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

作     者：孟海洋 徐自翔 杨京 梁彬 程建春 Hai-Yang Meng;Zi-Xiang Xu;Jing Yang;Bin Liang;Jian-Chun Cheng

作者机构：Key Laboratory of Modern AcousticsMOEInstitute of AcousticsDepartment of PhysicsNanjing UniversityNanjing 210093China Collaborative Innovation Center of Advanced MicrostructuresNanjing UniversityNanjing 210093China 

出 版 物：《Chinese Physics B》 (中国物理B（英文版）)

年 卷 期：2022年第31卷第6期

页      面：470-475页

核心收录：

学科分类：12[管理学] 080704[工学-流体机械及工程] 08[工学] 0710[理学-生物学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 080103[工学-流体力学] 0807[工学-动力工程及工程热物理] 0714[理学-统计学（可授理学、经济学学位）] 0835[工学-软件工程] 0701[理学-数学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 0702[理学-物理学] 0801[工学-力学（可授工学、理学学位）] 

基　　金：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 

主　　题：aerodynamic noise prediction deep neural network aeroacoustics 

摘      要：Accurate and fast prediction of aerodynamic noise has always been a research hotspot in fluid mechanics and *** conventional prediction methods based on numerical simulation often demand huge computational resources,which are difficult to balance between accuracy and ***,we present a data-driven deep neural network(DNN)method to realize fast aerodynamic noise prediction while maintaining *** proposed deep learning method can predict the spatial distributions of aerodynamic noise information under different working *** on the large eddy simulation turbulence model and the Ffowcs Williams-Hawkings acoustic analogy theory,a dataset composed of 1216samples is *** reference to the deep learning method,a DNN framework is proposed to map the relationship between spatial coordinates,inlet velocity and overall sound pressure *** 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 *** work paves a novel way for fast prediction of aerodynamic noise with high accuracy and has application potential in acoustic field prediction.