Analytical Modeling of the Vibro-Acoustic Response of a Double-Walled Cylindrical Shell With Microperforation Excited by Turbulent Boundary Layer Pressure Fluctuations

2017 ◽  
Vol 140 (2) ◽  
Author(s):  
Qunlin Zhang ◽  
Yijun Mao ◽  
Datong Qi
Keyword(s):  
Boundary Layer ◽  
Cylindrical Shell ◽  
Turbulent Boundary Layer ◽  
Transmission Loss ◽  
Pressure Fluctuations ◽  
Velocity Model ◽  
Wide Frequency Range ◽  
Sound Insulation ◽  
Acoustic Response ◽  
Interior Space

An analytical model is developed to investigate the vibro-acoustic response of a double-walled cylindrical shell with the inner wall perforated when excited by the external turbulent boundary layer (TBL) pressure fluctuations. The shell motion is governed by the Donnell’s thin shell theory, and the mean particle velocity model is employed to describe the boundary condition between the microperforated shell and fluid media. Numerical results indicate that the transmission loss (TL) for the configuration of microperforating the inner wall could be larger than that for the conventional solid double-walled cylindrical shell with and without the core of porous material over a wide frequency range. Comparison between TL results with excitations from the TBL and the acoustic diffuse field (ADF) shows that with the thought of microperforating the inner shell, to reduce the acoustical excitation will be of more importance than the flow excitation over the ring frequency for a quiet interior space. Parametric studies illustrate that the perforation ratio is the main factor affecting the sound insulation performance through the total reactance.

Modeling the Vibroacoustics Response of Structures With Attached Noise Control Materials

2011 ◽  
Author(s):  
Noureddine Atalla ◽  
Dilal Rhazi
Keyword(s):  
Boundary Layer ◽  
Numerical Methods ◽  
Turbulent Boundary Layer ◽  
Porous Materials ◽  
Insertion Loss ◽  
Power Transmission ◽  
Transmission Loss ◽  
Acoustic Response ◽  
Radiated Power ◽  
Double Wall

This paper discusses the modeling of the vibration and acoustic response of panels with attached sound packages, using both analytical and numerical methods. Special attention is devoted to the modeling of various types of porous materials (rigid, limp, porous elastic,…) in various mounting conditions (single wall and double wall) together with the calculation of various vibroacoustic indicators (vibration response, radiated power, transmission loss, added damping, air-borne insertion loss, Structure-borne insertion loss…) under various excitations (acoustical, mechanical and Turbulent Boundary Layer). In particular, examples illustrating the practicality and usefulness of these methods are presented.

Predicting Turbulent Boundary Layer Wall Pressure Fluctuations in Adverse Pressure Gradients

2005 ◽  
Author(s):  
Frank J. Aldrich
Keyword(s):  
Boundary Layer ◽  
Turbulent Boundary Layer ◽  
Pressure Gradient ◽  
Pressure Fluctuations ◽  
Adverse Pressure Gradient ◽  
Wall Pressure ◽  
Prediction Tool ◽  
Pressure Gradients ◽  
Wall Pressure Fluctuations ◽  
The Difference

A physics-based approach is employed and a new prediction tool is developed to predict the wavevector-frequency spectrum of the turbulent boundary layer wall pressure fluctuations for subsonic airfoils under the influence of adverse pressure gradients. The prediction tool uses an explicit relationship developed by D. M. Chase, which is based on a fit to zero pressure gradient data. The tool takes into account the boundary layer edge velocity distribution and geometry of the airfoil, including the blade chord and thickness. Comparison to experimental adverse pressure gradient data shows a need for an update to the modeling constants of the Chase model. To optimize the correlation between the predicted turbulent boundary layer wall pressure spectrum and the experimental data, an optimization code (iSIGHT) is employed. This optimization module is used to minimize the absolute value of the difference (in dB) between the predicted values and those measured across the analysis frequency range. An optimized set of modeling constants is derived that provides reasonable agreement with the measurements.

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