Noise characteristic of the nacelle/pylon/slat juncture in a realistic high-lift aircraft configuration

Bai, Baohong; Li, Xiaodong

doi:10.1016/j.ast.2022.107694

Cited by 6 publications

(1 citation statement)

References 28 publications

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“…Aircraft noise consists of many sources, including jet noise, 1 fan noise, 2 landing gear noise, 3 high-lift device noise 4 and the pylon/nacelle juncture noise. 5 The ranking of these noise sources is different under different operating conditions.…”

Section: Introductionmentioning

confidence: 99%

A physics merged deep neural network-based prediction method for jet turbulent mixing noise

Bai,

Zhang,

et al. 2024

International Journal of Aeroacoustics

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Turbulent mixing noise is a vital component of jet noise, and its rapid, accurate prediction has always been persistently pursued. Recent advancement in machine learning has been applied to jet noise prediction. However, these applications are pure curve fitting and lack physical constraints. In this study, a physics-merged deep neural network (PMNN)-based prediction method was developed for turbulent mixing jet noise by merging the physics of the jet noise. This deep neural network (DNN)-based method employed recent advancements in jet turbulent mixing noise containing large- and fine-scale turbulence structures. Two simple rational functions for large- and fine-scale turbulent noise similarity spectra were proposed to replace the original complex similarity spectra functions and incorporated into the DNN-based prediction method. For comparison, we present two data-driven DNN-based prediction methods (DDNN). The first DDNN method used the sound pressure level (SPL) as the output of neural networks, directly establishing the nonlinear relationship between the input features and SPL. In the second DDNN method, the dominant modes of the jet noise spectra extracted using the proper orthogonal decomposition method were merged with DNN. These DNN-based methods were then trained using a set of experimental data over a wide range of jet operating conditions. Their performance was evaluated and compared. It was evident that all these DNN-based methods were capable of predicting turbulent mixing noise reasonably well. In contrast to the DDNN methods, the PMNN method could provide insights into the jet turbulent mixing noise components. It demonstrates that the turbulent mixing jet noise spectra at the mid polar angle is generated by the large-scale noise component at low-frequency range and by the fine-scale noise component at high-frequency range.

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