带锯齿尾缘翼型的半经验噪声预测

Y. Mayer, B. Lyu, Hasan Kamliya Jawahar, M. Azarpeyvand
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引用次数: 6

摘要

©2018由美国航空航天研究所,Inc。版权所有。建立了一种新的锯齿形尾缘噪声预测模型,并成功地应用于具有直尾缘和锯齿形尾缘的实际翼型。该模型与Howe模型相比有几个优点,因为它利用迭代过程来求解控制偏微分方程,而不是衍射格林函数。新模型同时考虑了相消干涉和相消干涉,也适用于高马赫数的情况。TNO和Chase波数-频率模型都被实现为STE噪声预测模型的输入。必要的边界层输入参数可以通过实验或标准计算流体力学模拟得到。在这项工作中,我们使用了RANS CFD模拟来获得TNO和Chase模型所需的边界层信息。将STE模型与实验数据进行了远场噪声比较。本文还进行了一项研究,以确定锯齿区域上最适合提取边界层参数的位置,并研究了由于锯齿的存在而引起的流动变化。在锯齿区不同的边界层提取位置,预测的远场降噪最大可达5 dB。所实现的STE模型有可能提高当前的噪声预测能力,从而改进服务设计。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Toward a Semi-Empirical Noise Prediction for Airfoils with Serrated Trailing Edges
© 2018 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved. A novel serrated trailing edge (STE) noise prediction model has been implemented and applied successfully to realistic airfoils with straight and serrated trailing edges. This model has several advantages over Howe's model as it utilizes an iterative procedure to solve the governing partial differential equations as opposed to the diffraction Green's function. The new model also takes both destructive and constructive interferences into account and is also applicable to high Mach numbers. Both the TNO and Chase wavenumber-frequency models have been implemented as input to the STE noise prediction model. The necessary boundary layer input parameters can be obtained from experiments or standard computational fluid dynamics simulations. In this work, we have used RANS CFD simulations to obtain the boundary layer information needed for the TNO and Chase models. Far-field noise comparisons are provided between the STE model and experimental data. A study to determine the most suitable location over the serration area to extract the boundary layer parameters has also been conducted and the changes in the flow due to the presence of the serrations are investigated. Differences in the predicted far-field sound reduction of up to 5 dB have been found for different boundary layer extraction locations over the serration area. The implemented STE model has the potential to improve the current noise prediction capabilities, and thereby improve serration design.
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