Spectral Analysis of Aeroacoustic Noise Using CAE Tools

This presentation was made at CAASE18, The Conference on Advancing Analysis & Simulation in Engineering. CAASE18 brought together the leading visionaries, developers, and practitioners of CAE-related technologies in an open forum, to share experiences, discuss relevant trends, discover common themes, and explore future issues.

Resource Abstract

The spectral representation of near field microphones will have significant contributions from hydrodynamic pressure. It is important to know the convective and acoustic contribution so as to relate radiating part as well as the part corresponds to structural excitation from the spectra. In general, the acoustic waves for given frequency can be identified on wavenumber space as k=2*pi*f/c and so the convective wave using kc=2*pi*f/Uc. Here c is speed of sound and Uc is mean free stream velocity. Most often, automotive simulations are focused on speed corresponds to Mach number less than 0.1 and hence for automotive cases the the acoustic and turbulent wavenumbers are di?erent from a factor 10 (Arguillat et al)
As the sound velocity is one order of magnitude larger than the convective turbulent velocity, as a matter of convenience, it is possible to separate aerodynamic pressure fluctuations from acoustic ones by wavenumber vectors.

It is observed before that Acoustic wavenumber in general of same order of magnitude that of to the ?exural wavenumber (kf) of Plate surface (Gaudard et al.). The side window buffeting is of the important application in this context.


Present work will discuss the spectral decomposition in one and two dimensions. A Matlab code is used to post process pressure spectra to achieve better understanding of Cross power spectral density (CPSD).

CPSD gives us variance of the in-phase and out-of-phase components of two time-series (quadrature spectrum). The phase information contained in CPSD estimates wave directional information.
Ansys Fluent is used to generate time history data for the case of side window buffeting under influence of mirror. A scale resolving simulations like SBES is carried out to accurately account for smallest scale structures. An automated script is used to selectively generate time domain signal at well-defined array points. Another Matlab script is used to obtain cross spectral density and hence have wave number frequency spectra (WFS).

Document Details

ReferenceCAASE_Jun_18_53
AuthorSom. P
LanguageEnglish
TypePresentation
Date 6th June 2018
OrganisationAnsys India
RegionAmericas

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