I0001,共18页Acta Aerodynamica Sinica

基　　金：国家自然科学基金(92152301);四川省自然科学基金(2023NSFSC0006)。

摘　　要：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.

关 键 词：机器学习 气动噪声 声场重构 压缩感知 

分 类 号：V211.78[航空宇航科学与技术—航空宇航推进理论与工程]