Anisotropic source modelling for turbulent jet noise prediction

Author:

Xu Xihai1,Li Xiaodong2ORCID

Affiliation:

1. School of Aeronautic Science and Engineering, Beihang University (BUAA), Beijing 100191, People's Republic of China

2. School of Energy and Power Engineering, Beihang University (BUAA), Beijing 100191, People's Republic of China

Abstract

An anisotropic component of the jet noise source model for the Reynolds-averaged Navier–Stokes equation-based jet noise prediction method is proposed. The modelling is based on Goldstein's generalized acoustic analogy, and both the fine-scale and large-scale turbulent noise sources are considered. To model the anisotropic characteristics of jet noise source, the Reynolds stress tensor is used in place of the turbulent kinetic energy. The Launder–Reece–Rodi model (LRR), combined with Menter's ω -equation for the length scale, with modified coefficients developed by the present authors, is used to calculate the mean flow velocities and Reynolds stresses accurately. Comparison between predicted results and acoustic data has been carried out to verify the accuracy of the new anisotropic source model. This article is part of the theme issue ‘Frontiers of aeroacoustics research: theory, computation and experiment’.

Funder

National Key Research and Development

National Science Foundation of China

Publisher

The Royal Society

Subject

General Physics and Astronomy,General Engineering,General Mathematics

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A physics merged deep neural network-based prediction method for jet turbulent mixing noise;International Journal of Aeroacoustics;2024-01-25

2. Determination of SPL From a Jet Exhaust Using Computational Fluid Dynamics;2020 17th International Bhurban Conference on Applied Sciences and Technology (IBCAST);2020-01

3. Advances in aeroacoustics research: recent developments and perspectives;Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences;2019-10-14

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