使用滤波瑞利散射(FRS)和机器学习方法解决航空发动机进气口流动变形问题的进展

IF 2.8 2区 工程技术 Q2 ENGINEERING, MECHANICAL Experimental Thermal and Fluid Science Pub Date : 2024-09-28 DOI:10.1016/j.expthermflusci.2024.111325
Matteo Migliorini , Ulrich Doll , Nicholas J. Lawson , Sergey M. Melnikov , Jonas Steinbock , Michael Dues , Pavlos K. Zachos , Ingo Röhle , David G. MacManus
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引用次数: 0

摘要

要确保雷诺数和马赫数的正确比例以及飞机的适航认证,就必须在飞行中测量气动量。获取此类测量值的能力受到多种挑战的制约,例如仪器安装、环境、测量值类型以及空间和时间分辨率。考虑到在不久的将来嵌入式推进系统的使用会更加频繁,测量技术需要适应复杂流动中多类型流动变形的特征描述,以评估喷气推进系统的可操作性。为了满足对高保真实验数据日益增长的需求,过滤瑞利散射(FRS)方法被认为是一种很有前途的技术,因为它可以同时测量整个空气动力界面平面(AIP)上的压力、温度和三维速度。这项工作展示了新型 FRS 仪器在地面测试设施中的应用,以评估 S 型导管扩散器中的流动变形。将 FRS 结果与立体粒子图像测速仪(S-PIV)测量结果进行比较后发现,在 AIP 处,平面外速度的一致性很好,不超过 3.3%。此外,机器学习方法的引入大大加快了 FRS 数据的处理速度,最高可达 200 倍,为实时数据分析提供了广阔的前景。这项研究展示了 FRS 技术的进一步发展,其最终目标是为飞行环境测量进气道气流畸变。
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Advancements on the use of Filtered Rayleigh Scattering (FRS) with Machine learning methods for flow distortion in Aero-Engine intakes
In-flight measurements of aerodynamic quantities are a requirement to ensure the correct scaling of Reynolds and Mach number and for the airworthiness certification of an aircraft. The ability to obtain such measurement is subject to several challenges such as instrument installation, environment, type of measurand, and spatial and temporal resolution. Given expected, more frequent use of embedded propulsion systems in the near future, the measurement technology needs to adapt for the characterization of multi-type flow distortion in complex flow, to assess the operability of air-breathing propulsion systems. To meet this increasing demand for high-fidelity experimental data, the Filtered Rayleigh Scattering (FRS) method is identified as a promising technology, as it can provide measurements of pressure, temperature and 3D velocities simultaneously, across a full Aerodynamic Interface Plane (AIP). Τhis work demonstrates the application of a novel FRS instrument, to assess the flow distortion in an S-duct diffuser, in a ground testing facility. A comparison of FRS results with Stereo-Particle Image Velocimetry (S-PIV) measurements reveals good agreement of the out of plane velocities, within 3.3 % at the AIP. Furthermore, the introduction of machine learning methods significantly accelerates the processing of the FRS data by up to 200 times, offering a substantial prospect towards real time data analysis. This study demonstrates the further development of the FRS technique, with the ultimate goal of inlet flow distortion measurements for in-flight environments.
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来源期刊
Experimental Thermal and Fluid Science
Experimental Thermal and Fluid Science 工程技术-工程:机械
CiteScore
6.70
自引率
3.10%
发文量
159
审稿时长
34 days
期刊介绍: Experimental Thermal and Fluid Science provides a forum for research emphasizing experimental work that enhances fundamental understanding of heat transfer, thermodynamics, and fluid mechanics. In addition to the principal areas of research, the journal covers research results in related fields, including combined heat and mass transfer, flows with phase transition, micro- and nano-scale systems, multiphase flow, combustion, radiative transfer, porous media, cryogenics, turbulence, and novel experimental techniques.
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