Fast prediction of aerodynamic noise induced by the flow around a cylinder based on deep neural network

doi:10.1088/1674-1056/ac5e98

Abstract Accurate and fast prediction of aerodynamic noise has always been a research hotspot in fluid mechanics and aeroacoustics. The conventional prediction methods based on numerical simulation often demand huge computational resources, which are difficult to balance between accuracy and efficiency. Here, we present a data-driven deep neural network (DNN) method to realize fast aerodynamic noise prediction while maintaining accuracy. The proposed deep learning method can predict the spatial distributions of aerodynamic noise information under different working conditions. Based on the large eddy simulation turbulence model and the Ffowcs Williams-Hawkings acoustic analogy theory, a dataset composed of 1216 samples is established. With reference to the deep learning method, a DNN framework is proposed to map the relationship between spatial coordinates, inlet velocity and overall sound pressure level. The root-mean-square-errors of prediction are below 0.82 dB in the test dataset, and the directivity of aerodynamic noise predicted by the DNN framework are basically consistent with the numerical simulation. This work paves a novel way for fast prediction of aerodynamic noise with high accuracy and has application potential in acoustic field prediction.

Keywords: aerodynamic noise prediction deep neural network aeroacoustics

Received: 14 January 2022
Revised: 02 March 2022
Accepted manuscript online: 17 March 2022

PACS:	43.28.+h	(Aeroacoustics and atmospheric sound)
	07.64.+z	(Acoustic instruments and equipment)

Fund: Project supported by the National Key Research and Development Program of China (Grant No. 2017YFA0303700), the National Natural Science Foundation of China (Grants Nos. 12174190, 11634006, 12074286, and 81127901), the Innovation Special Zone of the National Defense Science and Technology, High-Performance Computing Center of Collaborative Innovation Center of Advanced Microstructures, and the Priority Academic Program Development of Jiangsu Higher Education Institutions.

Corresponding Authors: Jing Yang
E-mail: yangj@nju.edu.cn

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Hai-Yang Meng(孟海洋), Zi-Xiang Xu(徐自翔), Jing Yang(杨京), Bin Liang(梁彬), and Jian-Chun Cheng(程建春) Fast prediction of aerodynamic noise induced by the flow around a cylinder based on deep neural network 2022 Chin. Phys. B 31 064305

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Fast prediction of aerodynamic noise induced by the flow around a cylinder based on deep neural network

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