基于改进的四维声学类比模型和多域特征分析的流致噪声预测

IF 1.2 4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Wensi Zheng, Qiuhong Liu, Jinsheng Cai, Fang Wang
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引用次数: 0

摘要

本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predicting Flow-Induced Noise Based on an Improved Four-Dimensional Acoustic Analogy Model and Multi-Domain Feature Analysis
Flow-induced noise issues are widely present in practical engineering fields. Accurate prediction of noise signals is fundamental to studying the mechanism of noise generation and seeking effective noise suppression methods. Complete acoustic field information often includes both acoustic pressure and velocity vectors. However, the classic acoustic analogy theory can only consider the feature distribution of acoustic pressure. This study starts from the dimensionless Navier-Stokes equations followed by fluid motion and, with the concept of electromagnetic analogy, introduces a vector form of the fluctuation equation that includes density perturbations and velocities in three directions. By choosing the permeable integral surface surrounding the object as the sound source surface, this study further analyzes the composition of the volume source term and extract the complete load source term, proposing the time-domain integral analytical formula T4DC and the frequency-domain integral formula F4DC. Numerical predictions for stationary dipoles and rotating monopoles are carried out in the time domain, frequency domain, and spatial domain. The numerical results show that the time-domain and frequency-domain noise obtained by this method can be consistent with the analytical solution, while the method of Dunn has a significant difference from the analytical solution, especially for dipole noise distribution. Compared with the accurate solution, the acoustic velocity amplitude error obtained by Dunn's method reached more than 35% at m=1 frequency, fully demonstrating that our method can accurately predict far-field acoustic pressure and velocity vectors.
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来源期刊
Traitement Du Signal
Traitement Du Signal 工程技术-工程:电子与电气
自引率
21.10%
发文量
162
审稿时长
>12 weeks
期刊介绍: The TS provides rapid dissemination of original research in the field of signal processing, imaging and visioning. Since its founding in 1984, the journal has published articles that present original research results of a fundamental, methodological or applied nature. The editorial board welcomes articles on the latest and most promising results of academic research, including both theoretical results and case studies. The TS welcomes original research papers, technical notes and review articles on various disciplines, including but not limited to: Signal processing Imaging Visioning Control Filtering Compression Data transmission Noise reduction Deconvolution Prediction Identification Classification.
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