Vibrations of Plates With Clamped and Free Edges Excited by Highly Subsonic Turbulent Boundary Layer Flow

Author(s):  
Stephen A. Hambric ◽  
Yun Fan Hwang ◽  
William K. Bonness

Plate vibrations due to turbulent boundary layer (TBL) excitation can depend strongly on the plate boundary conditions, especially when the flow convects over the plate at speeds much slower than those of the bending waves in the plate. The vibration response of a TBL excited flat rectangular plate is analyzed with two sets of boundary conditions: (A) all four edges clamped, and (B) three edges clamped and one edge free, with the flow direction perpendicular to the free edge. A finite element model with discretization sufficient to resolve the convective wavenumbers in the flow excitation field is used for the study. Three TBL wall pressure excitation models are applied to the plates to represent the cross-spectra of the wall pressures: (1) a modified Corcos model, which includes all wavenumber components of excitation; (2) a low-wavenumber excitation model previously derived by one of the authors, which only models the wavenumber-white region of the modified Corcos model; and (3) an equivalent edge force model which only models the convective component in the modified Corcos model. The TBL wall pressure autospectrum is approximated using the model derived by Smolyakov and Tkachenko. The results obtained from applying models (2) and (3) to the clamped and free edge plates are compared to those generated using model (1). For the completely clamped boundary conditions, the low-wavenumber and Modified Corcos models yield nearly identical vibration spectra, indicating that surface interactions dominate the response of fully clamped plates excited by TBL pressures. For the free edge boundary condition, the vibrations predicted using the equivalent edge force and modified Corcos models match very well, showing that edge interactions between TBL pressures and structural modes dominate the vibrations of plates with free edges excited by TBL flow.

1997 ◽  
Vol 119 (2) ◽  
pp. 277-280 ◽  
Author(s):  
B. A. Singer

Models for the distribution of the wall-pressure under a turbulent boundary layer often estimate the coherence of the cross-spectral density in terms of a product of two coherence functions. One such function describes the coherence as a function of separation distance in the mean-flow direction, the other function describes the coherence in the cross-stream direction. Analysis of data from a large-eddy simulation of a turbulent boundary layer reveals that this approximation dramatically underpredicts the coherence for separation directions that are neither aligned with nor perpendicular to the mean-flow direction. These models fail even when the coherence functions in the directions parallel and perpendicular to the mean flow are known exactly. A new approach for combining the parallel and perpendicular coherence functions is presented. The new approach results in vastly improved approximations for the coherence.


AIAA Journal ◽  
1999 ◽  
Vol 37 ◽  
pp. 1088-1096
Author(s):  
O. H. Unalmis ◽  
D. S. Dolling

Author(s):  
Frank J. Aldrich

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.


Sign in / Sign up

Export Citation Format

Share Document