空气动力学学报2024，Vol.42Issue(11)：1-17,17.DOI:10.7638/kqdlxxb-2024.0036
航空气动噪声机器学习研究进展
Research progress in machine learning of aviation aerodynamic noise
摘要
Abstract
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Key words
machine learning/aerodynamic noise/acoustic field reconstruction/compressed sensing分类
航空航天引用本文复制引用
张巧,杨党国,吴德松,张伟伟..航空气动噪声机器学习研究进展[J].空气动力学学报,2024,42(11):1-17,17.基金项目
国家自然科学基金(92152301) （92152301）
四川省自然科学基金(2023NSFSC0006) （2023NSFSC0006）
