Research progress in machine learning of aviation aerodynamic noise

ZHANG Qiao; YANG Dangguo; WU Desong; ZHANG Weiwei

doi:10.7638/kqdlxxb-2024.0036

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Research progress in machine learning of aviation aerodynamic noise

更新时间：2026-07-27

- Research progress in machine learning of aviation aerodynamic noise
- Vol. 42, Issue 11, Pages: 1-17(2024)
- 作者机构：
  
  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
- 作者简介：
- 基金信息：
- DOI：10.7638/kqdlxxb-2024.0036
  CLC： V211.78
- Published：2024
- 稿件说明：
移动端阅览
张巧, 杨党国, 吴德松, 等. 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.

ZHANG Qiao, YANG Dangguo, WU Desong, et al. Research progress in machine learning of aviation aerodynamic noise[J]. 2024, 42(11): 1-17.
张巧, 杨党国, 吴德松, 等. 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. DOI： 10.7638/kqdlxxb-2024.0036

ZHANG Qiao, YANG Dangguo, WU Desong, et al. Research progress in machine learning of aviation aerodynamic noise[J]. 2024, 42(11): 1-17. DOI： 10.7638/kqdlxxb-2024.0036.

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。

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

Keywords

references

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