航空发动机风扇宽频噪声预测技术发展研究  

Development of Aeroengine Fan Broadband Noise Prediction Methods

作　　者：甘露 GAN Lu(Commercial Aircraft Engine Co.,Ltd.,AECC,Shanghai 200000,China)

机构地区：[1]中国航发商用航空发动机有限责任公司,上海200000

出　　处：《航空计算技术》2023年第6期124-128,共5页Aeronautical Computing Technique

基　　金：国家科技重大专项资助(2017-Ⅱ-0008-0022)。

摘　　要：综述了风扇宽频噪声预测技术在航空发动机噪声控制领域的研究进展和应用,包括经验模型、分析模型、计算气动声学和机器学习等方法。对这些方法的发展现状进行了回顾,并对国内外情况进行了对比分析。研究发现,不同的预测模型适用于不同阶段的设计过程,如经验模型适用于概念设计阶段,分析模型适用于通流设计阶段,计算气动声学适用于详细设计阶段。此外,基于机器学习的风扇尾迹湍流预测是一个重要的研究方向,提高训练效率和预测精度将显著提升机器学习的适用性。最后,对风扇宽频噪声预测方法进行了简要总结和展望。An overview of the research progress and applications of fan rotor-stator interaction broadband noise prediction techniques in the field of aircraft engine noise control is provided.The methods,including empirical models,analytical models,computational aeroacoustics(CAA),and machine learning are reviewed.The development status of these methods is discussed and compared with international research.It is found that different prediction methods are suitable for different stages of the design process,such as empirical models for conceptual design,analytical models for through-flow design,and CAA for detailed design.Additionally,the prediction of fan wake turbulence based on machine learning is an important research direction,the applicability of machine learning will be greatly enhanced via the improvement of the training efficiency and prediction accuracy.Finally,a brief summarization and future prospects of fan broadband noise prediction methods are provided.

关 键 词：计算气动声学 轴流风扇 宽频噪声 机器学习 经验模型 分析模型 

分 类 号：V23[航空宇航科学与技术—航空宇航推进理论与工程]